Resting-state Connectivity Dynamics in the Human Brain ...

University of New MexicoUNM Digital Repository

Physics & Astronomy ETDs Electronic Theses and Dissertations

8-25-2016

Resting-state Connectivity Dynamics in theHuman Brain using High-speed f MRIKishore Vakamudi

Follow this and additional works at: https://digitalrepository.unm.edu/phyc_etds

Part of the Astrophysics and Astronomy Commons, and the Physics Commons

This Thesis is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted forinclusion in Physics & Astronomy ETDs by an authorized administrator of UNM Digital Repository. For more information, please [email protected].

Recommended CitationVakamudi, Kishore. "Resting-state Connectivity Dynamics in the Human Brain using High-speed fMRI." (2016).https://digitalrepository.unm.edu/phyc_etds/73

https://digitalrepository.unm.edu?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/phyc_etds?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/etds?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/phyc_etds?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

http://network.bepress.com/hgg/discipline/123?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

http://network.bepress.com/hgg/discipline/193?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/phyc_etds/73?utm_source=digitalrepository.unm.edu%2Fphyc_etds%2F73&utm_medium=PDF&utm_campaign=PDFCoverPages

mailto:[email protected]

i

KISHORE VAKAMUDI Candidate PHYSICS AND ASTRONOMY Department This thesis is approved, and it is acceptable in quality and form for publication: Approved by the Thesis Committee: STEFAN POSSE, PhD, Chair ARVIND CAPRIHAN, PhD, Committee member TUDOR I. OPREA, PhD, Committee member

ii

RESTING-STATE CONNECTIVITY DYNAMICS IN THE HUMAN BRAIN

USING HIGH-SPEED FMRI

by

Kishore Vakamudi

THESIS

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Master of Science

Physics

The University of New Mexico

Albuquerque, New Mexico

July 2016

iii

freedom, reason, and truth

iv

ACKNOWLEDGEMENTS

Stefan Posse, PhD I would like to express my profound appreciation and gratitude to

my supervisor and committee chair Dr. Stefan Posse, who has the

attitude and substance of a genius. Without his persistent help,

excellent mentorship, and great support, this work would not have

been possible.

Arvind Caprihan, PhD My sincere gratitude to Dr. Caprihan for his acceptance to be on my

thesis committee and for his guidance.

Tudor I. Oprea, PhD My sincere gratitude to Dr. Oprea for his acceptance to be on my

thesis committee.

Elena S. Ackley, MS I am indebted to Elena S. Ackley for her motherly guidance and

invaluable support when I started this project and for her enormous

patience with me.

Dinesh Loomba, PhD I would like to thank Dr. Loomba for his great guidance and

encouragement as my academic advisor.

Vince D. Calhoun, PhD I would like to express my sincere gratitude to Dr. Calhoun for his

guidance and encouragement.

Sudhakar Prasad, PhD My sincere gratitude to Dr. Prasad for his support.

Nitant V. Kenkre, PhD My sincere gratitude to Dr. Kenkre for his stimulating philosophical

conversations and insights on physics.

Mickey Odom I would like express my sincere appreciation to Mickey for his support and friendship during my TA sessions.

Departments of Physics

and Astronomy and

Neurology

Sincere thanks to the departments of Physics and Astronomy, and Neurology, University of New Mexico, Albuquerque, for the financial support.

v

Colleagues A special thanks to Cameron Trapp for his help in reviewing the

thesis. Alec Landow, Kevin Fotso, Eswar Damaraju and other

colleagues from MRN, Albuquerque, and University of

Copenhagen, Denmark for their camaraderie.

At UNM Sincere thanks to Ruslan Hummatov, Dipendra Adhikari, Qufei Gu,

Esad Shobjee, Prabhakar Palni along with all my professors for their

invaluable help and guidance.

Teachers I grateful to all my teachers past and present.

Friends I am deeply indebted to all my friends for being my strength.

For everything they are.

and to my parents

Suseela and Late Sobhanachalam Vakamudi

vi

RESTING-STATE CONNECTIVITY DYNAMICS IN THE HUMAN BRAIN

USING HIGH-SPEED FMRI

by

Kishore Vakamudi

University of New Mexico, MS, 2016

ABSTRACT

Resting-state fMRI using seed-based connectivity analysis (SCA) typically involves regression of

the confounding signals resulting from movement and physiological noise sources. This not only

adds additional complexity to the analysis but may also introduce possible regression bias. We

recently introduced a computationally efficient real-time SCA approach without confound

regression, which employs sliding-window correlation analysis with running mean and standard

deviation (meta-statistics). The present study characterizes the confound tolerance of this

windowed seed-based connectivity analysis (wSCA), which combines efficient decorrelation of

confounding signal events with high-pass filter characteristics that reduce sensitivity to drifts. The

confound suppression and the strength of resting-state network (RSN) connectivity were

characterized for a range of confounding signal profiles as a function of sliding-window width and

scan duration, using simulation and in vivo data. The connectivity strength in six resting-state

networks (RSNs) and artifactual connectivity in white matter were compared between wSCA and

conventional regression-based SCA (cSCA). The wSCA approach demonstrated scalable

confound suppression that increased with decreasing sliding-window width and increasing scan

duration in both simulations and in vivo. The confound suppression for sliding-window widths ≤

15 s was comparable to that of cSCA. Twenty-eight RSNs that were previously reported in a group-

ICA study were detected in real-time at scan durations as short as 30 s and with sliding-window

widths as short as 4 s. The inter- and intra- network connectivity dynamics of the 28 resting-state

networks were studied in real-time and self-repeating connectivity patterns were identified.

vii

The wSCA is further investigated offline to study the strength and temporal fluctuations in

connectivity using 28 single-region seeds and 28 multi-region seed clusters to measure inter-

regional connectivity (IRC) in 140 functional brain regions and inter-network connectivity (INC)

among the hubs of 28 RSNs. Multi-region seed IRC maps displayed smaller temporal fluctuations

and stronger resting-state connectivity compared with single-region seed IRC maps. Dual

thresholding of the meta-statistics maps demonstrated higher spatio-temporal IRC stability in

auditory, sensorimotor, and visual cortices compared to other brain regions. The group averaged

INC matrices for single-region seeds were consistent with the functional network connectivity

matrices (FNCMs) presented in the aforementioned group-ICA study. Furthermore, we extended

the mapping of functional connectivity to the whole-brain connectivity fingerprints.

In combination with novel brain parcellation methods and advanced machine learning algorithms,

wSCA can aid in studying the spatial and temporal connectivity dynamics of the resting-state

connectivity. The robust confound tolerance, high temporal resolution, and compatibility with real-

time high-speed fMRI, make this approach suitable for monitoring data quality, neurofeedback,

and clinical research studies involving disease related changes in functional connectomics.

viii

Glossary

BOLD Blood-Oxygenation Level Dependence

CSF Cerebrospinal Fluid

EEG Electroencephalography

EPI Echo-Planar Imaging

fMRI Functional Magnetic Resonance Imaging

FNCM Functional Network Connectivity Matrix

fNIRS Functional Near-Infrared Spectroscopy

GRAPPA Generalized Autocalibrating Partially Parallel Acquisition

HRF Hemodynamic Response Function

ICA Independent Component Analysis

INCM Inter-Network Connectivity Matrix

IRCM Inter-Regional Connectivity Matrix

MEG Magnetoencephalography

MEVI Multi-slab Echo-Volumar Imaging

MNI Montreal Neurological Institute

PCA Principle Component Analysis

PCC Posterior Cingulate Cortex

PET Positron Emission Tomography

ROI Region Of Interest

rsfMRI Resting-State Functional Magnetic Resonance Imaging

RSN Resting-State Network

SCA Seed-based Connectivity Analysis

cSCA Conventional Seed-based Connectivity Analysis

wSCA Windowed Seed-based Connectivity Analysis

SNR Signal-to-Noise Ratio

TurboFIRE Turbo Functional Imaging in REal-time

ix

CONTENTS

Acknowledgements ................................................................................................................... iii

Abstract ....................................................................................................................................... v

Glossary ................................................................................................................................... vii

Chapter 1 Introduction

1.1. Background .................................................................................................................... 1

1.2. Thesis Organization ....................................................................................................... 3

References ............................................................................................................................. 4

Chapter 2 Nuclear Magnetic Resonance

2.1. Introduction .................................................................................................................... 5

2.2. Angular Momentum and Energy ................................................................................... 5

2.3. From Quantum To Classical .......................................................................................... 7

2.4. Classical Equations of NMR ........................................................................................ 8

2.4.1. Precession ............................................................................................................. 8

2.4.2. Excitation .............................................................................................................. 8

2.4.3. Bloch Equation...................................................................................................... 9

2.4.4. Relaxation ........................................................................................................... 10

2.5. Magnetic Resonance Imaging (MRI)........................................................................... 13

2.5.1. Zeugmatography and Slice Selection.................................................................. 14

2.5.2. Two-Dimensional Spatial Encoding and K-Space ............................................. 15

2.6. MR Contrast Mechanisms............................................................................................ 16

2.6.1. T1 contrast ........................................................................................................... 17

2.6.2. T2 contrast ........................................................................................................... 17

2.6.3. T2* contrast .......................................................................................................... 18

x

2.7. Conventional MRI ....................................................................................................... 18

2.8. Advanced fMRI ........................................................................................................... 20

2.8.1. Echo-Planar Imaging (EPI) ................................................................................. 20

2.8.2. Echo-Volumar Imaging (EVI) ........................................................................... 21

2.8.3. Multi-slab Echo-Volumar Imaging (MEVI) Reconstruction ............................. 22

2.8.4. MEVI - Advantages and Limitations .................................................................. 24

References ........................................................................................................................... 25

Chapter 3 Functional Magnetic Resonance Imaging

3.1. Introduction .................................................................................................................. 27

3.2. Blood-Oxygenation Level Dependent (BOLD) signal ................................................ 28

3.3. Hemodynamic Response Function (HRF) ................................................................... 28

3.4. Resting-state fMRI ....................................................................................................... 29

3.5. Confounding Signals in Resting-state fMRI ................................................................ 30

3.6. Preprocessing in fMRI ................................................................................................. 31

3.7. Resting-state Networks (RSNs) ................................................................................... 32

References ........................................................................................................................... 34

Chapter 4 Windowed Seed-based Connectivity Analysis

4.1. Introduction .................................................................................................................. 36

4.2. Subjects and Data Acquisition ..................................................................................... 37

4.3. Seed-based Connectivity Analysis (SCA) ................................................................... 39

4.3.1. Seed Selection ..................................................................................................... 39

4.3.2. Conventional Seed-based Connectivity Analysis (cSCA) .................................. 40

4.3.3. Windowed Seed-based Connectivity Analysis (wSCA) ..................................... 41

4.4. Simulation of Confound Suppression using Windowed SCA ..................................... 41

xi

4.5. Performance Comparison of Windowed and Conventional SCA................................ 46

4.5.1. Computational Efficiency ................................................................................. 47

4.5.2. Intra- and Inter-RSN Connectivity.................................................................... 50

4.5.3. Artifactual White Matter Connectivity ............................................................. 51

4.6. Whole-brain Connectivity Profiles .............................................................................. 52

4.7. Physiological Noise Suppression ................................................................................. 54

4.8. Mapping of Resting-state Networks ............................................................................ 55

4.9. Windowed Seed-based Connectivity Analysis ............................................................ 57

4.9.1. Confound Suppression ...................................................................................... 57

4.9.2. Frequency Selectivity of Windowed SCA ........................................................ 58

4.9.3. Conventional and Windowed SCA ................................................................... 59

4.9.4. Real-time Implementation of Windowed SCA ................................................. 59

4.9.5. Limitations ........................................................................................................ 60

References ........................................................................................................................... 61

Chapter 5 Functional Connectomics

5.1. Introduction .................................................................................................................. 67

5.2. Statistical Hierarchy ..................................................................................................... 67

5.3. Seed Selection .............................................................................................................. 68

5.4. Resting-state Networks with Multi-region Seeds ........................................................ 69

5.5. Inter-Regional Connectivity......................................................................................... 70

5.5.1. Single-subject Connectomics .............................................................................. 71

5.5.2. Group Connectomics .......................................................................................... 73

5.5.3. Spatio-temporal Stability of Inter-Regional Connectivity .................................. 78

5.6. Inter-Network Connectivity ......................................................................................... 81

5.7. Atlas-based Functional Connectomics ......................................................................... 83

References ........................................................................................................................... 85

xii

Chapter 6 High Dimensional Connectomics

6.1. Introduction .................................................................................................................. 87

6.2. Materials and Methods ................................................................................................. 87

6.3. Whole-brain Connectivity Fingerprints ....................................................................... 91

References ........................................................................................................................... 92

Chapter 7 Real-time Neurofeedback

7.1. Introduction .................................................................................................................. 93

7.2. Real-time Windowed SCA .......................................................................................... 95

7.3. Connectivity Dynamics of Resting-state Networks ..................................................... 95

7.4. Pattern Classification and Brain States ........................................................................ 96

7.5. Real-time Neurofeedback ............................................................................................ 96

References ........................................................................................................................... 98

Chapter 8 Conclusions and Future Directions

8.1. Conclusions .................................................................................................................. 99

8.2. Future Directions ......................................................................................................... 99

References ......................................................................................................................... 101

1

Chapter 1 Introduction

1.1. Background

The human brain is one of the most highly complex systems known in Nature. The discovery of

nuclear magnetic resonance (NMR) has enabled much better understanding of the brain through

magnetic resonance imaging (MRI) in conjunction with non-MR based brain imaging techniques,

such as positron emission tomography (PET), electroencephalography (EEG), and

magnetoencephalography (MEG). Brain imaging techniques have evolved significantly during the

past few decades, making MRI in particular, a powerful tool to study the structure (structural MRI)

and function of the brain (functional MRI).

The principal advantage of MRI compared to other neuroimaging techniques is the high spatial

resolution for imaging various tissue types. MRI is non-invasive and does not involve any ionizing

radiation as in X-ray or Tomography imaging. It also offers flexibility to image any plane of the

object without the physical rotation, which is extremely helpful for imaging the structure and

function of the brain [1-3]. One of the fundamental questions in neuroscience is to establish the

correspondence between the structural and functional properties of the brain. Functional MRI

(fMRI) has become a principal tool in establishing such a correspondence, based on a biophysical

mechanism known as blood-oxygenation level dependence (BOLD) [1-3]. With constantly

improving spatial and temporal resolutions, fMRI is at the forefront of non-invasive neuroimaging

techniques to map underlying structure with associated function to a higher degree of accuracy.

The methods developed to analyze the resting-state (subject laying at rest with minimal extraneous

thoughts) and task-based (while performing a specific task e.g., finger tapping, response to

auditory, visual stimuli) fMRI data can be broadly classified into model-dependent and model-free

methods [4]. These methods aim to obtain statistically meaningful results in single subjects and

groups of subjects through various analysis approaches. Seed-based connectivity analysis (SCA)

is an example of a model-dependent analysis method in which the time-dependent signal from a

particular region of interest (ROI/seed) is correlated against signals from all other regions in the

brain, resulting in a functional connectivity map [5,6]. These maps facilitate a straightforward

interpretation of the extent to which the seed region is functionally connected to the rest of the

2

brain. Model-free methods, such as principal component analysis (PCA) [7], independent

component analysis (ICA) [8,9], hierarchical [10], and normalized clustering [11] offer a

generalized data-driven approach to investigate the functional connectivity, but pose a challenge

in the interpretation of the results. Although SCA is widely used approach for fMRI analysis, it is

highly sensitive to confounds such as head motion, physiological noise (respiration and cardiac

related pulsations), and requires regression. On the other hand, regression may not only bias the

connectivity analysis, but also impairs the real-time implementation of SCA. Recent advances in

high-speed fMRI acquisition techniques such as multi-band EPI [12], echo-shifted imaging

[13], MR-encephalography [14], as well as single-shot and multi-slab echo-volumar imaging

(MEVI) [15,16] are shown to enhance sensitivity by unaliasing the physiological noise

components for detection of fluctuations in the fMRI data.

The objective of this thesis is to develop a generalized framework for mapping the functional

connectivity by combining the advanced high-speed MEVI technique with a novel seed-based

connectivity analysis, which employs a sliding-window correlation analysis with running mean

and standard deviation (meta-statistics). This novel windowed SCA (wSCA) approach [17] is

computationally efficient for real-time (online) fMRI analysis and does not require the regression

of confounding signal sources. The real-time implementation of wSCA enables detection of

dynamic changes in functional connectivity at very short time scales. The offline extension of this

methodology enables for characterizing confound tolerance in comparison with conventional

regression-based approaches and mapping functional connectivity in the entire brain through high

dimensional connectomics. The combination of wSCA with multi-slab echo-volumar high-speed

fMRI data (temporal resolution of 136 ms) is shown to provide high sensitivity for mapping

resting-state connectivity dynamics among 28 networks previously detected in a spatial group-ICA

study [18]. The robust confound tolerance achieved using wSCA and its compatibility with real-

time high-speed fMRI, make this approach suitable for monitoring data quality, neurofeedback,

and clinical research studies that involve disease related changes in functional connectomics.

3

1.2. Thesis Organization

Chapter 2 outlines the principles of nuclear magnetic resonance in obtaining MR signal and basics

of magnetic resonance imaging.

Chapter 3 briefly explains functional MRI, generation of blood-oxygenation level dependent

(BOLD) signal, and the hemodynamic response function (HRF). A brief introduction to resting-

state networks is presented.

Chapter 4 describes the methodology of the windowed seed-based connectivity analysis (wSCA).

This chapter characterizes the confound suppression of wSCA using simulations and in vivo data

and its implementation in real-time.

Chapter 5 presents the application of the wSCA approach offline to map the resting-state

connectivity in single subjects and group using 28 single- and multi- region seeds and a dual

threshold mechanism to identify the regions with stable and unstable functional connectivity.

Chapter 6 extends the mapping of functional connectivity from 28 seed regions to the high

dimensional whole-brain connectome fingerprints, generated using an automated atlas-based

implementation of the wSCA.

Chapter 7 discusses the real-time resting-state connectivity dynamics with a proof-of-concept

neurofeedback experiment and the identification of the recursive connectivity patterns.

Chapter 8 speculates on the future directions that this work might take.

4

References

1. Haacke, E. M., Brown, R. W., Thompson, M. R., and Venkatesan, R. 1999. Magnetic resonance imaging: Physical principles and sequence design. John Wiley & Sons, NY.

2. Clare, S. 1997. Functional MRI: Methods and applications. University of Oxford. 3. Hornak, J. P. 1996-2014. The basics of MRI. Center for imaging science, Rochester

institute of technology. NY. 4. Van den Heuvel, M. P., Hulshoff Pol, H. E., 2010. Exploring the brain network: A review

on resting-state fMRI functional connectivity. European Neuropsychopharmacology 20, 519–534. doi:10.1016/j.euroneuro.2010.03.008.

5. Biswal, B.B., Van Kylen, J., Hyde, J.S., 1997. Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR Biomed. 10 (4–5), 165–170.

6. Cordes, D., Haughton, V.M., Arfanakis, K., Wendt, G.J., Turski, P.A., Moritz, C.H., Quigley, M.A., Meyerand, M.E., 2000. Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am. J. Neuroradiol. 21 (9), 1636–1644.

7. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S., 1993. Functional connectivity: the principal component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13 (1), 5–14.

8. Comon, P., 1992. ICA, a new concept? Signal Processing 36, 287-314. 9. Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group

inferences from functional MRI data using indepen-dent component analysis. Hum. Brain Mapp. 14 (3), 140–151.

10. Cordes, D., Haughton, V., Carew, J.D., Arfanakis, K., Maravilla, K., 2002. Hierarchical clustering to measure connectivity in fMRI resting-state data. MRI, 20 (4), 305–317.

11. Van den Heuvel, M.P., Mandl, R.C., Hulshoff Pol, H.E., 2008a. Normalized group clustering of resting-state fMRI data. PLoS ONE 3 (4), e2001.

12. Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., et al, 2010. Multi-plexed echo-planar imaging for sub-second whole-brain fMRI and fast diffusion imaging. PLoS ONE 5(12): e 15710. doi: 10.1371/journal.pone.0015710.

13. Gibson, A., Peters, A. M., Bowtell, R., 2006. Echo-shifted multislice EPI for high-speed fMRI. Magnetic Resonance Imaging 24 (2006) 433-442. doi: 10.1016/j.mri.2005.12.030.

14. Zahneisen, B., Hugger, T., Lee, K, J., LeVan, P., Reisert, M., et al, 2012. Single-shot concentric shells trajectories for ultra-fast fMRI. MRM. doi: 10.1002/mrm.23256.

15. Mansfield, P., Harvey, P. R., Stehling, M. K., 1994. Echo-volumar imaging. Mag. Res materials in physics, Biology and Medicine 1994 Volume 2, Issue 3, 291-294.

16. Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., Murray-Krezan, C., et al, 2012. Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage 61. doi: 10.1016/j.neuroimage.2012.02.059.

17. Vakamudi, K., Ackley, E. S., Trapp, C. W., Damaraju, E., Calhoun, V. D., Posse, S, 2016. Confound-Tolerant Seed-Based Sliding-Window Connectivity Analysis for Real-Time High-Speed Resting-State fMRI, Neuroimage, submitted March 2016.

18. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner. Calhoun, V. D., et al, 2011. A baseline for the multivariate comparison of resting-state networks. Front. Syst. Neurosci. 5:2.

5

Chapter 2 Nuclear Magnetic Resonance

2.1. Introduction

The discovery of nuclear magnetic resonance (NMR) had its foundation in the quantum

mechanical description of the atomic nuclei. The spin angular momentum of protons and their

interactions with a magnetic field were extensively studied by Stern, Gerlach, Rabi, and others

[1,2]. Felix Bloch [3] and Edward Purcell [4] independently measured the effect of the spin

precession in a magnetic field, which lead to the discovery of NMR in 1946.

Atomic nuclei with an odd number of protons/neutrons contribute to nuclear magnetic resonance.

For a nucleus to possess the NMR property, it must have both a non-zero magnetic moment and

angular momentum. In the case of a proton of charge ‘+e’ and mass ‘m’, the magnetic moment and

spin angular momentum arise from rotational motion due to thermal energy around its axis. Many

nuclei of biological interest, such as 1H, 3He, 13C, 15N, 17O, and 19F, have odd number of protons

and therefore possess NMR properties. Since 1H is the most abundant nucleus in biological systems

(e.g. in H2O and many organic compounds), MRI is primarily implemented for protons [5-11].

2.2. Angular Momentum and Energy

The basis for MRI can be understood from the interaction of a proton spin with the magnetic field

in the quantum mechanical framework. The magnitude of the spin angular momentum is given by

│S │ = ħ√s(s + 1) (2.1)

In a magnetic field that is applied along the Z-direction, the angular momenta have the following

values

Sz = ħms (2.2)

where the (2S+1) values of ms are given by ms = s, (s-1), (s-2), … -s.

A proton of spin ½, has two possible spin states

Sz = ±1/2 ħ (2.3)

6

The energy of a spin system placed in a magnetic field �� is given by

E = -µ·�� (2.4)

where µ is the magnetic moment. It is proportional to the spin angular momentum,

µ=γ𝑆 (2.5)

where γ is gyromagnetic ratio and is constant for a given nucleus. By combining the Eqs. 2.4 and

2.5, we can derive a Hamiltonian for the system with magnetic field applied in the Z-direction,

H= -γħ (�� ·𝑆 )

Hz= -γħBzSz (2.6)

Solving the Schrödinger equation of this Hamiltonian for the energy eigenstate, we get

H│�� s˃ = E│�� s˃

E = -γħBzms (2.7)

The change in energy of a proton as a result of transition between two spin states (±1/2),

ΔE = E−21 − E+2

1 = 𝛾ħBz (2.8)

This transition is accompanied by emission or absorption of a photon with frequency ν0

ΔE = γħBz = hν0 (2.9)

where ν0 = γ

2πBZ. For a constant field Bz

= B0z,

ω0 = 𝛾B0 (2.10)

The Eq. 2.10 is a fundamental relation in NMR, called the Larmor equation. Since the

gyromagnetic ratio ‘γ’ is constant for a given nucleus, the Larmor frequency is determined solely

by the applied magnetic field.

7

2.3. From Quantum To Classical

Although quantum mechanical theory can quantify the spin properties of an individual nucleus,

real systems contain large numbers of nuclei, and therefore, the theory must be extended to

encompass an ensemble of spins. The thermal energy associated with normal human body

temperature prevents proton spins from completely aligning in the direction of the external

magnetic field, generating more parallel spin states (low-energy) relative to anti-parallel spin states

(high-energy). The net magnetization resulting from this distribution of spin states depends on the

relative energy difference ΔE, temperature (T), and a proportionality constant (κB), called the

Boltzmann’s constant. If the probability for parallel spin states is P+1/2, and the probability for anti-

parallel spin states is P-1/2, the relative distribution of these probabilities is given by

P+2

1

P−2

1= e

∆E

κBT (2.11)

where 𝜅𝐵 = 1.3806x10-23 J/K and T is the absolute temperature. Since 𝜅𝐵 is very small ( ∆𝐸

𝜅𝐵𝑇≪ 1),

Eq. 2.11 can be approximated as

P+2

1

P−2

1≈ 1 +

∆E

κBT (2.12)

Using 𝑃+21 + 𝑃−2

1 = 1, we calculate the excess number of spins (𝑃+21 − 𝑃−2

1) that are parallel to

the magnetic field as

P+21 − P−2

1 ≈ ∆E

2κBT (2.13)

A small sample of any biological tissue contains N ∆𝐸

2𝜅𝐵𝑇 excess proton spins, where N is Avogadro

number (6.022x1023). In a magnetic field of 3 Tesla, for every million proton spins there are

approximately eighteen excess parallel proton spins. For example, in a volume element (voxel) of

size 4x4x4 mm3 (volume of 0.064 ml), the total number of excess spins will be ~8x1016. The

magnetic moment generated from such a large number of spins leads to macroscopic NMR effects

for imaging the bulk properties of the tissue, justifying the classical Boltzmann distribution

employed in studying MRI.

8

2.4. Classical Equations for NMR

2.4.1. Precession

The equation of motion of a magnetic moment vector (M ), placed in a magnetic field (B ) is

dM

dt= γM x B (2.14)

For a static magnetic field, B = B0k, the solution of this equation can be written as

M (t) = Mx0(x cosωt − y sinωt) + My0(x sinωt + y cosωt) + Mz0t (2.15)

The Eq. 2.15 describes the precession of the magnetization vector about Z-axis as shown in Fig.

2.1. The precessional frequency is equal to the Larmor frequency, in accordance with Eq. 2.10.

2.4.2. Excitation

In the NMR experiment, MR signal is detected by electromagnetic coils (radiofrequency coils)

that transmit and receive electromagnetic fields at resonant frequencies of the nuclei within a static

magnetic field. When a sample is placed in the magnetic field, the magnetic moments of the nuclei

align with the static field. The radiofrequency coils then transmit electromagnetic waves

(excitation pulses) at the Larmor frequency to rotate the equilibrium spin magnetization away from

Fig. 2.1. Precession of a magnetization vector M , in a magnetic field B 0 directed along Z-

axis with an angular frequency ω.

9

the Z-axis. This process is called excitation. After the duration of the excitation pulse, the spins

undergo relaxation. The magnetization component due to the spins along the Z-axis is governed

by exponential growth, and along the transverse plane (XY), it is governed by exponential decay

which are detected by the radiofrequency receiver coils as an MR signal. The detected signal is

called the ‘Free-Induction Decay’ (FID). The energy absorbed by the nuclei during excitation is

released through longitudinal relaxation (see 2.4.4).

2.4.3. Bloch Equation

When a time-varying magnetic field B1 (t) = B1(x cosωt − y sinωt) is applied along with the

static magnetic field B = B0z, the precession of the spins follow two different types of relaxation

mechanisms to reach the original spin state. This combined effect can be studied using a

generalized equation of motion called the Bloch equation:

dM

dt= γM x B +

1

T1(M0 − Mz

) − 1

T2(Mx + My

) (2.16)

The Bloch equation describes the behavior of a net magnetization vector of a spin system in the

presence of a time-varying magnetic field, which precesses around the stationary magnetic field

component at the Larmor frequency with longitudinal changes governed by T1 and transverse plane

changes governed by T2. The solutions of Eq. 2.16 govern the dynamics of the magnetization

vector during steady-state, excitation, and relaxation.

Mathematically, solving Bloch equation is more convenient by rotating the XY plane by an angular

frequency Ω = –γB0, as shown in Fig. 2.2. The new frame of reference is called the rotating frame

of reference, in which the magnetization vector appears to smoothly tip down, while Mxy appears

stationary. The equation of motion with a time-varying magnetic field in the rotating frame of

reference (X’, Y’, Z’) is given by dM

dt= γM x B eff (2.17)

where B eff = B + Ω

γ.

10

By converting Eq. 2.16 to the rotating frame of reference, we obtain the solutions for the Bloch

equation through the following relaxation mechanisms.

2.4.4. Relaxation

Longitudinal relaxation

After the duration of the excitation pulse, the spin system gradually loses the energy that was

absorbed during excitation and retains the original energy state along the direction of the external

magnetic field (Z). This process is called longitudinal relaxation or spin-lattice relaxation [5-11].

At equilibrium, the net magnetization vector M is along the direction of the applied magnetic field

�� . This is called the equilibrium magnetization M0 , which contains only the Z component, known

as longitudinal magnetization Mz . After a 90o excitation RF pulse, which flips the longitudinal

magnetization into the transverse plane, the longitudinal magnetization reorients along the Z-axis

with time constant T1 (Fig.2.3). The dynamics of the longitudinal magnetization are described by

the Bloch equations (Eq. 2.16). The longitudinal magnetization is governed by,

dMz

dt= −

(Mz−M0)

T1 (2.18)

Fig. 2.2. Laboratory and rotatory frames of reference. The XY plane of a laboratory frame is

rotated by an angular frequency Ω = –γ𝐵0.

11

Solving for 𝑀𝑧,

Mz = M0(1 − e−T1t

) (2.19a)

T1 is the time to recover 63.2% of the equilibrium magnetization. The most commonly used

experiment to measure T1 is the ‘inversion recovery’, in which the net magnetization is inverted

along the -Z-axis using a 180o inversion RF pulse which will return to its equilibrium position

along the +Z-axis at a rate governed by T1. The magnetization is measured after a time delay

(inversion time) using a 90o pulse. A series of experiments with different inversion times allows

to measure T1 by fitting the Eq. 2.19 as a function of inversion time t,

Mz = M0(1 − 2e−T1t

) (2.19b)

Fig. 2.3. Longitudinal relaxation. a) Spins oriented along the direction of the magnetic field (Z),

b) spins flipped on to the XY-plane by a 90o pulse, c) spins reorienting along the Z-axis, and d)

spins along the Z-axis after relaxation. Graph shows the magnetization along the Z-axis over time,

with T1 being the longitudinal relaxation time.

12

Transverse relaxation

The magnetization in the transverse plane is subject to microscopic fluctuations in the local

magnetic fields resulting from the spin-spin interactions. These randomly fluctuating precessional

frequencies lead to randomly changing spin phases that decrease the net magnetization. In

transverse plane this can be described as ‘fanning out’ of the spin orientation in time (Fig. 2.4),

which corresponds to dephasing of the magnetization. The decrease of the transverse

magnetization is given by the Bloch equations (Eq. 2.16) [5-11]:

dMx

dt= My. γB −

Mx

T2 and dMy

dt= −Mx. γB −

My

T2 (2.20)

Combining the interactions of X and Y components in complex form, the net transverse

magnetization is denoted by Mxy = Mx + iMy. The solution of this combined magnetization in

both the directions is,

Mxy = Mxy0e−T2

t

. e−iωt (2.21)

where Mxy0 is the initial magnitude of the transverse magnetization after excitation.

Fig. 2.4. Transverse relaxation. a) Spins oriented along the XY-plane, b) spins start spreading

along the XY-plane causing incoherence due to phase differences, c) transverse spin relaxation

in the XY-plane. Graph shows the transverse magnetization in the XY plane, with T2 being the

transverse relaxation time.

13

2.5. Magnetic Resonance Imaging (MRI)

Magnetic field gradients are used to spatially encode the spin magnetization after excitation. These

gradient fields are vector fields represented by Gx, Gy, and Gz whose magnitude vary linearly along

the X, Y, and Z axes and oriented along the main magnetic field B0 . These gradient fields, when

combined with the static magnetic field B0 (directed along the scanner axis), generate an effective

field B that varies at every spatial location (X, Y, Z). The magnitude of the effective field thus can

be written as a linear combination of the main magnetic field and the gradient fields, at a given

location (X, Y, Z) and time t.

�� (t) = 𝐵0 + Gx(t) �� + Gy(t) ��+ Gz(t) �� (2.22)

For the purpose of imaging, these magnetic field gradients are switched dynamically after signal

excitation, to encode the spin location in the spin frequency and phase, thus modulating the

received signal. Using ω = 𝛾B in Eq. 2.21, the transverse magnetization at a given location and

time is given by,

Mxy(x, y, z, t) = Mxy0(x, y, z)e−T2

t

. e−iγBot e−iγ∫ (Gx(t)x+Gy(t)y+Gz(t)z)dtt0 (2.23)

The MR signal measured by the antenna is equal to the sum of the magnetizations over each voxel

(unit three-dimensional volume), at time 𝜏 and is given by,

S(τ) = ∭ Mxy(x, y, z)dx dy dz

x,y,z (2.24)

Substituting Mxy from Eq. 2.23 into 2.24, we get,

S(τ) = ∭ Mxy0(x, y, z)e−T2

t

. e−iγBot e−iγ∫ (Gx(t)x+Gy(t)y+Gz(t)z)dtt0 dx dy dz

x,y,z (2.25)

Eq. 2.25 is called the MR signal equation [5-11]. It states that the MR signal at any given position

and time is governed by T2 relaxation time, main magnetic field 𝐵𝑜, phase accumulated by the

effective field, and the sum of the net magnetizations across the voxels. This generalized equation

represents the relationship between the acquired signal S(τ) to the properties of the

object Mxy0(x, y, z) that is being imaged.

14

2.5.1. Zeugmatography and Slice Selection

The principle of encoding space using magnetic field gradients is called Zeugmatography [12],

introduced by Nobel Laureate Lauterbur in 1972 [13]. The term e−T2t

in Eq. 2.25 only influences

the magnitude of the signal through T2 relaxation, not the spatial location of the signal (unless T2

is short relative to the duration of spatial encoding). In addition, the demodulation of the Larmor

frequency (similar to demodulation of the carrier frequency in an FM radio) omits the term e−iγBot.

Therefore, we can eliminate these two terms from Eq. 2.25 for spatial encoding.

S(τ) = ∭ Mxy0(x, y, z)e−iγ∫ (Gx(t)x+Gy(t)y+Gz(t)z)dt

t0 dx dy dz

x,y,z (2.26)

Most of the functional and structural scans are acquired as a set of two-dimensional images and

reconstructed to form three-dimensional images. Although Eq. 2.26 is a reduced form of Eq. 2.25,

it still represents three-dimensional imaging, which is a slower process and not ideal for studying

the functional properties of the brain. This equation can be reduced to two-dimensions by first

selecting a slice of certain thickness along the Z-dimension and then acquiring the MR signal from

a single two-dimensional slice at a time.

The rectangular slices can be selected through the application of sinc modulated pulses along the

Z-axis. The frequency of precession becomes linear in the presence of a slice select gradient, given

Fig. 2.5. The frequency (ω) as in the laboratory frame as a function of position along the slice

selection axis when a gradient is applied along the Z-axis.

15

by, ω(z) = ω0 + γGzZ, where ω0 is the Larmor frequency at Z=0. The relationship is depicted in

Fig. 2.5. The slice selection depends on three factors: the central frequency of the excitation pulse

(ω), the band width (∆ω) of the radio frequency excitation field, and the strength of the gradient

field (Gz) whose thickness (TH) is given by,

𝑇𝐻 =∆𝜔𝑟𝑓

𝛾Gz (2.27)

The magnetization within the slice depends only on X and Y, and Eq. 2.26 is reduced to

S(τ) = ∬ M(x, y)e−iγ∫ (Gx(t)x+Gy(t)y)dtt0 dx dy

x,y (2.28)

2.5.2. Two-Dimensional Spatial Encoding and K-Space [14]

The two gradients Gx and Gy in Eq. 2.28 correspond to the frequency encoding gradient and phase

encoding gradient respectively [6-9]. The frequency encoding gradient changes the Larmor

frequency linearly along the gradient direction, thus enabling the localization of the MR signal

along that direction through Fourier transformation. This results in a projection image. The phase

encoding gradient is applied before the acquisition due to which, spins accumulate linearly

changing phase offsets along the gradient direction. Spatial information (i.e. spin phase encoding)

is acquired stepwise in a series of individual experiments with different phase encoding gradient

moments. The equations for these gradients in k-space formalism are given by equations 2.29a

(frequency encoding) and 2.29b (phase encoding).

kx(t) = γ

2π∫ Gx(τ)dτ

t

0 (2.29a)

ky(t) = γ

2π∫ Gy(τ)dτ

t

0 (2.29b)

These equations are also known as k-space trajectories. Using these equations, the magnetization

within the slice can be expressed in k-space notation. The relationship between the image space

and the frequency space is given by a mathematical formalism, known as Fourier transformations

[15]. This notation scheme using spatial frequencies offers computational and conceptual

16

advantages in describing the image formation from the MR signal. Using the k-space trajectories

(Eqs. 2.29) in Eq. 2.28, the MR signal equation in k-space is given by

S(τ) = ∬ M(x, y)e−i2πkx(t)xe−i2πky(t)ydx dy

x,y (2.30)

This equation describes a linear relationship between the image space and k-space. The signal can

be represented by a two-dimensional coordinate system in units of spatial frequency with kx and

ky as the axes. This coordinate system is defined as k-space. The Eq. 2.30, when operated by

inverse Fourier transform, will be converted to image space, a process known as image

reconstruction. The sampling parameter in the image space is distance and k-space is distance-1,

respectively. This infers that a larger k-space will correspond to finer details (spatial resolution) in

image space, but correspondingly will take longer time to encode, reducing the temporal

resolution. Thus, there is always a trade-off between spatial and temporal resolutions in MRI.

2.6. MR Contrast Mechanisms

The relative advantage of MRI to the other neuroimaging methods is the high degree of tissue

contrast based on differences in relaxation properties and water concentration. The contrast

between two tissue types A and B, is the relative difference between the corresponding MR signals.

Fig. 2.6. The relationship between image space and k-space through the two-dimensional

Fourier transform of a brain image.

17

For a spin echo experiment, there are two important parameters that govern the MR signal

acquisition and contrast mechanisms. These parameters are the repetition time (TR), defined as the

time interval between successive excitation pulses, and the echo time (TE), defined as the time

interval between excitation and data acquisition [9]. The TR and TE, along with the relaxation

times T1 and T2 discussed in the previous sections, determine the contrast between tissues. We

calculate this contrast by subtracting the MR signals from tissues A and B, using the following

equation.

CAB = M0A (1 − e−

TR

T1A ) e−

TR

T2A – M0B (1 − e−

TR

T1B ) e−

TR

T2B (2.31)

2.6.1. T1 contrast

The most common static contrast mechanism used to obtain structural brain information is based

on the T1 relaxation of atomic nuclei, also known as T1-weighed imaging. The tissues show a

maximum difference in T1 values for an optimal value of TR. For an exclusive T1 contrast, T2

contrast should be minimized using short TE. For TE << T2 (e−

TR

T2 1), Eq. 2.31 becomes

CAB = M0A (1 − e−

TR

T1A ) − M0B (1 − e−

TR

T1B ) (2.32)

Therefore, in order to acquire the T1 sensitive images, short echo times and intermediate repetition

times are employed.

2.6.2. T2 contrast

The signal loss in the case of T2 weighted imaging also depends on the TR and TE values. For

optimal T2 contrast, TR should be long so that T1 contrast is minimal. For TR >> T1 (𝑒−𝑇𝑅

𝑇1 0),

Eq. 2.32 becomes,

CAB = M0Ae−

TR

T2A – M0Be−

TR

T2B (2.33)

18

T2-weighted images are generated using spin echo pulse sequences (see 2.6.3) that have longer

repetition times and intermediate echo times. T2 images play an important role in clinical

applications as they show maximal contrast between fluid regions and the brain tissue, clearly

distinguishing various types of tumors, arteriovenous malformations, and other fluid associated

pathologies. High-resolution T2 images are also used as anatomical references in fMRI along with

T1-weighted images.

2.6.3. T2* contrast

Transverse relaxation is caused not only by spin-spin relaxation, but also by regional differences

in the spin precessional frequencies due to the local inhomogeneities in the magnetic field. The

combined effect of these two factors in the decay of transverse relaxation is defined as T2*

relaxation, given by,

1

T2∗ =

1

T2+

1

T2′ (2.34)

where T2 > T2* and T2

′ represents the dephasing factor due to the field inhomogeneities. T2* imaging

can sensitively differentiate oxygenated and deoxygenated hemoglobin present in the blood and

changes in overall blood volume, forming the basis for BOLD contrast fMRI (see Chapter 3).

Similar to the T2 contrast, T2* contrast is also achieved using pulse sequences with long repetition

times and intermediate echo times that matches the T2* value in the tissue. The effect of T2

′ due to

the field inhomogeneities can be compensated using a spin echo experiment, which consists of a

90o RF pulse and a 180o refocusing RF pulse with symmetric time delay TE/2 around the 180o

refocusing RF pulse and using long TR. The amplitude of the resulting signal, which is measured

at the echo time TE depends on T2, irrespective of local magnetic field inhomogeneities

as, S ~ e−

TR

T2 .

2.7. Conventional MRI

Conventional MRI experiments use a spin echo acquisition with readout encoding and phase

encoding as described above. This type of acquisition is time consuming, since each phase

19

encoding step is acquired in a separate signal excitation. The contrast mechanisms mentioned in

the previous section enable the anatomical imaging, albeit, at very long TR values. Anatomical

images maximize contrast at the expense of speed. The schematic of a gradient echo MRI pulse

sequence with a 90o radiofrequency (RF) excitation pulse, slice selection gradient (Gx), frequency

encoding gradient (Gy), phase encoding gradient (Gz), and readout (RO) are shown in Fig. 2.7. The

gradient echo MRI experiment employs a gradient echo that is sensitive to T2* signal dephasing,

for the purpose of fast imaging. It also allows to use short repetition times and low flip angles (flip

angle, θ = γB1τ, with τ being the duration of excitation pulse) to reduce the acquisition time to

several seconds for a single slice. However, in order to investigate the function of the brain using

MRI (fMRI), even faster imaging sequences are necessary, with temporal resolution of the order

of seconds or less, for whole-brain imaging, to map the physiological and task related signal

changes of interest (e.g., BOLD).

Fig. 2.7. Schematic of a gradient echo MRI pulse sequence with a 90o radiofrequency (RF)

excitation pulse, the slice selection (Gx), frequency encoding (Gy), phase encoding (Gz)

gradients, and the readout (RO).

20

2.8. Advanced MRI

2.8.1. Echo-Planar Imaging (EPI)

Conventional methods focus on encoding k-space line-by-line, which require a large number of

excitations for a single image, depending on the image matrix size. Nobel Laureate Mansfield, in

1977, proposed a method in which the entire k-space is encoded by rapid switching of gradients

with a single excitation, called echo-planar imaging (EPI) [6-9]. EPI requires the data acquisition

to be fast in the presence of T2*/T2 relaxation signal decay. The schematic of the EPI sequence with

the corresponding k-space encoding is shown in Fig. 2.8. The negative Gx and Gy gradients will

direct the k-space encoding to start from the bottom left corner. The k-space data is acquired

through scanning alternating k-space lines in opposite directions demanding a highly powerful

gradient system for such a rapid switchback. The artifacts introduced due to inconsistencies

between the different readout gradient polarities have to be corrected before the reconstruction,

using a navigator signal acquisition that measures these inconsistencies. The prevalent artifacts in

EPI are due to the inhomogeneities in static and gradient magnetic fields. Geometrical distortions

also occur in EPI due to the long k-space acquisition times following the excitation that lead to a

Fig. 2.8. a) Schematic of EPI pulse sequence. b) The k-space encoding, based on the gradients

of the corresponding colors in the pulse sequence (a) showing the rapid zig-zag movement

through k-space due to rapidly changing gradients.

21

convolution of the “slow” phase encoding process with local frequency offsets due to magnetic

field inhomogeneities (Eq. 2.35). EPI is a widely used imaging technique for high-speed imaging

that is available on all clinical scanners. However, it still poses limitations for imaging brain

function, in particular, it is sensitive to rapid movement of the head (intra-scan movement) and

heart beat related signal pulsations. In addition, EPI being a two-dimensional technique, it is highly

sensitive to motion in and out of the image plane that cannot be corrected for. These limitations

can be addressed by a more advanced, ultra-fast three-dimensional imaging technique built upon

EPI, known as Echo-Volumar Imaging (EVI), also proposed by Mansfield [16,17].

2.8.2. Echo-Volumar Imaging (EVI) [18]

The principle of the EVI is similar to EPI, except for the absence of a slice selection step. Instead,

either the whole volume of the object is excited (using nonselective RF pulse) or slab within the

object is excited (using a slab-selective RF pulse with a Z-gradient similar to slice selection) for

spatial encoding. The EVI technique allows to acquire an entire volume in a single excitation using

repeated EPI modules with phase encoding Z-gradient between the EPI modules, to resolve the

slices within the imaging volume. The EVI sequence thus employs an echo-planar frequency

encoding gradient and two interleaved phase encoding gradients.

The data acquisition technique used in the current study is a particular extension of the

conventional EVI, called the multi-slab echo-volumar imaging (MEVI) [18], a 3D sequence

developed based on a multi-echo EPI sequence [19-21] with fly-back along the kz (Fig. 2.9). The

acquisition of slabs shortens the duration of the readout module thereby improving image quality

compared with conventional EVI. Adjacent slabs are excited sequentially to cover a larger region

across the object and encoded during a TR using repeated EPI modules with interleaved phase

encoding gradients. The EPI modules consisted of trapezoidal oscillating gradients (GRO) along

the readout direction and a series of blipped primary phase encoding gradients (GPE1) that were

rewound at the end of every partition. A blipped secondary phase encoding gradient (GPE2) that

encodes the third spatial dimension was applied after each EPI module. Kz-space was encoded

symmetrically using a dephasing gradient before the first EPI module (kmax/2). The kx – ky space

22

trajectories for each kz step were traversed in the same direction using 4-fold acceleration for

GRAPPA [22] reconstruction. Geometrical distortion was computed using Eq. 2.35 from Jezzard

and Balaban, 1995 [23]:

dpixel = γ∆B0(x, y, z)Ta (2.35)

where γ is the gyromagnetic ratio and Ta is the sampling time. Blurring of the point spread function

due to the signal relaxation in the slice direction is computed using,

FWHMTTz∗ =

√3

π

Ta

Tz∗ ∆x (2.36)

The elongated readout of MEVI increases the effective echo time and as a consequence the BOLD

contrast for small structures, compared to EPI. The effect of the long MEVI readout on BOLD

contrast can be computed as a function of kz using,

∆S(t) = S0t

Tz∗ e

−(t

Tz)∆Tz

∗

Tz∗ (2.37)

EVI reconstruction performance is further improved due to the high SNR afforded by the slab

selection compared to EPI. Numerically optimized slab selection RF pulses and spatial

oversampling of one slice on either side of each slab were used to minimize inter-slab crosstalk,

to avoid spatial aliasing and to minimize signal losses at the slab edges. The bandwidth of the RF

excitation pulse was increased almost 2-fold (time-bandwidth product:10) and the duration of the

RF pulse was decreased 4-fold from 2560 μs of the product sequence down to 640 μs to improve

the slice profile and to minimize chemical shift and susceptibility related slab displacement.

2.8.3. Multi-slab Echo-Volumar Imaging (MEVI) Reconstruction [18]

The reconstruction pipeline employs distributed computing across the scanner using the image

computation environment (ICE) integrated with the custom fMRI research tool TurboFIRE. In-

plane (kx, ky) reconstruction of complex (magnitude and phase) images for each encoded kz step

and slab was performed online on the scanner. Image reconstruction in the kz dimension included

Hamming filtering, Fourier transformation, slice ordering and concatenation of stacks of slices

23

from different slabs to form contiguous 3D image volumes. Slice ordering consisted of the

following steps: (i) the offset D of the stack of slabs from the magnet center was computed based

Fig. 2.9. a) Schematic of the MEVI sequence. The pulse sequence for each slab consists of a

trapezoidal oscillating readout gradient (GRO) along the readout direction, a blipped primary

phase encoding gradient (GPE1) that is rewound at the end of every partition and a blipped

secondary phase encoding gradient (GPE2) that encodes the third spatial dimension. (b) K-

space trajectory for each slab with 4-fold acceleration in the ky-direction.

24

on the slab prescription on the scanner console, which includes the offset of the center of the

slabstack P with respect to the magnet center along the principal axes: right-left (RL), anterior–

posterior (AP), head-foot (HF), the slab rotation angles α around the RL axis, and β around the AP

axis, as well as the slice thickness. This involves computing the normal unit vector of the slabstack

(n ) using Euler rotation matrices, projecting the position vector P onto n, and adjusting a half-slice

offset to account for the digitization of the slices:

D = RL cosα sinβ− AP sinα+ HF cosα cosβ− 0.5SLT (2.38)

(ii) D determines the aliasing of reconstructed slices along the slice direction within the encoded

FOV (FOVz), which was accounted for by a circular shift of the stack of slices for each slab. (iii)

Slices at the edge of the slab were included, if their overlap with the slab exceeded a user-selected

fraction of the slice thickness (30%).

2.8.4. MEVI – Advantages and Limitations

Using whole-brain MEVI, 4-slab data is acquired with a TR of 286 ms (4 mm isotropic voxel size)

and a 2-slab partial brain data is acquired with a TR of 136 ms for partial (4×4×6 mm3 voxel size)

on a conventional clinical 3 T scanner equipped with 12-channel head coil. Functional MRI

benefits from such high temporal resolution as it facilitates higher sensitivity gains for mapping

the resting-state fluctuations at low frequencies (0.01 – 0.1 Hz). Apart from the increased temporal

resolution and enhanced BOLD sensitivity [18], the increased SNR per unit time of 3D versus 2D

encoding [24,25] makes EVI advantageous. EVI acquisitions, however, are associated with

considerable challenges, such as the need for advanced hardware, persistent image quality

constraints due to geometrical image distortion, blurring and signal drop outs that are exacerbated

by head movement, signal drifts due to gradient instability, and steady-state effects. These

challenges are particularly pronounced at high magnetic field strengths. Additionally, practical

applications of EVI are hampered by time consuming image reconstruction of the large amounts

of data generated. The primary objective of this study is to demonstrate the applicability of the

MEVI technique in real-time resting-state fMRI by taking advantage of the high sensitivity gains.

25

References

1. Gerlach, W., and Stern, O., 1924. Uber die Richtungsquantelung im Magnetfeld. Ann. Phys., 74: 673.

2. Rabi, I. I., Zacharias, J. R., Millman, S., Kusch, P., 1938. A new method of measuring nuclear magnetic moments. Phys. Rev., 53: 318.

3. Bloch, F., 1946. Nuclear induction. Phys. Rev., 70: 460, 1946. 4. Purcell, E. M., Torrey, H. C., and Pound, R. V., 1946. Resonance absorption by nuclear

magnetic moments in a solid. Phys. Rev., 69: 37, 1946. 5. Mansfield, P., Grannell, P. K., 1973. NMR ‘diffraction’ in solids? J. Phys. C: Solid state

phys.: L422, 1973. 6. Haacke, E. M., Brown, R. W., Thompson, M. R., and Venkatesan, R. 1999. Magnetic

resonance imaging: Physical principles and sequence design. John Wiley & Sons, NY. 7. Clare, S. 1997. Functional MRI: Methods and applications. University of Oxford. 8. Hornak, J. P. 1996-2014. The basics of MRI. Center for imaging science, Rochester

institute of technology. NY. 9. Huettel, A. A., Song, A. W., Mc Carthy, G., 2008. Functional magnetic resonance imaging,

2nd edition, Sinauer Associates, Inc, Publishers, MA, USA. 10. Slichter, C. P. 1996. Principles of magnetic resonance, Third edition. Springer-Verlag, NY. 11. Bernstein, M. A., King, K. F., Zhou, X. J., 2004. Handbook of MRI Pulse Sequences,

Elsevier academic Press., Burlington, MA, USA. 12. Kumar, A., Welti, D., and Ernst, R. R., 1975. NMR Fourier Zeugmatography. J. Magn.

Reson., 18: 69, 1975. 13. Lauterbur, P. C., 1973. Image formation by induced local interactions: Examples

employing NMR. Nature, 242: 190, 1973. 14. Posse, S., Otazo, R., Dager, S. R., and Alger, J. 2012. MR Spectroscopic Imaging:

Principles and Recent Advances. JMRI. doi: 10.1002/jmri.23945 15. Bracewell, R. N. 1986. The Fourier transform and its applications. McGraw-Hill, NY. 16. Masfield, P., Howseman, A. M., and Ordidge, R. J., 1988. Volumar imaging using NMR

spin echoes: echo-volumar imaging (EVI) at 0.1 T. J. Phys. E: Sci. Instrum. 22 (1989) 324-330. 0022-37351891050324 IOP Publishing Ltd.

17. Van der Zwaag, W., Francis, S., and Bowtell, R., 2006. Improved echo-volumar imaging (EVI) for functional MRI. MRM 56:1320-1327.

18. Posse, S., Ackley, E., Muthiak, R., Rick, J., et al. 2012. Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage 61 (2012) 115-130. doi: 10.1016/j.neuroimage.2012.02.059.

19. Posse, S., Wiese, S., Gembris, D., Mathiak, K., Kessler, C., Grosse-Ruyken, M.-L., Elghawagh, B., Richards, T., Dager, S.R., Kiselev, V.G., 1999. Enhancement of BOLD contrast sensitivity by single-shot multiecho functional MR imaging. Magn. Reson. Med. 42 (1), 87–97.

20. Speck, O., Hennig, J., 1998. Functional imaging by I0- and T2*-parameter mapping using multiimage-EPI. Magn. Reson. Med. 40 (2), 243–248.

26

21. Otazo, R. 2007. Advanced parallel magnetic resonance imaging methods with applications to MR spectroscopic imaging. Dissertation, University of New Mexico.

22. Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A., 2002. Generalized autocalibrating partially parallel acquisitions (GRAPPA) Magn. Reson. Med. 47, 1202–1210.

23. Jezzard, P., Balaban, R.S., 1995. Correction for geometric distortion in echo-planar images from B0 field variations. Magn. Reson. Med. 34 (1), 65–73.

24. Edelstein, W.A., Glover, G.H., Hardy, C.J., Redington, R.W., 1986. The intrinsic signal-to-noise ratio in NMR imaging. Magn. Reson. Med. 3 (4), 604–618.

25. Hu, Y., Glover, G.H., 2007. Three-dimensional spiral technique for high-resolution functional MRI. Magn. Reson. Med. 58 (5), 947–951.

27

Chapter 3 Functional Magnetic Resonance Imaging

3.1. Introduction

Functional neuroimaging techniques are primarily divided into two categories: direct measurement

of neuronal activity or indirect measurement of neuronal activity. Electromagnetic approaches,

such as electroencephalography (EEG) and magnetoencephalography (MEG), are characterized

by the direct measurement of neuronal surface currents. The metabolic approaches, on the other

hand, are based on the indirect measurement of the neural activity and the associated neurological

and physiological changes. Functional MRI (fMRI) [1-3] belongs to the latter category, where the

neuronal activity is measured indirectly by the MR signal due to the differences in the magnetic

properties of the hemoglobin in two different oxygenation states. This is called the blood-

oxygenation level dependent (BOLD) signal. The BOLD signal generation depends on the cerebral

blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of oxygen (CMRO2)

[4-9]. The complete understanding of such an indirect process requires a thorough analysis of the

brain’s metabolism, anatomical and functional neurovascular coupling, relationship between the

neuronal activity and the supply-demand chain of energy in the brain along with some additional

factors, which is beyond the scope of this thesis. Instead, this chapter focuses on the basic

properties of the BOLD signal.

The spin dephasing interactions, or transverse relaxation (T2 relaxation), as discussed in chapter 2

are of particular significance in fMRI. The transverse relaxation in an ideal homogenous magnetic

field should follow a free-induction decay (FID) signal profile with a time constant T2, but in

reality, this process is more rapid in the biological tissue due to local field inhomogeneities,

resulting in a reduced time constant T2*. These field variations alter the precessional frequency of

protons, perturbing the phase and accelerating the transverse relaxation. The local magnetic field

inhomogeneities depend on the local blood supply and physiological states. These in turn depend

on the neural activity, making the inhomogeneity measurement (T2*) an indirect estimate of the

neuronal activity. Functional MRI has significantly enhanced the ability to study functional brain

connectivity though the BOLD signal. It offers excellent spatial resolution relative to EEG or MEG

and higher temporal resolution compared to positron emission tomography (PET).

28

3.2. Blood-Oxygenation Level Dependent (BOLD) Signal

Seiji Ogawa [10,11] discovered that the blood vessels can be identified on T2* images by changing

the quantity of oxygenated blood flowing through the vessels. Under normal activity conditions,

the oxygenated hemoglobin (Hb) is converted to deoxyhemoglobin (dHb) at a constant rate in the

capillaries. During the neuronal activation either by external stimuli or by internal biophysical

mechanisms, the demand for oxygen increases in the activated brain regions. This causes changes

in the cerebral blood flow (CBF) and cerebral blood volume (CBV), which in turn changes the

oxygenated hemoglobin (diamagnetic) and deoxyhemoglobin (paramagnetic) levels. Additionally,

during activation, the vascular system supplies more oxygenated hemoglobin than is being

consumed. The overcompensated oxygenated hemoglobin flushes the deoxygenated hemoglobin

from the capillaries resulting in an increase in the MR signal. This disparity between the oxygen

consumption and utilization results in a reduced signal loss due to T2* effects, which forms the

basis for generation of the BOLD signal [1-8] and results in a brighter MR image.

3.3. Hemodynamic Response Function (HRF)

The change in the MR signal caused by neuronal activity is called hemodynamic response (HDR)

which depends on the rate of change of deoxygenated hemoglobin in a given voxel (measured by

the BOLD signal). The theoretical models of BOLD signal generation can be verified through

comparison with the amplitude and time course of the measured BOLD signal due to brief stimuli,

known as the temporal impulse response function or hemodynamic response function (HRF) [12-

14]. There are various modeling schemes for illustrating HDR, the most popular one being the

canonical HRF [12-16]. It is mathematically represented by a double gamma function shown in

Eq. 3.1.

ℋ(t, θ) = c1tα1−1

Γ(α1)eβ1t + c2

tα2−1

Γ(α2)eβ2t (3.1)

where Γ(𝛼𝑖) is the Gamma function, c1 ≥ c2 and θ = [c1, α1, β1, c2, α2, β2] ϵ ℝ6. The schematic

representation of HDR is shown in Fig. 3.1.

29

The measured signal change is the convolution of the HRF with the neuronal activation pattern.

The HDR can be broadly divided into three distinct segments, called initial dip (region ‘a’),

overcompensation (region ‘b’), and undershoot (region ‘c’). The merit of the double gamma HRF

is that it can establish a closer correspondence between the physiological time scales in the brain

and the HDR. For the first 1-2 s of the HDR cycle, the T2* signal decreases below the baseline,

due to the transient increase in deoxyhemoglobin from neuronal activity. The next 2-5 seconds are

governed by the oversupply of oxygenated hemoglobin, resulting in an effective decrease in

deoxyhemoglobin and an increase of the HDR to the maximum. The third segment of the HDR is

characterized by undershoot below the baseline amplitude, due to the increased blood volume and

reduced blood flow, after the neuronal activity [3].

3.4. Resting-state fMRI

Resting-state fMRI is the study of functional interactions among brain regions when the subject is

not performing any explicit task and with minimal extraneous thoughts. Discovered 20 years ago

Fig. 3.1. The hemodynamic response (HDR) represented as a double Gamma function which

consists of a) initial dip (1-2 s), b) primary response (2-5 s), and c) undershoot (1-3 s).

30

with the seminal work of Biswal et al., 1995 [17], it is now widely used to characterize the inter-

regional functional connectivity patterns/modes based on spontaneous, low frequency (< 0.1 Hz)

BOLD signal changes. The neuronal basis of these spontaneous low frequency fluctuations is not

yet completely understood. The conventional fMRI protocols have a temporal resolution of 0.5 Hz

(TR of 2-3 s), which causes the aliasing of high frequency components into the lower frequencies

(0.01–0.1 Hz). These high frequency components stem from the cardiac and respiratory pulsations,

introducing artificial correlations between anatomically distant brain regions. It has previously

been suggested that these spontaneous low frequency fluctuations are the result of cardiac and

respiratory induced high frequency fluctuations, rather than the representation of any neuronal

activity. However, it has been well established that the resting-state signals correlate between brain

regions that are anatomically distant but functionally related, e.g., motor, auditory and visual

cortices, supporting the neuronal basis. In addition, most of the power in the resting-state

fluctuation spectrum is solely from contributions of very low frequencies, which counters the

argument of aliasing high frequencies. In summary, more observations favor the neurological basis

of the resting-state signals. It has also been widely known that cardiac and respiratory pulsations

do confound the resting-state connectivity apart from motion and hardware induced noise.

3.5. Confounding Signals in Resting-state fMRI

Resting-state fMRI has been increasingly used as an adjunct to the task-fMRI for mapping

functional connectomics in groups of healthy controls and patients [18-20]. The weak BOLD

signal changes are confounded by body and head movements [21,22], physiological noise [23],

variations in neurovascular coupling, instabilities in the scanner baseline signal, sensitivity losses

due to susceptibility variations at tissue borders, ghosting due to even-odd echo interference in the

case of EPI, among other sources [24]. Regression of the confounding signals is a widely

implemented procedure in the analysis of fMRI data. The regression of the global mean, however,

has been a topic of intense debate [25-28], since it is known to introduce negative correlations and

bias positive correlations. This has led to the implementation of alternative approaches (e.g.

scrubbing, despiking) to remove movement related artifacts that confound functional connectivity

[29].

31

3.6. Preprocessing in fMRI

Seed-based connectivity analysis (see Chapter 1), is one of the most widely used fMRI data

analysis methods, which computes the correlations between each individual voxel time course and

the time course from an ROI. For such an implementation to be statistically meaningful, the data

should be free from a variety of confounding signals mentioned earlier. Preprocessing refers to a

series of computational techniques to compensate for unwanted variability in the data and to obtain

accurate statistical results by minimizing the ambiguity caused by such confounding signals of

non-neurological origin. Preprocessing is expected to enhance the functional resolution of an fMRI

experiment. The major preprocessing steps implemented in a typical fMRI analysis are as follows:

Motion Correction

Head and body movements pose substantial problems for fMRI studies. Motion correction has

been an area of intense investigation from the early days of fMRI experiments [30]. Since fMRI

acquisition is highly sensitive to motion, the slightest head movement can introduce spurious

connectivity across the brain, leading to misinterpretation of the connectivity results. Along with

preventing head motion to the greatest extent possible, various techniques were developed to

correct for head motion during and after the scan. Motion correction is implemented using six rigid

body transformations (three translational and three rotational). The effect of the movement is

estimated using these six parameters, their (higher) derivatives and mitigated using regression

analysis.

Slice time correction

During a multi-slice fMRI acquisition, the slices may be collected using a number of RF pulses at

various time points within a TR (e.g., ascending/descending slice acquisition or interleaved

acquisition). Due to the imperfections in RF profiles, the immediate neighborhood of the selected

slice is also excited. This region should be avoided in the following slice acquisition, as the spins

are not recovered to the equilibrium. It is a common practice to excite by leaving a gap between

the slices or to excite either even or odd numbered slices to mitigate these signal changes due to

cross-slice excitation. These time differences are corrected prior to the analysis using temporal

interpolation.

32

Spatial and temporal filtering

Spatial filtering, also known as spatial smoothing, is typically implemented using a Gaussian filter

in order to distribute the intensity of each voxel to the nearby voxels. This results in a high SNR

thereby improved statistical validity of functional connectivity metrics [31]. Temporal filtering

improves functional SNR by minimizing the signal changes at unwanted frequency bands, e.g.

physiological noise components in the frequency ranges 1.0 -1.5 Hz (heart rate) and 0.2 – 0.3

(respiration). The presence of temporal autocorrelations, which can bias the connectivity results,

should be accounted for, by applying filters to remove autocorrelations in time series data, before

the analysis. This process is known as pre-whitening [32].

Coregistration

The low spatial resolution of fMRI images cannot provide finer details of the anatomy in a subject.

In most of the studies, it is necessary to map the functional activations onto high-resolution and

high contrast anatomical images in order to accurately delineate the regions associated with a

specific function. The computational technique that maps the functional images onto anatomical

images, is called coregistration.

Spatial Normalization

Human brains show extensive morphological variability. When investigating a specific function in

the brain, integration of the data over a large number of subjects is often necessary. Spatial

normalization is a mathematical technique and an extension of coregistration, through which each

brain is stretched, squeezed and warped so that the anatomy is similar to a reference brain.

Normalization is typically performed using standard atlas spaces like Talairach or MNI [33].

3.7. Resting-state Networks

The brain is an intricate network with regions that are structurally distributed but functionally

connected. Functional connectivity is defined as the temporal dependency of neuronal activation

patterns of anatomically separated brain regions. Recent advances in the fMRI acquisition and

analysis methods have been instrumental in probing the functional networks in the primate as well

33

as human brain. A vast number of group studies have established a significant number of highly

correlated functional brain regions during the resting-state condition, known as resting-state

networks [19,20]. Prototypes of some of the major classes of such networks are shown in Fig. 3.2.

Despite the fact that these studies have used different subject populations, fMRI acquisition

protocols, and analysis methods (chapter 1), they show a great deal of common features with

respect to the functional connectivity patterns.

In the case of seed-based correlation analysis, if the region of interest selected is an ipsilateral

motor, auditory, or visual cortex, a strongly correlated contralateral side is observed, supporting

the functional connectivity hypothesis. In the case of ICA, these bilateral networks are identified

based on the fluctuation frequency of a given network. These findings suggest that neurons show

spontaneous activity even in the absence of external stimuli, potentially allowing for the

continuous transmission of information to the other neurons.

Fig. 3.2. The six major classes of resting-state networks (RSNs) with blue regions representing

the principal activation regions in: a) attention network, b) auditory network, c) default-mode

network, d) frontal network, e) sensorimotor network, and f) visual network.

34

References 1. Buxton, R. B., 2002. Introduction to functional magnetic resonance imaging: Principles

and techniques. Cambridge University Press, New York. 2. Haacke, E. M., Brown, R. W., Thompson, M. R., and Venkatesan, R., 1999. Magnetic

resonance imaging: Physical principles and sequence design. John Wiley & Sons, NY. 3. Huettel, A. A., Song, A. W., Mc Carthy, G., 2008. Functional magnetic resonance imaging,

Second edition, Sinauer Associates, Inc, Publishers, MA, USA. 4. Logothetis, N. K., Pfeuffer, J., 2004. On the nature of the BOLD f MRI contrast

mechanism. Magnetic Resonance Imaging 22, 1517 – 1531. 5. Logosthetis, N., 2008. What we can do and what we cannot do with fMRI. Nature. Vol 453

12 June 2008. doi:10.1038/nature06976. 6. Logosthetis, N., 2003. The Underpinnings of the BOLD Functional Magnetic Resonance

Imaging Signal. The Journal of Neuroscience, 23(10):3963–3971 7. Logosthetis, N., Wandell, B. A., 2004. Interpreting the BOLD signal. Annu. Rev. Physiol.

2004. 6:735–69. doi: 10.1146/annurev.physiol.66.082602.092845. 8. Logothetis, N. K., 2002. The neural basis of the blood-oxygen-level dependent functional

magnetic resonance imaging signal. Phil. Trans. R. Scp. Lond. B. 9. Edlstein, W. A., Glover, G. H., Hardy, C.J., and Redington, R. W., 1986. The intrinsic

signal-to-noise ratio in NMR imaging. Mahn. Reson. Med., 3: 604-618. 10. Ogawa, S., Kim, S. G., Ugurbil, K., and Menon, R. S., 1998. On the characteristics of fMRI

in the brain. Ann. Rev. Biophys. Biomol. Struct., 27:447-474. 11. Kim, S., and Ogawa, S., 2012. Biophysical and physiological origins of blood-oxygenation

level dependent fMRI signals. J of Cereb. Blood Flow & Met, 1–19. 12. Aguirre, G. K., Zarahn, E., D’Esposito, M., 1998. The variability of human BOLD

hemodynamic responses. NeuroImage, 8: 360-369. 13. Lindquist, M. A., Loh, J. M., Atlas, L. Y., Wager, T. D., 2009. Modeling the hemodynamic

response function in MRI: Efficiency, Bias and Mis-modeling. NeuroImage 45 (1 Suppl): S187-S198. doi: 10.1016/j.neuroimage.2008.10.065.

14. Handwerker, D. A., Ollinger, J. M., and D’ Esposito, M., 2004. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage, 21: 1639-1651.

15. Saad, Z. S., Ropella, K. M., DeYoe, E. A., and Bandettini, P. A., 2003. The spatial extent of the BOLD response. NeuroImage, 19:132-144.

16. Barbe, K., Van Moer, W., Nagels, G. 2012. Fractional-order time series models for extracting the hemodynamic response from functional magnetic resonance imaging data. IEEE transactions on biomedical engineering, vol. 59, No. 8, August 2012.

17. Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi: 10.1002/mrm.1910340409.

18. Raichle, M. E., MacLeod, A. M., Snyder, A. J., et al, 2001. A default-mode of brain function. Natl Acad Sci USA 2001; 98:676-82.

35

19. Lee, M. H., Smyser, C. D., Shimony, J. S. 2013. Resting-State fMRI: A Review of Methods and Clinical Applications. AJNR. http://dx.doi.org/10.3174/ajnr.A3263.

20. Van den Heuvel, M. P., Hulshoff Pol H. E., 2010. Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology 20, 519–534. doi: 10.1016/j.euroneuro.2010.03.008.

21. Satterthwaite, T. D., Elliott, M, A., Gerraty, R. T., et al., 2013. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64 (2013) 240-256.

22. Van Dijk, K. R. A., Sabuncu, M. R., Buckner, R. L., 2012. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59 (2012) 431-438. Doi: 10.1016/j.neuroimage.2011.07.

23. Triantafyllou, C., Hoge, D. R., Krueger, G., Wiggins, J. C., Potthast, A., Wiggins, C.G., and Wald, L. L., 2005. Comparison of physiological noise at 1.5 T, 3 T, and 7 T and optimization of fMRI acquisition parameters. NeuroImage 26 (2005) 243-250.

24. Greve, D. N., Brown, G. G., Mueller, B. A., Glover, G., Liu, T. T., 2013. A survey of the sources of noise in fMRI. Psychometrika – vol 78, No. 3, 396-416. Doi: 10.1007/s11336-012-9294-0.

25. Chang, C., Glover, G. H., 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50, 81-98. doi: 10.1016/j.neuroimage.2009.12.011.

26. Fox, M. D., Zhang, D., Snyder, A. Z., and Raichle, M. E., 2009. The global signal and observed anti-correlated resting-state brain networks. J. Neurophysiol. 101, 3270-3283. doi:10.1152/jn.90777.2008.

27. Liu, X., and Duyn, J. H., 2013. Resting-state fMRI signal anti-correlation exists in absence of global signal regression. Proc. Intl. Soc. Mag. Reson. Med. 21, 2013.

28. Murphy, K., Birn, R. M., Bandettini, P. A 2013. Resting-state fMRI confounds and cleanup. NeuroImage 80, 349-359. doi:10.1016/j.neuroimage.2013.04.001.

29. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlagger, B. L., Petersen, S. E., 2014. Methods to detect, characterize, and remove motion artifact in resting-state fMRI. NeuroImage 84, 320-341. http://dx.doi.org/10.1016/j.neuroimage.2013.08.048.

30. Power, J. D., Schlaggar, B. L., Petersen, S. E., 2014. Recent progress and outstanding issues in motion correction in resting-state fMRI. doi:10.1016/j.neuroimage.2014.10.044.

31. Lowe, M. J., Mock, B. J., and Sorenson, J. A., 1998. Functional connectivity in single and multislice echo-planar imaging using resting-state fluctuations. NeuroImage 7, 119-132 (198). Article no. NI 970315.

32. Woolrich, M. W., Ripley, B. D., Brady, M., Smith, S. M. 2001. Temporal autocorrelation in univariate linear modeling of fMRI data. NeuroImage 14, 1370-1386. doi:10.1016/nimg2001.0931.

33. Chau, W., McIntosh, A. R., 2005. The Talairach coordinate of a point in the MNI space: how to interpret it. NeuroImage, 25: 408–416.

36

Chapter 4 Windowed Seed-based Connectivity Analysis [1]

4.1. Introduction

Resting-state connectivity is currently studied in a wide range of neuroscience and clinical

applications [2-8]. Functional connectomics group studies across large number of subjects have

identified over a hundred RSNs, which exhibit considerable spatio-temporal non-stationarity [9-

12]. Among the methods developed to measure resting-state network (RSN) connectivity [13-17],

seed-based connectivity analysis (SCA) is well established, particularly for clinical research

studies, since it provides high sensitivity and straightforward interpretation of connectivity in

single subjects. However, the small amplitude of blood-oxygenation level dependent (BOLD)

signal changes renders resting-state fMRI sensitive to a wide range of confounds as discussed in

chapter 2 [18-26]. Studies have demonstrated that the regression of measured head movement

parameters and regionally averaged time courses from white matter and the ventricles is essential

for artifact removal. Further, the regression of the global mean has been a topic of intense debate,

since it may not only introduce anti-correlations, but also bias the positive correlations [27-39].

The realization that deconvolution of movement artifacts in post-processing using conventional

motion correction approaches and confound regression remains incomplete has led to increased

use of alternate approaches (e.g., data scrubbing, despiking) to remove residual movement related

artifacts that confound functional connectivity metrics [40-44]. Confound correction continues to

be a topic of intense research in fMRI [18,34,45-48]. Further technical development is necessary

to establish confound-tolerant resting-state mapping approaches that complement the existing

model-based and data-driven analysis methods.

We recently introduced a windowed seed-based connectivity analysis (wSCA) approach

[1,49] targeted at single-subject analysis, which provides confound suppression without the need

for regression of confounding signal changes. This wSCA approach employs sliding-window

correlation analysis with running mean and standard deviation across dynamically updated

correlation maps (meta-statistics) [1,49,50]. Furthermore, it combines high-pass filter

characteristics with decorrelation of confounding signal events by constraining the effect of the

confounds to the windows that contain the confounding signals. The computational efficiency

makes this approach suitable for real-time fMRI [51,52]. The wSCA approach is particularly well

suited for high-speed fMRI methods [53,54] that enable unaliased measurement of cardiovascular

37

and respiration related signal fluctuations. High-speed fMRI considerably enhances the sensitivity

of mapping RSNs compared with the conventional EPI. Simultaneous multi-slice (SMS) or multi-

band EPI [55,56], magnetic resonance encephalography (MREG) [57], and multi-slab echo-

volumar imaging (MEVI) [54,58] have emerged as the principal high-speed fMRI data acquisition

techniques. Using wSCA in combination with high-speed MEVI [54], we demonstrated in a

preliminary study the feasibility of mapping RSNs in eloquent cortex in patients with brain tumors,

epilepsy, and arteriovenous malformations [49].

The present chapter characterizes the confound suppression and intra- and inter-RSN

connectivity strength of the wSCA as a function of sliding-window width, resting-state frequency,

and scan duration, both in computer simulations and in vivo in single subjects. The confound

tolerance and sensitivity for mapping six RSNs is compared with regression-based conventional

SCA (cSCA) in single subjects. We also investigated the feasibility of mapping 28 RSNs in single

subjects that were previously detected in a group-ICA study [59] with optimized processing

parameters for real-time connectivity mapping.

4.2. Subjects and Data Acquisition

Resting-state scans were acquired in 5 right-handed healthy controls (3 female and 2 male) aged

between 21-50 years using a scan duration of 5 minutes. These 5 subjects are a subset of data

studied previously [49,54]. Subjects were instructed to keep their eyes open, clear their mind, relax

and fixate on a cross-hair presented on a computer screen. Institutionally reviewed, informed

written consent was obtained.

Data were collected using a 3T Siemens TIM Trio clinical scanner equipped with 12-channel (4

Subjects) and 32-channel (1 Subject) head array coils [49,54]. A high-speed MEVI pulse sequence

was used for data acquisition with fly-back along the kz – direction, as described in [54]. Multiple

adjacent slabs were excited sequentially in a single TR and encoded using repeated EPI modules

with interleaved phase encoding gradients, fourfold acceleration using generalized autocalibrating

partially parallel acquisition (GRAPPA), 6/8 partial Fourier encoding, and oversampling along the

slab-direction. The MEVI scan parameters: 2 slabs, TR: 136 ms, TEeff : 28 ms, α: 10º, two slabs in

38

Table 4.1. Seed locations adopted from a spatial group-ICA study [59]. This table lists the 28 seeds that belongs to seven classes of RSNs and the corresponding independent components (ICs), MNI coordinates, and the functional brain regions. For optimization of the preprocessing parameters, six seeds are chosen from six classes of networks indicated by an asterisk [*] (RSN* seeds: ATN*, AUN*, DMN*, FRN*, SMN*, and VSN*).

R e s t in g -s ta te N e tw o r k

I n d e p e n d e n t C o m p o n e n t

C o o r d in a te s in M N I te m p la te

F u n c t io n a l B r a in R e g io n

B r o d m a n n A r e a

A T N IC 3 4 * ( -4 7 , -5 7 , 3 9 )* L e ft in fe r io r p a r ie ta l lo b u le B A 4 0

IC 6 0 (4 2 , -5 6 , 4 2 ) R ig h t in fe r io r p a r ie ta l lo b u le B A 4 0

IC 5 2 ( -3 3 , -6 4 , 3 1 ) L e ft a n g u la r g y ru s B A 3 9

IC 7 2 (0 , -5 3 , 6 1 ) B ila te ra l p re c u n e u s B A 0 7

IC 7 1 (5 7 , -4 4 , 1 1 ) R ig h t s u p e r io r te m p o ra l g y ru s B A 4 0

IC 5 5 (0 , 2 2 , 4 5 ) B ila te ra l c in g u la te g y ru s B A 4 0

A U N IC 1 7 * ( -5 1 , -1 8 , 7 )* L e ft s u p e r io r te m p o ra l g y ru s B A 2 2

B G N IC 2 1 (2 5 , -1 , 0 ) R ig h t p u ta m e n

D M N IC 5 0 * (1 , -6 4 , 4 3 )* B ila te ra l p re c u n e u s B A 0 7

IC 5 3 (0 , -5 2 , 2 2 ) B ila te ra l p o s te r io r c in g u la te c o r te x B A 2 3

IC 2 5 (0 , 4 1 , 4 ) B ila te ra l a n te r io r c in g u la te c o r te x B A 3 2

IC 6 8 ( -2 6 , 2 6 , 4 2 ) L e ft m id d le f ro n ta l g y ru s B A 0 8

F R N IC 4 2 * (5 0 , -2 3 , 2 )* R ig h t in fe r io r f ro n ta l g y ru s B A 4 5

IC 2 0 ( -5 5 , 2 2 , 7 ) L e ft in fe r io r f ro n ta l g y ru s B A 2 3

IC 4 7 ( -4 8 , 1 7 , 2 9 ) L e ft m id d le f ro n ta l g y ru s B A 0 9

IC 4 9 (3 1 , 5 5 , 7 ) R ig h t m id d le f ro n ta l g y ru s B A 1 0

S M N IC 0 7 * ( -5 2 , -9 , 3 1 )* L e ft p re c e n tra l g y ru s B A 0 6

IC 2 3 ( -3 5 , -2 7 , 5 4 ) L e ft p re c e n tra l g y ru s B A 0 4

IC 2 4 (3 7 , -2 5 , 5 3 ) R ig h t p re c e n tra l g y ru s B A 0 4

IC 2 9 (1 , -2 8 , 6 1 ) B ila te ra l p a ra -c e n tra l lo b u le B A 0 6

IC 3 8 ( -5 5 , -3 4 , 3 7 ) L e ft s u p ra m a rg in a l g y ru s B A 0 2

IC 5 6 (1 , -3 , 6 1 ) B ila te ra l s u p p le m e n ta ry m o to r a re a B A 0 6

V S N IC 4 6 * (1 , -8 7 , -2 )* B ila te ra l l in g u a l g y ru s B A 1 7 , 1 8

IC 6 4 (1 , -7 1 , 1 3 ) B ila te ra l c a lc a r in e g y ru s B A 1 7 , 1 8

IC 6 7 ( -1 5 , -5 6 , -8 ) L e ft l in g u a l g y ru s B A 1 8

IC 4 8 (2 9 , -7 6 , -8 ) R ig h t l in g u a l g y ru s B A 1 8 , 1 9

IC 3 9 (4 8 , -6 3 , -8 ) R ig h t in fe r io r te m p o ra l g y ru s B A 3 7

IC 5 9 (2 , -8 4 , 2 8 ) B ila te ra l c u n e u s B A 1 9

39

AC/PC orientation, slab thickness: 42 mm, inter-slab gap: 10%, matrix per slab: 64×64×8, Field

of View (FOV) per slab: 256×256×48 mm3, reconstructed voxel dimensions: 4×4×6 mm3, 13

slices, 2200 scan repetitions, scan time: 5 min and 16 s. The in-plane image reconstruction was

performed on the scanner and the through-plane reconstruction was performed in real-time using

our custom TurboFIRE (Turbo Functional Imaging in Real-time) fMRI analysis tool [49,60].

4.3. Seed-based Connectivity Analysis (SCA)

The reconstructed data were processed using TurboFIRE (version v5.12.4.0.2) on an Intel Xeon

E5530, 6 core, 2.4 GHz workstation. The processing steps included: six-parameter rigid body

motion correction [61], spatial normalization into MNI space using a look-up table approach [62]

with a 4×4×4 mm3 target voxel size, spatial smoothing of raw images using an 8×8×8 mm3

Gaussian spatial filter, and a 2 s moving average time domain low-pass filter [63,64] to suppress

signal fluctuations due to cardiac and respiratory pulsations. A 10% threshold was applied to the

intensity of the raw images to avoid any spurious correlations outside of the brain. A maximum of

six regions of interest (ROI) were simultaneously processed in TurboFIRE to create dynamic

reference vectors for online correlation analysis [65]. In addition to the display of sliding-window

meta-statistics maps, the cross-correlation values between the six ROI time courses were

continuously displayed in the form of 6×6 color-coded correlation matrices in a separate window.

Non-thresholded meta-statistics tables and the corresponding maps were obtained for all the voxels

at the end of the scan for six seed regions. This processing pipeline was used to monitor

connectivity in six RSNs during the ongoing scan using spatial normalization by mapping the MNI

atlas into subject space. To obtain meta-statistics for all 28 ROIs across subjects (see Table 4.1),

this pipeline was repeated offline for sets of six ROIs using spatial normalization into MNI space.

The analysis of the time series was limited to 1400 scan repetitions due to computer memory

limitations.

4.3.1. Seed Selection

The 28 single-voxel seeds were selected from the peak activations of the 28 RSNs that belong to

seven major classes of RSNs: attention (ATN), auditory (AUN), basal ganglia (BGN), default-

mode (DMN), frontal (FRN), sensorimotor (SMN), and visual (VSN) identified in [59] based on

40

the maximum t-score value and the spatial extent of the cluster (Table 1). The effective full width

half maximum (FWHM) of a single pixel seed region after spatial filtering was approximately

12×12×14 mm3. The coordinates of the seed locations were selected after spatial normalization

into MNI space and automatically assigned using the Talairach Daemon (TD) database [66,67].

The 140 brain regions from the TD database are shown in Table 4.2. The transformation from MNI

to Talairach coordinates was performed using Matthew Brett’s formula (http://www.mrc-

cbu.cam.ac.uk/Imaging/mnispace.html).

4.3.2. Conventional Seed-based Connectivity Analysis (cSCA)

Regression-based conventional SCA was performed in five subjects to compare with the

windowed SCA. Prior to performing connectivity analysis, voxel time courses were denoised using

a univariate general linear model (GLM) framework implemented in SPM

Table 4.2. Brain segmentation and region assignment based on the Talairach Daemon database.

These 70 regions are mapped to both left and right hemispheres, covering 140 functional brain

regions (BA: Brodmann Area).

1 B A 0 1 1 5 B A 1 9 2 9 B A 3 4 4 3 A m y g d a la 5 7 M a m m illa ry B o d y

2 B A 0 2 1 6 B A 2 0 3 0 B A 3 5 4 4 A n te r io r C o m m is s u re

5 8 M e d ia l D o rs a l N u c le u s

3 B A 0 3 1 7 B A 2 1 3 1 B A 3 6 4 5 A n te r io r N u c le u s 5 9 M e d ia l G e n ic u lu m B o d y

4 B A 0 4 1 8 B A 2 2 3 2 B A 3 7 4 6 C a u d a te B o d y 6 0 M e d ia l G lo b u s P a l l id u s 5 B A 0 5 1 9 B A 2 3 3 3 B A 3 8 4 7 C a u d a te H e a d 6 1 M id lin e N u c le u s

6 B A 0 6 2 0 B A 2 4 3 4 B A 3 9 4 8 C a u d a te T a i l 6 2 P u lv in a r

7 B A 0 7 2 1 B A 2 5 3 5 B A 4 0 4 9 C o rp u s C a llo s u m 6 3 P u ta m e n 8 B A 0 8 2 2 B A 2 7 3 6 B A 4 1 5 0 D e n ta te 6 4 R e d N u c le u s

9 B A 0 9 2 3 B A 2 8 3 7 B A 4 2 5 1 H ip p o c a m p u s 6 5 S u b s ta n ia N ig ra

1 0 B A 1 0 2 4 B A 2 9 3 8 B A 4 3 5 2 H y p o th a la m u s 6 6 S u b th a la m ic N u c le u s 1 1 B A 1 1 2 5 B A 3 0 3 9 B A 4 4 5 3 L a te ra l D o rs a l

N u c le u s

6 7 V e n tra l A n te r io r N u c le u s

1 2 B A 1 3 2 6 B A 3 1 4 0 B A 4 5 5 4 L a te ra l G e n ic u lu m B o d y

6 8 V e n tra l L a te ra l N u c le u s

1 3 B A 1 7 2 7 B A 3 2 4 1 B A 4 6 5 5 L a te ra l G lo b u s

P a l l id u s

6 9 V e n tra l P o s te r io r L a te ra l

N u c le u s 1 4 B A 1 8 2 8 B A 3 3 4 2 B A 4 7 5 6 L a te ra l P o s te r io r

N u c le u s

7 0 V e n tra l P o s te r io r M e d ia l

N u c le u s

41

(http://www.fil.ion.ucl.ac.uk/spm/software/spm12). Following the coregistration of functional

images with anatomical images (and normalization into MNI space), time courses were obtained

from the motion corrected data using automated delineation of voxels of interest from each of the

delineated sub-regions of WM and CSF. The regression was performed with AFNI software

(https://afni.nimh.nih.gov/afni/) and custom MATLAB scripts using 14 independent regressors:

the mean time courses from white matter (WM) and cerebrospinal fluid (CSF), and 12 individual

motion parameters (6 rigid body motion estimates and their derivatives). Neither scrubbing nor

despiking was used. The subsequent seed-based connectivity analysis was performed with

TurboFIRE using conventional cumulative correlation analysis (i.e. mean and SD of correlations

across the entire scan without a moving average filter).

4.3.3. Windowed Seed-based Connectivity Analysis (wSCA)

A schematic overview of the wSCA approach is shown in Fig. 4.1. The time courses of the regions

of interest (ROIs/seeds) are extracted from the preprocessed data. The signal time course within

each seed region is used as an input for generating a dynamic reference vector model [65]. The

cross-correlation coefficients between the seed time courses and the individual voxel time courses

at each TR, using sliding-window correlation analysis described in Gembris et al., 2000 [60]. The

meta-statistics approach (running mean and SD of Pearson's correlation coefficients) uses an

efficient running variance algorithm [50] to compute cumulative meta-maps of the mean and SD

of the correlations during the scan [49]. The initial 50 scans (7 s of data acquisition) were discarded

to account for non-steady-state signal changes.

4.4. Simulations of Confound Suppression using Windowed SCA

The confound suppression of the wSCA approach was characterized and quantified in a series of

simulations as a function of the sliding-window width and frequency. The mathematical modeling

of the wSCA approach was implemented using custom MATLAB scripts. The simulations were

performed for a 70 s time series as a function of the sliding-window width (4 – 70 s) and RSN

frequency (0.0001-0.5 Hz). Resting-state fMRI signal fluctuations were simulated for two

42

scenarios: (a) identical resting-state signal oscillation in two brain regions (two nodes of an RSN)

with added random noise (SNR = 1), and (b) uncorrelated random signal changes in two brain

regions. The following commonly observed confounding signals were added to the two simulation

models: a linear signal drift, a signal offset, and a signal spike. These confounding signals were

introduced into one of the nodes of the RSN in (a), thus decreasing connectivity, and into both

brain regions in the case of uncorrelated noise in (b), introducing artifactual connectivity. The

addition of the above confounding signals to both the nodes of the simulated RSN, scenario (a),

was also investigated. The simulations were averaged over 100 iterations with different

instantiations of random noise and the standard deviation was computed. The confound

Fig. 4.1. Windowed seed-based connectivity analysis (wSCA). Extraction of time courses

from seed regions and computation of seed-based sliding-window correlations with running

mean and standard deviation (meta-statistics) across the correlation maps.

44

suppression of the wSCA was quantified in terms of Fisher Z-transformed correlation coefficients.

The simulations in Fig. 4.2A show the estimation of connectivity (Fisher Z-transformed correlation

coefficients) in the presence of a confound in one of the two nodes of the RSN as a function of

sliding-window width. The connectivity increases with decreasing window width for the three

confound types (drift, offset, and spike) until it reaches a value near the confound-free case (i).

The corresponding simulations of two brain regions without resting-state connectivity in Fig. 4.2C

show the opposite case. The introduction of confounds in both brain regions results in decreasing

connectivity with decreasing window width until it reaches a value near the confound-free case

(ix). Of note, wSCA does not change the intrinsic connectivity between two brain regions in the

absence of confounds, independent of sliding-window width (Fig. 4.2 i, ix). Fig. 4.2B shows the

frequency dependence of confound suppression for the case of Fig. 4.2A (confound in one of the

two RSN nodes) which is independent of the RSN frequency above a threshold frequency. This

threshold frequency increases with decreasing sliding-window width, below which the confound

suppression decreases with decreasing frequency. The frequency dependence of the connectivity

for the offset (iii, vii, xi) and the spike (iv, viii, xii) displays an oscillating behavior, an interference

pattern reflecting the frequency content of the confound. The magnitude of changes in repeated

instantiations of the confounds were very small as shown in the error bars in Fig.4.2, even for

shorter windows.

In addition, we simulated the case of adding single instances of the confounding signals to both

nodes of the RSN (data not shown), which in case of the maximum window width increased the

Z-scores beyond the confound-free case. With decreasing sliding-window width the Z-scores

decreased to the confound-free case with a similar profile as in Fig. 4.2A. The combinations of

multiple confounds in both nodes of the RSN further increased the Z-scores and resulted in a

steeper decrease with decreasing sliding-window width. Further, we also simulated multiple

instances of the confounds (repeated spikes of 4 s length, continuous box car, continuous saw-

tooth) in both the nodes of the RSN, showing similar confound suppression behavior as in the

previous case of a single instance, although with higher Z-scores for the maximum window width.

Nonetheless, consistent confound suppression was found for the shortest sliding-window. These

additional results further substantiate the robustness of confound suppression using wSCA in the

presence of multiple serial and overlapping confounding signal changes. In summary, the shorter

45

Fig. 4.3A. Resting-state connectivity and confound suppression in vivo in 6 major RSNs using

windowed SCA in comparison with conventional SCA. Meta-mean maps are displayed as a

function of sliding-window width (30 s, 15 s, 8 s, and 4 s) and scan duration (30 s, 60 s, 120 s,

and 180 s) in a representative subject. The confound reduction with decreasing sliding-window

width results in a decrease of spurious connectivity in white matter and in gray matter regions

that are unrelated to the 6 major RSNs. The green arrows indicate the seed locations. The maps

are in neurological orientation at zero meta-mean correlation threshold with slice locations given

by the Z-coordinates.

46

the sliding-window, the stronger the confound suppression, but the higher the cut-off frequency

that restricts the confound suppression at low frequencies. The simulations suggest that a sliding-

window of 15 s provides a compromise between the overall degrees of confound suppression and

the suppression efficiency at the low frequencies that constitute the resting-state frequency

spectrum.

4.5. Performance Comparison of Windowed and Conventional SCA

The confound suppression performance of the windowed and conventional SCA methods was

characterized as a function of sliding-window (4s, 8 s, 15 s, and 30 s) using two types of

connectivity analyses: (a) the comparison of the strength of the intra- and inter-network

connectivity in six RSNs using six single-voxel seeds, and (b) the comparison of the strength of

the artifactual connectivity using 4 supraventricular white matter single-voxel seeds [coordinates:

(-26, -38, 48), (26, 7, 31), (-26, -39, 41), (-30, 11, 27)].

The intra- and inter-network connectivity maps were computed for all voxels to assess the residual

artifactual connectivity across the entire brain. The average positive connectivity (average across

the regions with mean positive correlations) and the average negative connectivity (average across

the regions with mean negative correlations) were computed by segmenting the connectivity maps

Fig. 4.3B. Comparison of average positive connectivity and negative connectivity across 140

brain regions and the 6 RSNs shown in Fig. 4.3A as measured by the conventional SCA and

the windowed SCA as a function of sliding-window width and scan duration.

47

into 140 atlas-based brain regions and averaging across the 6 RSNs and 5 subjects. For each of the

6 RSNs, the intra-network connectivity was computed by averaging the region-wise correlation

values across a subset of regions (ATN: BA32, 33; AUN: BA41, 42; DMN: BA7, 23, 31; FRN:

BA9, 10; SMN: BA1-4, VSN: BA17-19) and the extra-network connectivity was computed by

averaging the region-wise correlation values across the remainder of the 140 brain regions. The

intra- and extra-network connectivity for each of the six RSNs as a function of sliding-window

width and scan duration for both wSCA and cSCA was presented as a color map.

The connectivity maps using WM seeds were computed at zero meta-mean threshold to quantify

the residual artifactual connectivity across the entire brain. The positive global mean artifactual

connectivity (average across the regions with mean positive correlations) and the negative global

mean artifactual connectivity (average across the regions with mean negative correlations) were

computed by segmenting the connectivity maps into 140 atlas-based brain regions.

4.5.1. Computational Efficiency

The data processing time for cSCA was on the order of 35-45 min including extraction of

regressors, GLM analysis for confound regression, and correlation analysis using 6 seeds. The

processing time for analyzing 28 seed regions was approximately 60 min for a 5 min scan. The

wSCA analysis was performed in real-time (with online motion correction and spatial

normalization of the MNI atlas into subject space) with time delay of less than a TR after image

reconstruction. The major time delay during online processing was due to the image reconstruction

on the scanner itself as described in [54]. During offline wSCA analysis, the processing time per

image volume (using spatial normalization by mapping the individual images into MNI space) was

120 ms and the total processing time for analyzing 28 seed regions was 15 min.

48

Fig. 4.3C. Intra-network and extra-network connectivity measured with conventional SCA

and windowed SCA in the 6 RSNs shown in Fig. 4.3A as a function of sliding-window width

(4 s, 8 s, 15 s, and 30 s) and scan duration (30 s, 60 s, 120 s, and 180 s). The color bar shows

the Z-scores of correlation coefficients (for cSCA) and meta-mean correlations (for wSCA)

ranging from -0.25 (blue) to +0.25 (red).

49

Fig. 4.4A. Subject-wise comparison of artifactual connectivity with conventional SCA and windowed SCA using white matter seeds. a) Artifactual connectivity in the raw data, b) conventional SCA with regression (cSCA), and (c-f) windowed SCA with sliding-window width of: c) 30 s, d) 15 s, e) 8 s, and f) 4 s. The maps are in neurological orientation at zero meta-mean correlation threshold with slice locations given by Z-coordinates. The color bar shows the Z-scores of correlation coefficients and meta-mean correlations ranging from -1 (blue) to +1 (red).

50

4.5.2. Intra- and Inter-RSN Connectivity

The resulting correlation maps (cSCA) and meta-mean maps (wSCA) were displayed at zero-

correlation threshold to show the apparent connectivity in the entire brain for the 6 classes of RSNs.

The example of a representative subject shown in Fig. 4.3A for the cSCA maps displays a decrease

in the apparent connectivity with scan duration particularly in the case of ATN, FRN, and SMN

and consistent confound suppression across the scan length in the case of AUN, DMN, and VSN.

The wSCA meta-mean maps in Fig. 4.3A (with 30 s, 15 s, 8 s, and 4 s sliding-windows) display:

improving localization of the RSNs with decreasing non-specific connectivity in cortical regions,

and increasing degrees of confound suppression in WM and CSF, as functions of increasing scan

durations and decreasing sliding-window widths. While the confound tolerance increases with

decreasing sliding-window width, the correlation coefficients decrease due to the rejection of more

Fig. 4.4B. Comparison of the average positive connectivity and the average negative connectivity

across 140 brain regions in the five subjects shown in Fig. 4.4A as measured by the conventional

SCA and the windowed SCA using the four WM seeds as a function of sliding-window width

(4 s, 8 s, 15 s, and 30 s).

51

strongly connected low frequency components, as expected. The results show that the connectivity

in most of the RSNs stabilizes within a minute. Although the connectivity dynamics are subject-

and RSN- specific, the overall connectivity patterns detected using wSCA are consistent across

subjects, showing robust confound tolerance at scan durations as short as 30 s. The wSCA

measured overall widely distributed bilateral connectivity among RSNs, in contrast to cSCA,

particularly in AUN and SMN. The spatial extent of connectivity is larger in the case of longer

sliding-windows and decreases with shorter windows as a function of scan duration, as predicted

by the simulations.

The overall positive and negative connectivity among the six RSNs is shown in Fig. 4.3B across

5 subjects using cSCA and wSCA as a function of sliding-window width and scan duration.

Conventional SCA showed a consistent decrease in both positive and negative connectivity as a

function of scan duration whereas wSCA showed a slight increase in positive connectivity in the

case of 4 s, 8 s and 15 s windows and a stronger increase in the case of 30 s window. The negative

connectivity decreased as a function of scan duration for the shorter windows (4 s, 8 s, 15 s) and

increased for the 30 s window, consistent with higher confound suppression for shorter sliding-

window widths.

Fig. 4.3C shows that the overall intra-network connectivity across 5 subjects is higher with wSCA,

particularly in AUN. VSN showed stronger connectivity using both methods whereas ATN showed

lower connectivity with cSCA relative to wSCA. The extra-network connectivity in SMN is higher

with cSCA and more negative in the AUN compared with wSCA. Conventional SCA did not show

significant differences as a function of scan duration among networks whereas wSCA showed an

increase in the connectivity as a function of scan duration.

4.5.3. Artifactual White Matter Connectivity

The degree of artifactual connectivity in the data (without using regression or sliding-window

meta-statistics) is shown in Fig. 4.4Aa as a reference for the confound suppression due to the

wSCA and cSCA. Fig. 4.4Ab demonstrates considerable suppression of background connectivity

in the cSCA correlation maps with the exception of S5. Figs. 4.4A c, d, e, and f show that the

confound suppression with wSCA increases with decreasing sliding-window width as predicted

by simulations. The spatial extent of the connectivity in the case of cSCA is constrained to the seed

52

region itself, whereas wSCA results in a slightly larger spatial extent. The same spatial smoothing

was used for both approaches. Interestingly, wSCA shows much stronger confound suppression in

the case of S5 compared to cSCA.

Fig. 4.4B shows overall lower positive and negative artifactual connectivity for cSCA across the

140 brain regions among five subjects compared to wSCA. The Z-scores of the positive

connectivity of wSCA were stronger than those of cSCA, even at the shortest sliding-window

width, and increased with increasing sliding-window width (4 s: 39%, 8 s: 53%, 15 s: 63%, 30 s:

71%). The Z-scores of the negative connectivity of the wSCA were stronger than those of cSCA,

yet only weakly dependent on sliding-window width (4 s: 39%, 8 s: 33%, 15 s: 37%, 30 s: 30%).

4.6. Whole-brain Connectivity Profiles

The connectivity strength across 140 brain regions was averaged across the 6 RSNs defined above

and quantified as a function of the sliding-window width (1 s, 2 s, 4 s, 6 s, 8 s, 15 s, 30 s, 60 s, 90

s, and 120 s). The resulting connectivity profiles were averaged across 5 subjects. The network-

wise sliding-window dependency was computed by Fisher Z-transforming the meta-mean

correlation across the 140 brain regions as a function of sliding-window width with RSN* seeds

(Table 4.1). The strength of the connectivity profiles for six single-voxel seeds (RSN* seeds)

across 70 regions in the left hemisphere (Fig. 4.5a) and 70 regions in the right hemisphere (Fig.

4.5b), across 5 subjects show distinct patterns of connectivity across certain regions. The strength

increases with increasing sliding-window width. However, some regions show negative

connectivity at short window widths. The increase in connectivity strength with increasing window

width is particularly pronounced in subcortical regions at the expense of temporal resolution and

degree of confound rejection. The average connectivity in the right hemisphere was stronger than

in the left hemisphere, particularly in BAs 35-41 and the subcortical regions, a finding that was

consistent across three out of the five subjects. The slope of this increase depends on the choice of

the RSN* seed. Shorter sliding-windows provide higher temporal resolution for monitoring

dynamic changes in high frequency connectivity (0.1-0.5 Hz), but decrease the Fisher Z-score,

53

which is dominated by low frequency connectivity (< 0.1 Hz). Fig. 4.5c also displays distinct

differences in global connectivity for different seed regions. The ATN*, AUN* and FRN* seeds

display stronger connectivity even at the shortest window widths, suggesting a higher global

spectral power density between 0.1 to 0.5 Hz for these seed regions.

Fig. 4.5. Connectivity profiles across 140 brain regions as a function of the sliding-window

width. The meta-mean correlation coefficients averaged across six RSNs* and five subjects are

plotted for window widths of 1 s, 2 s, 4 s, 8 s, 15 s, 30 s, 60 s, 90 s, and 120 s across (a) the 70

left; and (b) the 70 right-hemispheric regions. The numbers 1-70 correspond to the brain regions

listed in Table. 4.2. (c) Z-scores of meta-mean correlation coefficients averaged across 140

brain regions as a function of sliding-window width (1-120 s).

54

4.7. Physiological Noise Suppression

A moving average temporal low-pass filter was employed to suppress the physiological noise due

to cardiac and respiratory pulsations [63]. Previous studies [63,64] have demonstrated that a 2 s

window can reduce the effective variance in the hemodynamic baseline signal, resulting in

considerable reduction of the physiological noise, without the need for external physiological

monitoring and signal deconvolution. Suppression of physiological noise was quantified for

window widths of 0 s, 1 s, 2 s, 4 s, 6 s, and 8 s using symmetric and asymmetric Hamming filters

with filter windows of 25%, 50%, 75%, and 100% of the data vector, to attenuate the ringing and

Fig. 4.6. Temporal moving average filter. Suppression of cardiovascular and respiratory

pulsations in the frequency spectra of the time courses of the six RSNs* seeds as a function of

the moving average low-pass filter width in a single-subject: (a) no filter, (b) 2 s, (c) 4 s, and

(d) 8 s. The arrow shows the magnified section of the spectrum. Sliding-window width: 15 s,

symmetric Hamming filter: 50% data vector. Maximum amplitude: 120 (arbitrary units).

55

overshoot effects of the boxcar filter. A Fourier transform of the single-region seed time courses

was performed and the amplitude of the noise components in the spectrum was quantified as a

function of the filter window. The combination of the moving average time domain low-pass filter

of a frequency analysis of the time courses obtained from the above RSN* seeds without temporal

filter (0 s) (Fig. 4.6) shows the presence of strong cardiac and respiratory peaks in attention and

auditory networks at 0.5 Hz and 1 Hz, respectively. The spectrum with 1 s filter also contained and

the width of the sliding-window (high-pass filter) corresponds to band-pass filter. The suppression

of these pulsations is strongly enhanced by increasing the filter width to 2 s, resulting in a ~53%

and ~40% drop in the amplitude of the cardiac and respiratory peaks in the attention and auditory

networks, respectively. The longer filter widths of 4 s, 6 s, and 8 s show a smaller increase in

suppression with the cardiac respiratory peaks being close to the noise level. A 2 s temporal

averaging window is thus chosen as a compromise, to increase sensitivity and maintain temporal

resolution, while and attenuating the cardiac and respiratory pulsations.

4.8. Mapping of Resting-state Networks

Twenty-eight RSNs that were previously reported in a spatial group-ICA study [59] were mapped

in individual subjects using wSCA with 15 s sliding-window and 2 s temporal averaging filter. The

coordinates of the 28 single-voxel seed locations were identical to the maxima reported in Fig. 4A

of [59]. The resulting meta-mean maps were displayed at a threshold of 0.6. Windowed SCA

enabled mapping of 28 RSNs in single subjects using a 15 s sliding-window. In each of the 7

classes of RSNs, it was possible to map a subset of 6 RSNs in real-time with scans as short as 1

min. The majority of the RSN patterns shown in Fig. 4.7 using the most informative axial meta-

mean threshold maps in a representative single-subject (displayed at a threshold of 0.6), have

features that are very similar to the RSNs reported in [59], including ATN - IC34, IC52, IC72;

DMN - IC25, IC50, IC53, IC68; FRN - IC20, IC42, IC49; SMN - IC23, IC24, IC29; and VSN -

IC48, IC67. The other RSNs (ATN - IC55, IC60; SMN - IC38, IC56; and VSN - IC39, IC46, IC64)

show more widely distributed connectivity patterns that extend into the contralateral hemisphere,

compared with the ICA derived RSNs. Few RSNs (e.g., ATN - IC71, SMN - IC074, VSN - IC59)

show less extensive connectivity patterns compared to the ICA derived RSNs.

56

Fig. 4.7. Twenty-eight RSN patterns obtained with windowed SCA in a representative single-

subject. The meta-mean correlation maps from seven major classes of networks (ATN, AUN,

BGN, DMN, FRN, SMN, and VSN) using 28 single-voxel seeds, displayed using the most

informative axial slice locations (Z-coordinates) in neurological orientation for comparison with

Fig. 4A of the group-ICA study by [59]. The meta-mean maps are threshold at 0.6.

57

4.9. Windowed Seed-based Connectivity Analysis

Unambiguous quantification of resting-state signal changes and the characterization of spatio-

temporal fluctuations of RSNs in single subjects has serious challenges. Resting-state fMRI signal

changes in BOLD contrast fMRI are approximately an order of magnitude smaller than the

conventional task related signal changes (e.g., in sensorimotor, visual, and attention regions),

making the analysis of resting-state connectivity highly sensitive to head movement and

physiological noise sources [18,68-73]. It has also been shown that physiological noise increases

with magnetic field strength, which limits sensitivity gains in resting-state fMRI [21-24,74,75].

This requires deconvolution during post-processing [45], which has proven challenging in part due

to aliasing of cardiac pulsations in conventional fMRI acquisition methods. There has been

increasing evidence that conventional approaches to movement correction during post-processing

are incomplete due to the convolution of rigid body movement, geometrical image distortions,

regional image intensity changes, and spin history effects. This, combined with the fact that the

micro-movements can impact the connectivity in a systematic way has led to the use of data

scrubbing [41-44,76]. The current wSCA methodology addresses some of the limitations of

existing approaches.

4.9.1. Confound Suppression

This study extends the wSCA methodology [49] by quantifying the confound suppression and the

strength of the measured resting-state connectivity as a function of fluctuation frequency and

sliding-window width in comparison with regression-based cSCA, both in computer simulations

and in vivo [1]. The simulations of confound suppression using wSCA, with single and multiple

instances of the confounding signals in one or both nodes of the RSN, demonstrate a comparable

degree of confound suppression for all simulated confound waveforms. Since the confound

suppression of wSCA was shown to be similar for combinations of these waveforms, it is expected

to extend to arbitrary waveforms. As a function of the sliding-window width, wSCA provides a

selectable trade-off between the degrees of confound suppression and the frequency bandwidth for

mapping resting-state fluctuations. This frequency selectivity enabled sensitive detection of

intrinsic differences in the fluctuation frequency spectra among RSNs. The 15 s sliding-window

was selected to capture the minimum resting-state fluctuation frequency (on the order of fmin ~

58

0.06 Hz) in accordance with the rule of thumb described in [77], window width (W) ≥ 1/fmin.

However, we were also able to show the detection of functional connectivity at much shorter

window widths at correspondingly reduced sensitivities, as discussed in [78]. With respect to

cSCA, the wSCA methodology demonstrates comparable tolerance to confounds such as head

movement, physiological noise, unrelated signal changes in WM and CSF, and high sensitivity for

detecting RSNs. Windowed SCA is particularly well suited for the high temporal resolution due to

the sensitivity gains offered by high-speed fMRI acquisition methods. High-speed fMRI enables

sampling of a wider resting-state frequency spectrum beyond the 0.25 Hz that is detected in

conventional EPI studies with a TR of 2 s, thus improving the mapping of intra- and inter-network

connectivity (e.g. [79]). The high sensitivity and temporal resolution of our MEVI approach

enables mapping of 28 resting-state networks (RSNs) in single subjects that were previously

described in [59] over 600 subjects.

4.9.2. Frequency selectivity of Windowed SCA

Windowed SCA performs both high-pass filtering of the resting-state frequency spectrum and de-

correlates the effects of spurious signal changes across the time domain. The range of sliding-

window widths used in this study (2-30 s) restricts resting-state fluctuations to frequencies between

0.03-0.5 Hz, which carry the majority of temporal information for segregating RSNs. While

decreasing the sliding-window width increases the temporal resolution, which is desirable for

characterizing short-term fluctuations in resting-state connectivity, it also curtails the sensitivity

for detecting RSNs in a network dependent manner. The degree to which the sliding-window

suppresses the effect of the confounding signals depends on the frequency spectrum of the RSN

under consideration, as shown in the simulations. Networks such as ATN, AUN, SMN, and VSN

exhibit considerable high frequency connectivity (0.3-0.5 Hz), detected using sliding-windows as

short as 1 - 2 s. These networks also exhibit smaller spatio-temporal fluctuations across subjects

relative to the other networks. Although shorter sliding-windows show stronger confound

suppression at the expense of connectivity dynamics at lower frequencies, window widths in the

range between 8-15 s provide a reasonable compromise for mapping the minimum resting-state

fluctuation frequency (~0.06 Hz), as discussed above. The addition of the temporal moving

59

average filters (2 - 4 s) provides a high frequency cut-off (0.5 -0.25 Hz) to reduce the effects of

cardiac pulsatility, resulting in band-pass filter characteristics and enhancing the detection

sensitivity [63,64].

4.9.3. Conventional and Windowed SCA

Regression is highly effective in reducing spurious connectivity with white matter seed regions,

constraining the connectivity patterns to the seed voxel itself (Fig. 4.4A). It is well known that

regression analysis is performed under the assumption that the regressors and the underlying

resting-state signal fluctuation time courses are orthogonal (e.g. [80,81]. Collinearity between the

regressors and the resting-state signal fluctuations, therefore, results in a decrease of the measured

connectivity. Our data show that the signal fluctuations in auditory cortex are partly correlated

with WM seed time courses, resulting in strong suppression after regression. Regression of the

global mean, employed in conventional seed-based methods to improve the specificity of the

activations and effective artifact removal [31,32], has been a topic of intense debate

[25,28,29,33,34,37-39]. Global signal regression may not only introduce anti-correlations [27,36],

but also bias the positive correlations and has been shown to correlate with partial pressure

variation effects of carbon dioxide [30,82]. In contrast, wSCA preserves connectivity in the

auditory cortex and provides a higher level of positive connectivity in most networks. It also

appears to be more consistent in suppressing confounds in the case of strong anti-correlations

between different white matter regions (S5 in Fig. 4.4A). These intrinsic differences in confound

suppression contribute to distinct dependencies of positive connectivity (Fig. 4.3B, 4.4B) on the

scan duration, particularly in the auditory and sensorimotor cortices (Fig. 4.3C).

4.9.4. Real-time implementation of Windowed SCA

Conventional SCA with regression of the confounding signals requires cumulative computation of

the regressors, which is computationally intensive for real-time implementation and may suffer

possible bias due to the confound regression across the growing time series. A number of alternate

approaches have recently been explored for seed-based [83-87] and ICA-based real-time analyses

[88]. The wSCA methodology targeted at single-subject analysis, builds on our earlier

developments of the real-time analysis methods [60,61,65]. The computational efficiency of the

60

wSCA approach makes it suitable for mapping functional connectivity during an ongoing scan.

This is desirable not only for characterizing the elusive and fluctuating ‘brain states’ [86,87,89]

along with designing novel neurofeedback experiments, but also for assessing the data quality,

particularly in clinical applications. Additionally, this approach is suitable to measure changes in

brain states (e.g. drowsiness) and state of attention for applications in neuroscience and clinical

research studies in real-time [84-87] which cannot be monitored adequately with the existing

approaches. In clinical settings, wSCA will enable real-time RSN mapping to characterize regional

alterations in network connectivity patterns in patients with brain diseases, e.g., tumors, epilepsy

and arteriovenous malformations. Further developments of this methodology may enable

controlled self-regulation of functional connectivity in RSNs using real-time neurofeedback,

which would complement existing approaches for neurofeedback of task-based activation studies

[90-94].

4.9.5. Limitations

The degree of confound suppression achieved with wSCA is limited by the frequency selectivity,

which is determined by the sliding-window width. A high degree of confound suppression using

shorter windows thus sacrifices sensitivity for detecting very low frequency connectivity. A

correction of the correlation thresholds for the degrees of freedom as a function of sliding-window

width has not been applied in the present study and is under investigation. We expect that the

increase in threshold with decreasing sliding-window width resulting from this correction will

decrease artifactual connectivity, further enhancing the confound tolerance of wSCA, and decrease

the extent of true-positive connectivity, reducing the intra- and inter-RSN connectivity strength

measured with wSCA in comparison to cSCA. Neither scrubbing nor despiking were used in this

study, which might differentially affect the confound suppression of conventional and windowed

SCA approaches. Adding motion covariates to the correlation analysis, along with scrubbing and

despiking will be investigated in a future study. The low resolution partial brain images acquired

in this study limit the precision of spatial normalization and the delineation of seed regions in gray

matter, WM, and CSF. Like any seed-based approach, wSCA is highly sensitive to the choice of

the seed locations, therefore, seeds were carefully chosen to avoid the edges of the MEVI slabs,

where respiratory artifacts primarily manifest.

61

References

1. Vakamudi, K., Ackley, E. S., Trapp, C. W., Damaraju, E., Calhoun, V. D., Posse, S, 2016. Confound-Tolerant Seed-Based Sliding-Window Connectivity Analysis for Real-Time High-Speed Resting-State fMRI, NeuroImage, under review March 2016.

2. Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi: 10.1002/mrm.1910340409.

3. Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., Stam, S. J., Smith, S. M., Beckmann, C. F., 2006. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. USA 2006; 103:13848-13853.

4. De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M., Smith, S. M., 2006. FMRI resting-state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 2006; 29:1359-1367.

5. Lowe, J. Mark 2012. The emergence of doing “nothing” as a viable paradigm design. NeuroImage 62 1146-1151. doi: 10.1016/j.neuroimage.2012.01.014.

6. Power, J. D., Fair, D. A., Schlaggar, B. L.., Petersen, S. E., 2010. The Development of human functional networks. Neuron67, September 9, 2010. doi: 10.1016/j.neuron.2010.08.017.

7. Van den Heuvel, M. P., and Hulshoff Pol, H. E., 2010. Exploring the brain network: A review on the resting-state fMRI functional connectivity. European Neu.psy.pharma 2010, 519-534.

8. Calhoun, V. D., Miller, R., Pearlson, G., and Adali, T., 2014. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, vol. 84, pp. 262-274, Oct 22 2014, PMC4372723.

9. Chang, C., Glover, G. H., 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50, 81-98. doi: 10.1016/j.neuroimage.2009.12.011.

10. Sakoglu, U., Pearlson, G. D., Kiehl, K. A., Wang, Y., Michael, A., and Calhoun, V. D., 2010. A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA 23, 351-366. doi: 10.1007/s10334-010-0197-8.

11. Van Dijk, K. R A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., and Buckner, R. L., 2010. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophyiol. 103, 297-321. doi:10.1152/jn.00783.2009.

12. Beckmann, C. F., DeLuca, M., Devlin J. T et al, 2005. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005; 360:1001-13.

13. Bell, A. J., Sejnowski, T. J., 1997. The independent components of natural scenes are edge filters. Vision Res., Vol 37, No. 23, pp 3327-3338.

14. Braun, U., Plichta, M. M., Esslinger, C., Sauer, C., Haddad, L., et al. 2012. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage 59, 1404-1412. doi: 10.1016/j.neuroimage.2011.08.044.

15. Calhoun, V. D., Adalı, T., Pearlson, G. D., and Pekar, J. J., 2001. A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis. Human Brain Mapping, vol. 14, pp. 140-151, 2001.

16. Friston, K. J., Kahan, J., Biswal, B., Razi, A., 2014. A DCM for resting-state fMRI. NeuroImage 94 396-407. http://dx.doi.org/10.1016/j.neuroimage.2013.12.009.

17. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T., Kindermann, S. S., Bell, A. J., Sejnowski,

http://dx.doi.org/10.1016/j.neuroimage.2013.12.009

62

T. J., 1998. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6:160-188.

18. Satterthwaite, T. D., Elliott, M, A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. D., Hakonarson, H., Gur, R. C., Gur, R. E., Wolf, D. H., 2013. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage 64, 240-256.

19. Van Dijk, K. R. A., Sabuncu, M. R., Buckner, R. L., 2012. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59 431-438. doi: 10.1016/ j. neuroimage.2011.07.044.

20. Lowe, M. J., Mock, B. J., and Sorenson, J. A., 1998. Functional connectivity in single and multislice echo-planar imaging using resting-state fluctuations. NeuroImage 7, 119-132. Article no. NI 970315.

21. Triantafyllou, C., Hoge, D. R., Krueger, G., Wiggins, J. C., Potthast, A., Wiggins, C. G., and Wald, L. L., 2005a. Comparison of physiological noise at 1.5 T, 3 T, and 7 T and optimization of fMRI acquisition parameters. NeuroImage 26, 243-250.

22. Triantafyllou, C., Hoge, D. R., Krueger, G., Wiggins, J. C., Potthast, A., Wiggins, C.G., and Wald, L. L., 2005b. Comparison of physiological noise at 1.5 T, 3 T, and 7 T and optimization of fMRI acquisition parameters. NeuroImage 26, 243-250.

23. Triantafyllou, C., Hoge, R. D., and Wald L.L., 2006. Effect of spatial smoothing on physiological noise in high-resolution fMRI. NeuroImage 32, 551-557. doi: 10.1016/ j. neuroimage.2006.04.182.

24. Triantafyllou, C., Polimeni, J. R., Wald, L. L., 2011. Physiological noise and signal-to-noise ratio in fMRI with multichannel array coils. NeuroImage 55, 597-606. doi: 10.1016/j.neuroimage.2010.11.084.


26. Greve, D. N., Brown, G. G., Mueller, B. A., Glover, G., Liu, T. T., 2013. A survey of the sources of noise in fMRI. Psychometrika – vol 78, No. 3, 396-416. doi: 10.1007/s11336-012-9294-0.

27. Anderson, J. S., Druzgal, T. J., Lopez-Larson, M., Jeong, E. K., Desai, K., Yurgelun-Todd, D., 2011. Network Anti-correlations, Global Regression, and Phase-shifted Soft Tissue Correction (PSTCor). Hum Brain Mapp. 2011 June; 32(6): 919-934. doi:10.1002/hbm.21079.

28. Carbonell, F., Bellec, P., Shmuel, A., 2011. Global and System-specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks. Brain Connectivity, Volume 1, Number 6, 2011. doi:10.1089/brain.2011.0065.

29. Chai, X. J., Castanon, A. N., Ongur, D., Whifield-Gabriell, S., 2012. Anti-correlations in resting-state networks without global signal regression. Neuroimage. 2012 January 16; 59(2): 1420-1428 doi: 10.1016/j.neuroimage.2011.08.048.

30. Chang, C., Glover, G. H., 2010. Effects of model-based physiological noise correction default-mode network anti-correlations and correlations. NeuroImage 2009; 47:1448-59.

31. Fox, M. D., Zhang, D., Snyder, A. Z., and Raichle, M. E., 2009. The global signal and observed anti-correlated resting-state brain networks. J. Neurophysiol. 101, 3270-3283. doi:10.1152/jn.90777.2008.

32. Gavrilescu, M., Shaw, M. E., Stuart, G. W., Eckersley, P., Svalbe, I. D., and Egan, G. F., 2001. Simulation of the effects of global normalization procedures in functional MRI. NeuroImage 17, 532-542. doi:10.1006/nimg.2002.1226.

63

33. Hayasaka, S., 2013. Functional connectivity networks with and without global signal correction. Front. Hum. Neurosci. doi: 10.3389/fnhumsys.2013.00880.

34. He, H., and Liu, T. T., 2012. A geometric view of global signal confounds in resting-state functional MRI. NeuroImage 59, 2339-2348. doi:10.1016/j.neuroimage.2011.09.018.

35. Hahamy-Dubossarsky, A., Calhoun, V. D., Pearlson, G., Harel, M., Stern, N., and Attar, F. 2014. Save the global: global signal connectivity as a tool for studying clinical populations with fMRI. Journal of Molecular Neuroscience, vol. 53, pp. S59-S60, Aug 2014.

36. Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., and Bendettini, P. A., 2009. The impact of global signal regression on resting-state correlations: are anti-correlated networks introduced? NeuroImage 44, 893-905. doi: 10.1016/j.neuroimage. 2008.09.036.

37. Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., Cox, R. W., 2012. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity. doi: 10.1089/brain.2012.0080.

38. Uddin, L. Q., Clare Kelly, A. M., Biswal, B. B., Castellanos, F. X., Milham, M. P., 2009. Functional connectivity of default-mode network components: correlation, anti-correlation, and causality. Hum. Brain Mapp. February; 30(2). doi: 10.1002/hbm.20531.

39. Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., Windischberger, C., 2009. Correlations and anti-correlations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies. NeuroImage 2009; 1408-1416. doi: 10.1016/j.neuroimage.2009.05.005.

40. Patel, A., Kundu, P., Rubinov, M., Jones, S. P., et al, 2014. A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. doi: 10.1016/j.neuroimage.2014.03.012.

41. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlagger, B. L., Petersen, S. E., 2014. Methods to detect, characterize, and remove motion artifact in resting-state fMRI. NeuroImage 84 320-341. http://dx.doi.org/10.1016/j.neuroimage.2013.08.048.

42. Power, J. D., Schlaggar, B. L., Petersen, S. E., 2014. Recent progress and outstanding issues in motion correction in resting-state fMRI. doi: 10.1016/j.neuroimage.2014.10.044.

43. Power, J. D., Schlaggar, B. L., Petersen, S. E., 2014. Studying brain organization via spontaneous fMRI signal. NeuronPrimer. http://dx.doi.org/10.1016/j.neuron.2014.09.007.

44. Xu, Y., Tong, Y., Liu, S., Chow, M. H., Sabur, A., et al, 2014. Denoising the speaking brain: Toward a robust technique for correcting artifact-contaminated fMRI data under severe motion. doi: 10.1016/j.neuroimage.2014.09.013.

45. Beall, E. B., and Lowe, M. J. (2010). The non-separability of physiological noise in functional connectivity MRI with spatial ICA at 3 T. Journal of Neuroscience Methods 191, 263-276. doi: 10.1016/j.neumeth.2010.06.024.

46. Damaraju, E., Allen, E., Calhoun, V. D., 2014. Impact of head motion on ICA derived functional connectivity measures. Biennial Conference on Resting-state / Brain Connectivity Boston, MA, 2014.

47. Liston, A. D., Lund, T. E., Salek-Haddadi, A., Hamandi, K., Friston, K. J., and Lemieux, L. 2006. Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies. NeuroImage 30, 827–834. doi: 10.1016/j.neuroimage.2005.10.025.

48. Murphy, K., Birn, R. M., Bandettini, P. A., 2013. Resting-state fMRI confounds and cleanup. NeuroImage 80 (2013) 349-359. doi: 10.1016/j.neuroimage.2013.04.001.

49. Posse, S., Ackley, E., Mutihac, R., Zhang, T., Hummatov, R., Akhtari, M., Chohan, M., Fisch,

http://dx.doi.org/10.1016/j.neuron.2014.09.007

64

B., and Yonas, H., 2013. High-speed real-time fMRI using multi-slab echo-volumar imaging. Front. Hum. Neurosci. 7:479. doi:10.3389/fnhum.2013.00479.

50. Welford, B. P., 1962. Note on a method for calculating corrected sums of squares and products. Technometrics 4, 419-420. doi: 10.1080/00401706. 1962.10490022.

51. Cohen, Mark. S., 2001. Real-time functional magnetic resonance imaging. Methods 25, 201-220. doi:10.1006/meth.2001.1235.

52. Cox, R. W., Jesmanowicz, A., Hyde, J. S., 1995. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 1995; 33:230-236.

53. Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., et al, 2010. Multiplexed echo-planar imaging for sub-second whole-brain fMRI and fast diffusion imaging. PLoS ONE 5(12): e 15710. doi: 10.1371/journal.pone.0015710.

54. Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., Murray-Krezan, C., et al, 2012. Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage 61, 115-130. doi: 10.1016/j.neuroimage.2012.02.059.

55. Moeller, S., Yacoub, E., Olman, C. A., Auerback, E., Strupp, J., Harel, N., Ugurbil, K., 2010. Multiband Multislice GE-EPI at 7 Tesla, with 16-Fold Accelearation Using Partial Parallel Imaging with Applications to High Spatial and Temporal Whole-Brain FMRI. Magn Reso Med. 2010 May; 63(5); 1144-1153. doi:10.1002/mrm.22361.

56. Setsompop, K., Gagoski, B. A., Polimeni, J. R., Witzel, T., Wedeen, V. J., and Wald, L. L. 2012. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo-planar imaging with reduced g-factor penalty. Magn Reson Med, vol. 67, pp. 1210-1224, May 2012, 3323676.

57. Zahneisen, B., Grotz, T., Lee, K. J., Ohlendorf, S., Reisert, M., Zaistev, M., Henning, J., 2011. Three-dimensional MR-encephalography: fast volumetric brain imaging using rosette trajectories. Magn Reson Med. 2011 May;65(5):1260-8. doi: 10.1002/mrm.22711.

58. Mansfield, P., Harvey, P. R., Stehling, M. K., 1994. Echo-volumar imaging. Mag. Res materials in physics, Biology and Medicine 1994 Volume 2, Issue 3, pp 291-294.

59. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner. W., Segall, J. M., Silva, R. F., Calhoun, V. D., et al, 2011. A baseline for the multivariate comparison of resting-state networks. Front. Syst. Neurosci. 5:2. doi: 10.3389/fnsys.2011.00002.

60. Gembris, D., Taylor, G. J., Schor, S., Frings, W., Suter, D., and Posse, S., 2000. Functional Magnetic Resonance Imaging in Real-Time (FIRE): Sliding-Window Correlation Analysis and Reference Vector Optimization. Mag. Reson. Med. 43:259-268.

61. Mathiak, K., Posse, S., 2003. Evaluation of Motion and Realignment for Functional Magnetic Resonance Imaging in Real-time, Magnetic Resonance in Medicine 45(1):167-171.

62. Gao, K., Posse, S. 2003. TurboFire: Real-Time fMRI with Automated Spatial Normalization and Talairach Daemon Database, Hum. Brain. Mapp, NeuroImage 19 (2), 838, 2003.

63. Lin, F. H., Nummenmaa, A., Witzel, T., Polimeni, J. R., Zeffiro, T. A., Wang, F. N., et al, 2011. Physiological noise reduction using volumetric functional magnetic resonance inverse imaging. Hum. Brain. Mapp. 33, 2815-2830. doi:10.1002/hbm.21403.

64. Kruggel, F., von Cramon, D. Y., and Descombes, X., 1999. Comparison of Filtering Methods for fMRI Datasets. NeuroImage 10, 530-543.

65. Gao, K., and Posse, S., 2004. TurboFIRE: Real-time fMRI with online generation of reference vectors. OHBM, Budapest: Organization for Human Brain Mapping, WE177.

66. Lancaster J. L, Rainey L. H, Summerlin J. L, Freitas C. S, Fox P. T, Evans A. C, Toga A. W,

65

Mazziotta J. C., 1997. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp 5, 238-242, 1997. - http://www.talairach.org/.

67. Lancaster J. L, Woldorff M. G, Parsons L. M, Liotti M, Freitas C. S, Rainey L, Kochunov P. V, Nickerson D, Mikiten S. A, Fox P. T., 2000. Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping 10:120-131, 2000.

68. Barry, R. L., Williams, J. M., Klassen, M. L., Gallivan, J. P., Culham, J. C., Menon, R. S., 2010. Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI. Magnetic Resonance Imaging 28, 235-244.

69. Desjardins, E. A., Kiehl, K. A., and Liddle, P. F., 2000. Removal of confounding effects of global signal in functional MRI analyses. NeuroImage, 13, 751-758 (2001) doi: 10.1006/nimg.2000.0719.

70. Lee, D., Jang, C., and Park, H., 2015. Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification. NeuroImage 108, 203-213.

71. Macey, P. M., Macey, K. E., Kumar, R., Harper, R. M., 2003. A method for removal of global effects from fMRI time series. NeuroImage 22, 360-366.

72. Shaw, M. E., Strother, S. C., Gavrilescu, M., Podzebenko, K., Waites, A., Watson, J., Anderson, J., Jackson, G., and Egan, G., 2003. Evaluating subject-specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics. NeuroImage 19 (2003) 988-1001 doi: 10.1016/S1053-8119 (03)00116-2.

73. Tanabe, J., Miller, D., Tregellas, J., Freedman, R., and Meyer, F. G., 2001. Comparison of detrending methods for optimal fMRI preprocessing. NeuroImage 15, 902-907 (2002). doi:10.1006/nimg.2002.1053.

74. Churchill, N. W., Yourganov, G., Spring, R., Rasmussen, P. M., Lee, W., Ween, J. E., Strother, S. C., 2012. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI. NeuroImage 59 (2012) 1299-1314.

75. Hutton, C., Josephs, O., Stadler, J., Featherstone, E., Reid, A., Speck, A., Bernarding, J., Weiskopf, N., 2011. The impact of physiological noise correction on fMRI at 7 T. NeuroImage 57, 101-112. doi: 10.1016/j.neuroimage.2011.04.018.

76. Muschelli, J., Nebel, M. B., Caffo, B. S., Barber, A. D., Pekar, J. J., Mostofsky, S. H., 2014. Reduction of motion-related artifacts in resting-state fMRI using aCompCor. NeuroImage 96, 22-35. http://dx.doi.org/10.1016/jneuroimage.2014.03.028.

77. Leonardi, N., and Van De Ville, D., 2015. On spurious and real fluctuations of dynamic functional connectivity at rest. NeuroImage 104 (2015) 430-436. doi:10.1016/j.neuroimage.2014.09.007.

78. Zalesky, A., and Breakspear, M., 2015. Towards a statistical test for functional connectivity dynamics. NeuroImage 114, 466-470. doi:10.1016/j.neuroimage.2015.03.047.

79. Kalcher, K., Boubela, R. N., Huf, W., Bartova, L, Kronnerwetter, C et al, 2014. The spectral diversity of the resting-state fluctuation in the human brain. PLoS ONE 9(4): e93375. doi:10.1371/journal.pone.0093375.

80. Monti, M. M. 2011. Statistical analysis of fMRI time series: a critical review of the GLM approach. Front. Hum. Neuro. 5(28). doi: 10.3389/fnhum.2011.00028.

81. Mumford, J. A., Poline, J., Poldrack, R. A., 2015. Orthogonalization of Regressors in fMRI Models. PLoS ONE, 10(4). doi: 10.1371/journal.pone.0126255.

82. Chang, C., Glover, G. H., 2009. Relationship between respiration, end-tidal CO2, and BOLD

66

signals in resting-state fMRI. NeuroImage. 2009b; 47:1381–1393. 83. Koush, Y., Rosa, M. J., Robineau, F., Heinen, K., Rieger, S. W., Weiskopf, N., Vuilleumier, P.,

Van De Ville, D., Scharnowski, F., 2013. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. NeuroImage 81, 422-430. http://dx.doi.org/10.1016/j. neuroimage.2013.05.010.

84. Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P., Van De Ville, D., 2011. Decoding brain states from fMRI connectivity graphs. NeuroImage 56, 616-626. doi: 10.1016/j.neuroimage.2010.05.081.

85. Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N., Sitaram, R., 2014. Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks. Biological Psychology 95, 4-20. http://dx.doi.org/10.1016/j.biopsycho.2013.04.010.

86. Sitaram, R., Lee, S., Ruiz, S., & Birbaumer, N. 2011a. Real-time regulation and detection of brain states from fMRI signals. In R. Coben, & J. Evans (Eds.), Neurofeedback and neuromodulation techniques and applications (pp. 227–253). London: Elsevier Inc.

87. Sitaram, R., Lee, S., Ruiz, S., Rana, M., Veit, R., & Birbaumer, N. 2011b. Real-time support vector classification and feedback of multiple emotional brain states. NeuroImage, 56, 753–765.

88. Soldati, N., Calhoun, V. D., Bruzzone, L., Jovicich, J., 2013. The use of a priori information in ICA-based techniques for real-time fMRI: an evaluation of statics/dynamic and spatial/temporal characteristics. Front. Hum. Neurosci., March 2013. doi: 10.3389/fnhum.2013.00064.

89. Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., Calhoun, V. D., 2014. Tracking the whole-brain connectivity dynamics in the resting-state. Cerebral cortex, November 11, 2014. doi:10.1093/cercor/bhs352.

90. De Charms, C. R., Christoff, K., Glover, G. H., Pauly, M. J., Whitfield, S., Gabrieli, D. E. J., 2003. Learned regulation of spatially localized brain activation using real-time fMRI. NeuroImage 21, 436-443. doi: 10.1016/j.neuroimage.2003.08.041.

91. Haller, S., Kopel, R., Jhooti, P., Haas, T., Scharnowski, F., Lovbald, K., Scheffler, K., Van De Ville, D., 2013. Dynamic reconfiguration of human brain functional networks through neurofeedback. NeuroImage 81. http://dx.doi.org/10.1016/j.neuroimage.2013.05.019.

92. Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., et al, 2013. Real-time fMRI neurofeedback: Progress and Challenges; NeuroImage 76, 86-399. http://dx.doi.org/10.1016/j.neuroimage.2013.03.033.

93. Weiskopf, N., Sitaram, R., Josephs, O., Veit, R., Scharnowski, F., et al, 2007. Real-time functional magnetic resonance imaging: methods and applications. Mag. Reso. Imaging 25, 989-1003. doi: 10.1016/j.mri.2007.02.007.

94. Weiskopf, Nikolaus, 2012. Real-time fMRI and its applications to neurofeedback. NeuroImage 62, 682-692. doi: 10.1016/j.neuroimage.2011.10.009.

http://dx.doi.org/10.1016/j.biopsycho.2013.04.010

67

Chapter 5 Functional Connectomics

5.1. Introduction

Although resting-state fMRI is being increasingly used to map the functional connectomics,

variability inherent in the choice of the data analysis methods (e.g., SCA, ICA, graph theory) and

processing parameters, and subject-specific seed selection have limited reliability [1,2]. This can

impair the comparison across studies. Efforts of parcellating RSN connectivity in single subjects

have sought to overcome the shortcomings of spatial normalization and variability due to

structured noise sources by using multiscale clustering, dictionary based learning and related

approaches [3-7]. Standardization of seed selection and brain parcellation are of considerable

interest for increasing the clinical utility of resting-state fMRI [8,9].

This chapter presents the framework in which wSCA is implemented offline to map whole-brain

inter-regional connectivity (IRC) across 140 Brodmann and subcortical areas and inter-network

connectivity (INC) among 28 RSNs using single- and multi-region seeds. IRC and INC enable

comparison of sensitivity and specificity of mapping resting-state connectomics in single subjects

and across a group of subjects.

5.2. Statistical Hierarchy

Multi-level statistics implemented in the wSCA were shown in Fig. 5.1. The hierarchy consists of

three levels of statistics: single-voxel, region/single-subject, and subject group, resulting in

temporal, spatial, and group statistics respectively. The mean and SD of the Pearson’s correlation

coefficients [ρt] were calculated for each voxel at every TR (t – temporal), resulting in single-voxel

level meta-statistics (meta-mean [μt], meta-SD [σt]) which correspond to the temporal

information. Spatial means and SDs of the [μt] and [σt] were computed across brain regions using

segmentation based on a modified Talairach Daemon Database [10,11] that encompasses 140

bilateral brain regions from the Talairach atlas in the left and right hemispheres (see Table 4.2),

resulting in the region/single-subject level meta-statistics ([μ1s], [σ

1s], [μ2

s ], and [σ2s]) which

correspond to spatial information (s – spatial). The [μ2s ] maps were a measure of temporal

fluctuations of correlations within each brain region. The group information was obtained by

computing the mean and SD across subjects ([μ1g], [σ

1g], [μ2

g], and [σ2g]) (g – group). The [μ1

g]

68

corresponds to the strength of the correlations across the subjects and the [σ1g] measures the inter-

subject variability of the meta-mean correlations within each brain region. The [μ2g] corresponds

to the average temporal fluctuations within each brain region across subjects and the [σ2g] measures

the variability in temporal fluctuations in each brain region across subjects.

Fig. 5.1. Statistical hierarchy. Computation of cumulative meta-statistics (running mean and

SD) on Pearson’s correlations [ρt] for every voxel at each TR (temporal statistics) results in

meta-mean [μt] and meta-SD [σt] maps at the end of the scan. The red dotted squares depict

two positions of a moving sliding-window of width ‘w’. The spatial mean and SD across the

voxels within the functional brain regions (spatial statistics) yields [μ1s]/[σ

1s] and

[μ2s ]/[σ

2s] maps. The mean and SD across subjects (group statistics) provides group level maps

[μ1g]/[σ

1g] and [μ2

g]/[σ2g]. Notation: μ-principal mean, σ-principal SD; subscripts: 1-derived

mean, 2-derived SD; superscripts: t-temporal, s-spatial, and g-group.

69

5.3. Seed Selection

Single-region seeds Single-region seeds were selected as described in chapter 4 (4.3.1).

Multi-region seed clusters The 28 multi-region seed clusters (multiple voxels in multiple

regions) were derived from the 100 nodes of the 28 RSNs described in Allen et al., 2011 [12].

Spatially distributed voxels corresponding to the nodes of each of the above mentioned RSNs were

linked together to form a compound seed cluster consisting of 1-7 voxels. The motivation for

choosing these multi-region seed clusters was to encompass all the nodes of a given network to

probe the total spatial extent of connectivity and to investigate the interplay of the nodes for

mapping whole-brain connectivity. The MNI coordinates of all 100 nodes can be found in [12].

5.4. Resting-state Networks with Multi-region Seeds

The 28 resting-state networks were mapped using multi-region seeds, similar to the single-region

seeds as described in chapter 4 (see 4.8). In this chapter, the 28 wSCA generated RSNs using both

the single- and multi-region seeds were compared with the 28 RSN patterns shown in Fig. 4A of

the group-ICA study [12]. As a result of the optimization of processing parameters, a 15 s sliding-

window and a 2 s moving average temporal low-pass filter were selected, resulting in an RSN

frequency bandwidth of 0.06-0.5 Hz. Representative axial meta-mean ([μt]) correlation maps in a

single-subject are shown in Fig. 5.2. The thresholded meta-mean correlation maps display [μt]

values in the range of 0.6 to 1.0. The single-region seeds (ATN - IC55, IC60; SMN - IC38, IC56;

and VSN - IC39, IC46, IC64) generate more widely distributed connectivity patterns that extend

into the contralateral hemisphere, compared with the ICA derived RSNs. The majority of the

connectivity patterns generated by the multi-region seeds (ATN – IC52, IC55, IC72; AUN17,

DMN – IC25, IC68; FRN - IC42, IC47; SMN - IC23, IC24, IC38, IC56; and VSN - IC39, IC46,

IC64) show spatially extensive ipsi- and contralateral connectivity patterns that extend across

multiple functional brain regions in contrast to the single-region seed connectivity patterns. Some

of the multi-region seeds (ATN - IC34; BGN - IC21; DMN - IC43, IC50; FRN - IC20; SMN -

IC29; and VSN - IC67) generate connectivity patterns that are similar to the ICA derived RSNs. A

few single- and multi-region seeds (e.g., ATN - IC71, SMN - IC074, VSN - IC59) generate less

extensive connectivity patterns compared to the ICA derived RSNs.

70

5.5. Inter-Regional Connectivity (IRC)

The meta-statistics ([μt], [σt]) in single subjects were computed for the 28 single- and 28 multi-

region seeds and the resulting whole-brain meta-statistics maps were segmented into 140 brain

regions in standardized MNI space, in reference to the Talairach Daemon database [10,11]

comprising both the left and right hemispheres. The [μt], and [σt] were then parsed using custom

PERL scripts, resulting in region-wise meta-statistics for each seed region in single subjects ([μ1s],

[σ1s], [μ2

s ], and [σ2s]). The group level region-wise meta-statistics were obtained by averaging

across the subjects ([μ1g], [σ

1g], [μ2

g], and [σ2g]). IRC matrices (IRCMs) at the single-subject and

Fig. 5.2. Representative RSN patterns in a single-subject. The meta-mean correlation maps ([μt])

from seven major classes of networks (ATN, AUN, BGN, DMN, FRN, SMN, and VSN) using

a) 28 single- and b) 28 multi-region seeds, displayed using the most informative slice locations

(Z-coordinates) in neurological orientation for comparison with Fig. 4A of the group-ICA study

by Allen et al., 2011. The maps are thresholded at 0.6.

71

group level, which plot the region-wise meta-statistics across 28 single-, and 28 multi-region seeds

and 140 functional brain regions, were generated using custom MATLAB scripts. The following

IRCMs were computed separately for left and right hemispheres to quantify inter-hemispheric

differences: (a) the meta-mean correlation coefficients were plotted as a measure of functional

connectivity among the atlas-based functional brain regions at the single-subject ([μ1s]) and the

group levels ([μ1g]). (b) The meta-SDs were plotted as a measure of temporal fluctuations among

the atlas-based functional brain regions both at the single-subject ([σ1s]) and the group levels ([σ

1g]).

(c) The SDs of the meta-mean correlation coefficients ([μ2g] and the SD of meta-SDs [σ

2g] across

subjects were plotted as a measure of inter-subject variability of functional connectivity and inter-

subject variability of temporal fluctuations within each functional brain region. Global

connectivity metrics for groups of seed regions that correspond to six of the seven major classes

of RSN seeds were computed by averaging across all 140 brain regions for each of these IRCMs.

5.5.1. Single-subject Connectomics The functional connectivity ([μ1

s]) IRCMs in single subjects exhibit widely distributed seed region-

specific connectivity patterns across left cortical and subcortical brain regions as shown in the

example in Fig. 5.3A. Single-region seeds are associated with stronger inter-regional differences

in connectivity, both positive and negative (Fig. 5.3A, a), compared with multi-region seeds (Fig.

5.3A, b). Negative [μ1s] correlations are predominantly seen across the subcortical regions (Fig.

5.3A, a), particularly with FRN and SMN seeds and are also measured in a small number of cortical

regions, e.g., the strongest negative [μ1s] correlation between the DMN seed (DMN - IC25) and

BA33 in this subject is -0.55 (Fig. 5.3A, a). The multi-region seeds (Fig. 5.3A, b) show much

smaller negative [μ1s] correlations (the strongest in this subject being -0.24 in Fig. 5.3A, b) and

stronger positive connectivity that is more uniform across brain regions compared with single-

region seeds. Similar connectivity characteristics are observed in the right brain regions for single-

and multi-region seeds.

72

Fig. 5.3. Single-subject connectomics – Functional connectivity and temporal fluctuation inter-

regional connectivity matrices (IRCMs). (A) Functional connectivity ([μ1s]) IRCM among 28 seed

regions and 70 left-hemispheric atlas-based brain regions for: (a) single-region seeds; and (b)

multi-region seeds. (B) Temporal fluctuation ([σ1s]) IRCM among 28 seed regions and 70 left-

hemispheric atlas-based brain regions for: (a) single-region seeds; and (b) multi-region seeds. (C)

Maximum negative- (anti) and positive- [μ1s] correlations for each subject (S1-S8) and the group

average (Avg) across the whole-brain for single- (light and dark blue bars) and multi-region

(orange and green bars) seeds, respectively. (D) Minimum and maximum [σ1s] for each subject

(S1-S8) and the group average (Avg) for single- (light and dark blue bars) and multi-region

(orange and green bars) seeds, respectively. The error bars in both the maps indicate the standard

errors in the [μ1s] and the [σ

1s].

73

The single-subject temporal fluctuation ([σ1s]) IRMCs (Fig. 5.3B) display particularly strong

fluctuations for subsets of the ATN, DMN, FRN, SMN and VSN seeds. Single-region seeds (Fig.

5.3B, a), display stronger fluctuations across left brain regions compared with multi-region seeds

(Fig. 5.3B, b), in particular for the ATN and DMN seeds. The fluctuations in BAs 3-7 for multi-

region seeds are among the smallest across all brain regions (< 0.25 on average in Fig. 5.3B, b),

whereas fluctuations in the subcortical regions are considerably stronger (on the order of 0.45 for

ATN seeds in Fig. 5.3B, b). Overall, BAs 3-7, 20, 38, 39 and dentate show the smallest temporal

fluctuations with both seed types. The global connectivity metrics shown in Fig. 5.3C emphasize

the consistency of the positive [μ1s] correlations across subjects for both single- and multi-region

seeds. The negative [μ1s] correlations are more variable across subjects, in particular for multi-

region seeds. The range of positive [μ1s] correlations in single subjects is from 0.71 to 0.93 for the

left hemisphere, 0.78 to 0.92 for the right hemisphere in the case of single-region seeds, and

between 0.72 and 0.91 for the left hemisphere, and 0.73 to 0.92 for the right hemisphere in the case

of multi-region seeds. Negative [μ1s] correlations in single subjects are in the range from -0.57 to -

0.01 for the left hemisphere and from -0.66 to -0.24 for the right hemisphere in the case of single-

region seeds, and in the range of -0.69 to 0.03 for the left hemisphere and from -0.65 to -0.11 for

temporal fluctuations ([σ1s]) are more consistent across the subjects (range: 0.4 to 0.55) than the

[μ1s] correlations. The [σ

1s] in the case of single-region seeds ranged from a minimum of 0.02 (left

hemisphere) and 0.01 (right hemisphere), to a maximum of 0.53 (left hemisphere) and 0.54 (right

hemisphere). For multi-region seeds the [σ1s] range from a minimum of 0.02 (left and right

hemispheres) to a maximum of 0.52 (left hemisphere) and 0.55 (right hemisphere).

5.5.2. Group Connectomics a) The group functional connectivity IRCM ([μ

1

g]) in Fig. 5.4 (only left-hemispheric regions

shown) displays higher contrast between regions and on average lower mean compared with

the single-subject IRCMs (Fig. 5.3), both for single- (Fig. 5.4a) and for multi-region (Fig. 5.4b)

74

Fig. 5.4. Group averaged connectomics - Functional connectivity inter-regional connectivity

matrices (IRCMs). [μ1g] correlations among 28 seed regions and 70 left-hemispheric atlas-

based brain regions for: (a) single-region seeds, and (b) multi-region seeds. (c) Group

averaged global (140 regions) functional connectivity (Z-scores of [μ1g] correlations) of 6

major classes of single- and multi-region RSN seeds (ATN, AUN, DMN, FRN, SMN, and

VSN). The error bars represent the standard error in the [μ1g] correlations.

75

Fig. 5.6. Inter-subject variability of connectomics – Spatial inter-regional connectivity

matrices (IRCMs). [μ2g] correlation among 28 seed regions and 70 left-hemispheric atlas-based

brain regions: (a) single-region seeds, and (b) multi-region seeds. (c) Inter-subject variability

in global (140 regions) functional network connectivity (Z-scores of the [μ2g] correlation) of 6


VSN). The error bars represent the standard error in the [μ2g] correlations.

76

Fig. 5.5. Group averaged connectomics - Temporal fluctuation inter-regional connectivity

matrices (IRCMs). [σ1g] among 28 seed regions and 70 left-hemispheric atlas-based brain

regions for: (a) single-region seeds, and (b) multi-region seeds. (c) Group averaged global

(140 regions) temporal fluctuations in functional connectivity (Z-scores of meta-SD) of 6


VSN). The error bars represent the standard error in the [σ1g].

77

seeds. Single-region seeds show mostly low positive [μ1g] correlations (0-0.1) across the

majority of regions, particularly across the BAs 20-47 and subcortical regions. The maximum

negative [μ1g] correlation seen with single-region seeds is -0.03 for the left hemisphere and -

0.05 for the right hemisphere. BGN, DMN, and FRN seeds are the least positively correlated

throughout the brain whereas the subsets of other RSNs (ATN, AUN, SMN, and VSN) are

strongly correlated with their associated brain regions (e.g., SMN seeds are highly correlated

with BAs 01-07). The maximum positive [μ1g] correlations seen with single-region seeds are

0.69 for the left hemisphere and 0.75 for the right hemisphere.

The multi-region seeds (Fig. 5.4b), not only show strong connectivity with the regions that are

associated with the networks, but also extend over a larger number of regions. The ATN, AUN,

SMN, and VSN seeds show strong [μ1g] correlations with most of the 140 regions, whereas the

DMN and BGN seeds show smaller [μ1g] correlations. The maximum negative [μ1

g] correlations

seen with multi-region seeds are -0.03 for the left hemisphere and -0.01 for the right

hemisphere. The maximum positive [μ1g] correlations are 0.60 for the left hemisphere, 0.63 for

the right hemisphere. Similar connectivity characteristics are seen in right hemispheric brain

regions for both seeds. The group global connectivity statistics across the whole-brain and

subjects presented in Fig. 5.4c show that the multi-region seed clusters have 26% overall higher

connectivity strength compared to the single-region seeds. The average global [μ1g] correlation

across the networks, with single- and multi-region seeds is 0.23 and 0.29, respectively.

b) The temporal fluctuation IRCMs ([σ1g]) show that the single-region seeds (Fig. 5.5a) exhibit

slightly higher temporal fluctuations compared to the multi-region seeds (Fig. 5.5b). The [σ1g]

correlations vary from a minimum of 0.17/0.18 to a maximum of 0.43/0.40 in the case of

single/multi-region seeds, respectively. Apart from the slightly higher [σ1g] correlations seen in

the case of single-region seeds, the global connectivity patterns are similar between the single-

and multi-region seeds: the fluctuations in the frontal, visual, and posterior cingulate (PCC)

cortices are higher compared to the auditory, and sensorimotor cortices; BAs 20, 28, 35, 36, 38

and dentate show meta-SD of the order of 0.25. The global [σ1g] temporal fluctuations (Fig.

5.5c) are higher for ATN, FRN, and SMN with single-region seeds compared to the multi-

region seeds. The average global [σ1g] temporal fluctuations with single/multi-region seeds are

78

0.33/0.32, respectively, across the networks. Similar results are seen in the case of right brain

IRCMs.

c) The inter-subject variability of functional connectivity ([μ2g]) displayed in Fig. 5.6 for single-

(Fig. 5.6a), and multi-region seeds (Fig. 5.6b) show that the ATN, AUN, DMN, FRN and SMN

seeds exhibit a small spatial variability in most cortical areas, in particular in BAs 01-32, in

contrast to the BAs 33-35 and 43-44, for both types of seeds. The subcortical regions, including

amygdala, dentate, lateral dorsal nucleus, midline nucleus, red nucleus, display particularly

high variability across the subjects. The inter-subject variability graph in Fig. 5.6c shows that,

except for the AUN seed, all the other seeds show significant global variability. The global

inter-subject variability is higher for the AUN, DMN, SMN and VSN seeds compared to the

ATN and FRN seeds. The average global inter-subject variability with single/multi-region

seeds is 0.16/0.17 respectively, across the whole-brain.

5.5.3. Spatio-temporal Stability of Inter-Regional Connectivity A dual threshold technique was applied at the group level meta-statistics using functional

connectivity, temporal fluctuation and inter-subject variability IRCMs for both seed types using

custom MATLAB scripts. A spatial stability map was obtained by applying the first threshold on

the [μ1g] IRCM (group averaged functional connectivity) and by applying a second threshold, based

on the [μ2g] IRCM (inter-subject variability of functional connectivity). Similarly, a temporal

stability map was obtained by applying the first threshold to the [σ1g] IRCM (group averaged

temporal fluctuations) and by applying a second threshold based on the [σ2g] IRCM (inter-subject

variability of temporal fluctuations). Upper and lower thresholds were determined by histogram

analysis of each of the IRCMs at a 20 % cut-off level, resulting in four combinations of dual

thresholds: (i) high-mean, high-SD, (ii) high-mean, low-SD, (iii) low-mean, high-SD, and (iv) low-

mean, low-SD. The same thresholds were applied to temporal fluctuation maps. Among these

combinations of thresholds, maps (ii) and (iii) were of particular interest, as they identify strong

and stable connectivity, and weak and unstable connectivity, respectively. The percentages of

functional brain regions passing the dual threshold were calculated to quantify the whole-brain

spatio-temporal stability.

79

The spatial stability of the functional brain regions across subjects (Fig. 5.7A) shows that the

single-region seed IRCM (Fig. 5.7A, a) exhibits a smaller number of spatially stable regions

compared to the multi-region seeds (Fig. 5.7A, c) for a given threshold. The BAs 01-07 are among

the most stable regions identified by the selected thresholds for both seed types (Fig. 5.7Aa, c)

apart from the subsets of the VSN, DMN, and ATN seeds, particularly in the case of multi-region

seeds. Connectivity in subcortical regions is less stable compared to the cortical regions, as seen

using single-region seeds (Fig. 5.7A, b). Multi-region seeds show a smaller number of unstable

regions both in cortical and subcortical regions (Fig. 5.7A, d).

A similar analysis on the temporal stability IRCMs (Fig. 5.7B) shows that BA08, 09 show the

lowest temporal stability (SD of 0.4) with FRN, SMN, and VSN single-region seeds (Fig. 5.7B, a)

compared to smaller number of unstable regions with multi-region seeds (Fig. 5.7B, c). Also,

considerable number of functional regions beyond BA19 show higher temporal stability (SD of

0.3) (Fig. 5.7 B b, d), across higher Brodmann areas (BA20-47) and dentate which shows strikingly

significant fluctuations across all the RSN seeds, with both the seed types. The percentage of

functional regions passing a given combination of the dual threshold is listed in Table 5.1 for both

spatial and temporal stability maps. More number of regions show spatio-temporal stability with

multi-region seeds comparison to the single-region seeds.

Regions passing the dual threshold (%) Single-region seeds Multi-region seeds

Spatial stability (Fig 5.6A)

Stable (a) 2.10 Stable (c) 4.03 Unstable (b) 4.13 Unstable (d) 0.30

Temporal stability (Fig 5.6B)

Unstable (a) 3.16 Unstable (c) 1.27 Stable (b) 7.09 Stable (d) 4.23

Table 5.1. The percentage of functional brain regions passing the dual threshold in the spatio-

temporal stability maps in Fig. 5.7.

80

Fig. 5.7. Group averaged connectomics – Spatio-temporal stability inter-regional connectivity matrices (IRCMs) of left brain regions obtained with a dual threshold technique based on the [μ1

g]/[μ2g] (spatial) and [σ

1g]/[σ

2g] (temporal). The lower and upper thresholds of these meta-

statistics are selected at a 20% cut-off determined from a histogram analysis. (A) Thresholded spatial stability IRCMs for (a) regions that show higher spatial stability and (b) regions that show lower spatial stability using single-region seeds; (c) regions that show higher spatial stability and (d) regions that show lower spatial stability using multi-region seeds. (B) Thresholded temporal stability IRCMs for (a) regions that show lower temporal stability and (b) regions that show higher temporal stability using single-region seeds; (c) regions that show lower temporal stability and (d) regions that show higher temporal stability using multi-region seeds. The percentage of the functional left brain regions passing the dual threshold for each of the above maps are shown in Table 5.1.

81

5.6. Inter-Network Connectivity (INC)

Single-subject meta-maps for single-region seeds were masked individually with each of the 28

single-region seeds and spatially averaged within the mask to compute a seed region averaged [μ1s]

INC using TurboFIRE and custom MATLAB scripts. The same procedure was implemented for

multi-region seed maps. In addition to the single-subject INC, the group [μ1g] was also computed

to compare the resulting wSCA based connectivity map with the ICA-based connectivity map. The

seed region averaged meta-means were arranged in the form of a color-coded inter- network

connectivity matrix (INCM), similar to the functional network connectivity matrix (FNCM)

presented as Fig. 4B in the ICA-based group study [12]. Global metrics of group averaged INC

and inter-subject variability in INC were computed.

Inter-network connectivity matrices (Fig. 5.8) for: single-subject single-region seeds – [μ1s] (Fig.

5.8 a), single-subject multi-region seeds – [μ1s] (Fig. 5.8b), group average single-region seeds –

[μ1g] (Fig. 5.8c), and group average multi-region seeds – [μ1

g] (Fig. 5.8d) display seed vs. seed

connectivity compared to the seed vs. brain region connectivity (IRCMs) described in previous

sections. The group averaged single-region seed INCM (Fig. 5.8c) is found to have the most similar

connectivity patterns to the FNCM presented in the group-ICA study [12].

The strongest correlations (blocks of high connectivity across the principal diagonal) are seen

among the intra-RSNs (sub-networks that belong the same major class), both in the single subjects

and in group results, a characteristic feature also observed in the FNCM presented in the group-

ICA study. Single-subject INCMs (Fig. 5.8a, b) display stronger anti-correlations across DMN and

VSN seeds than the group INCM (Fig. 5.8c, d), in both seed analyses. The group averaged meta-

statistics (Fig. 5.8c, d) show predominantly positive correlations. The SMN seed shows strong

positive correlations with the ATN seed and weaker correlations with the VSN seed, in both single-,

and multi-region seed analyses. The DMN seed is less strongly correlated with the rest of task

positive RSN seeds, as expected. Fig. 5.8e shows that multi-region seeds have higher global

connectivity strength and lower inter-subject variability relative to the single-region seeds.

82

Fig. 5.8. Single-subject and group averaged inter-network connectivity matrices (INCMs).

RSN seed vs RSN seed [μ1s] correlation maps of the 28 networks listed in Table 4.1 for (a)

single-subject single-region seeds ([μ1s]); (b) single-subject multi-region seeds ([μ1

s]); (c) group

averaged single-region seeds ([μ1g]); (d) group averaged multi-region seeds ([μ1

g]). (e) Group

averaged global functional connectivity ([μ1g]) and inter-subject variability ([μ2

g]) of INC. The

error bars represent the standard errors of the [μ1g] and [μ2

g].

83

5.7. Atlas-based Functional Connectomics

The wSCA approach developed in this study takes advantage of the enhanced sensitivity offered

by high-speed fMRI acquisition to map RSNs across 140 functional regions in the entire brain,

which provides an unparalleled richness of information about the spatio-temporal dynamics of

intra- and inter-network connectivity in single subjects to create a connectome fingerprint (see

Chapter 6). Quantitative mapping of inter-subject variability in functional connectivity across

groups of subjects using the dual threshold technique based on the group statistics at a given

threshold, reveals distinct domains of stable connectivity and enabled segmentation of the regions

that exhibit strong fluctuations. The IRCMs generated in this study (that mapped connectivity

among the RSN seeds and whole-brain), are complementary to the spatially sparser INCMs or

FNCMs, and illustrate the connectivity across the whole-brain. The INCMs display anti-

correlations in the connectivity between RSNs at the single-subject level without the use of global

signal regression (GSR), which is consistent with several recent studies [13,14]. The single-region

seed INCMs shown in Fig. 5.7a, c are symmetric (in the absence of post-correlation filters), since

the masking of the meta-map to generate the single-region seed INCM is equivalent to the

multiplication of identity matrices whose product is commutative. In contrast, the multi-region

seed INCMs Fig. 5.7b, d are not symmetric, since the masking of the meta-maps to generate the

multi-region seed INCMs have different spatial configurations in terms of voxel locations resulting

in the multiplication of two non-identity matrices whose product is not commutative, hence the

asymmetry.

The developed wSCA approach represents an initial implementation of a generalized framework

for mapping whole-brain connectivity in individual networks and in clusters of networks that

expands current SCA approaches. While the current wSCA employs equal weights for the nodes

of the individual networks, an expansion of this approach using a weighted combination of seed

region time courses will provide further flexibility for hypothesis-driven analysis of whole-brain

inter-regional connectivity dynamics. The extension of this approach to a higher number of

seeds/dimensions will enhance the parcellation of spatio-temporal features of RSN connectivity.

This high dimensional INCM approach lends itself in training a spatially aggregated classifier [15]

to characterize the spatio-temporal dynamics of functional networks [16]. When comparing the

84

INCMs with ICA-based FNCMs [12,17], the single-region seed group results show more similar

patterns of connectivity but with higher and more positive correlations and reduced contrast among

networks, which may reflect the greater segregation of RSNs of the ICA algorithm relative to SCA

and the much smaller number of subjects in the present study. ICA can perform spatial filtering by

separation of spatially overlapping components, and segregation of confounding signal changes

[18] resulting in high contrast FNCMs.

85

References

1. Cole, M. W., Anticevic, A., Repovs, G., and Barch, D., 2011. Variable global dysconnectivity and individual differences in Schizophrenia. Biol. Psychiatry 2011; 70:43–50. doi: 10.1016/j.biopsych.2011.

2. Joel, S. E., Caffo, B. S., van Zijl, P. C. M., Pekar, J. J., 2011. On the relationship between seed-based and ICA-based measures of functional connectivity. Mag. Reson. Med. 66:644-657 (2011).

3. Anderson, J. S., Druzgal, T. J., Lopez-Larson, M., Jeong, E. K., Desai, K., Yurgelun-Todd, D., 2011. Network Anti-correlations, Global Regression, and Phase-shifted Soft Tissue Correction (PSTCor). Hum Brain Mapp. 2011 June; 32(6): 919-934. doi:10.1002/hbm.21079.

4. Blumensath, T., Jbabdi, S., Glasser, M. F., Van Essen, D. C., Ugurbil, K., Behrens, T. E., and Smith, S. M., 2013. Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. NeuroImage, vol. 76, pp. 313-324, 2013, 3758955.

5. Cohen, A. L., Fair, D. A., Dosenbach, N. U., Miezin, F. M., Dierker, D., Van Essen, D. C., Schlaggar, B. L., and Petersen, S. E., 2008. Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage, vol. 41, pp. 45-57, May 15 2008, 2705206.

6. Li, K., Langley, J., Li, Z., and Hu, X. P., 2014. Connectomic Profiles for Individualized Resting-state Networks and Regions of Interest. Brain Connect, Sep 25 2014.

7. Zhang, Y., Caspers, S., Fan, L., Fan, Y., Song, M., Liu, C., Mo, Y., C. Roski, Eickhoff, S., Amunts, K., and Jiang, T., 2014. Robust brain parcellation using sparse representation on resting-state fMRI. Brain Struct Funct, Aug 26 2014.

8. Castellanos, F. X., Martino, A. D., Craddock, R. C., Mehta, A. D., Milham, M. P., 2013. Clinical applications of the functional connectome. 80:527–540. doi: 10.1016/j.neuroimage.2013.04.083.

9. Shimony, J. S., Zhang, D., Johnston, J. M., Fox, M. D., Roy, A., Leuthardt, E. C 2009. Resting-state Spontaneous Fluctuations in Brain Activity: A New Paradigm for Pre-surgical Planning Using fMRI. Acad. Radiology, 16: 578–583. doi: 10.1016/j.acra.2009.02.001.

10. Lancaster J. L, Rainey L. H, Summerlin J. L, Freitas C. S, Fox P. T, Evans A. C, Toga A. W, Mazziotta J. C., 1997. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp 5, 238-242, 1997. - http://www.talairach.org/).

11. Lancaster J. L, Woldorff M. G, Parsons L. M, Liotti M, Freitas C. S, Rainey L, Kochunov P. V, Nickerson D, Mikiten S. A, Fox P. T., 2000. Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping 10:120-131, 2000.


86

13. Chai, X. J., Castanon, A. N., Ongur, D., Whifield-Gabriell, S., 2012. Anti-correlations in resting-state networks without global signal regression. Neuroimage. 2012 January 16; 59(2): 1420-1428 doi: 10.1016/j.neuroimage.2011.08.048.


15. Zheng, W., Ackley, E. S., Martinez-Ramon, M., and Posse, S., 2012. Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas. Magn. Reson. Imaging 31, 247-261. doi: 101016/j.mri.2012.07.010.

16. Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., Jirsa, V. K., 2015. Functional connectivity dynamics: Modeling the switching behavior of the resting-state. NeuroImage 105 (2015) 525-535. http://dx.doi.org/10.1016/j.neuroimage.2014.11.001.

17. Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., Calhoun, V. D., 2012. Tracking the whole-brain connectivity dynamics in the resting-state. Cerebral cortex, November 11, 2012. doi:10.1093/cercor/bhs352.

18. Calhoun, V. D., Adali, T., Pekar, J. J., 2004. A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks. Mag. Res. Imaging 22 (2004) 1181-1191 doi: 10.1016/j.mri.2004.09.004.

87

Chapter 6 High Dimensional Connectomics

6.1. Introduction

Functional connectomics using resting-state fMRI (rsfMRI) is a rapidly expanding task-free

approach which has significant clinical impact. There is a growing need for seed-based

connectivity mapping tools with improved tolerance to confounding signal changes that enable

whole-brain connectomics in single subjects. One of the ways to achieve this is via an unbiased,

automated seed selection method in combination with wSCA. This chapter presents a novel atlas-

based connectomics study to map the connectivity across the entire brain in a single-subject. This

is an extension of the inter-network connectivity maps discussed in Chapter 5, with no a priori

knowledge of seed locations or correlation thresholds. Functional connectivity across the 140

bilateral brain regions from the Talairach Daemon database were computed using the wSCA

approach and presented as a connectome fingerprint.

6.2. Materials and Methods

High-speed resting-state echo-volumar imaging data (TR/TE 136/35 ms, 5 min scan, Siemens 3T

scanner) was collected in a single-subject with informed consent [1]. Data were analyzed in

TurboFIRE v5.14.5.1 [2]. Preprocessing steps included motion correction, spatial normalization

into MNI atlas and mapping into Talairach Daemon database, spatial smoothing using 8 mm3

Gaussian filter, and a 4 s temporal moving average low-pass filter to suppress the physiological

noise [3]. The entire functional brain region is selected as an ROI from Talairach Daemon database

[4]. Seed time courses were extracted from each of the voxels in the region and non-thresholded

correlation statistics were computed with all 24,682 voxels in the brain. These statistics were then

averaged across the brain region over the time of the scan. A 30 s sliding-window was used coupled

with meta-statistics [5] for effective confound tolerance [6]. The meta-statistics were plotted using

PERL scripts and MATLAB toolboxes to represent the underlying functional connectivity

signature in the resting brain. The connectivity is then mapped across the hemispheres using

Talairach Daemon database (Table 4.2) and subsequently segregated into 12 major functional

systems.

88

Fig. 6.1a. The high dimensional, single-subject connectome fingerprint between 70 left

hemisphere and whole-brain.

89

Fig. 6.1b. The high dimensional single-subject connectome fingerprint between 70 right

hemisphere and whole-brain.

90

Fig. 6.2. The abridged functional connectivity map with brain regions segregated according to functional networks. Motor cortex – BA01-07; Frontal cortex – BA13,14; Visual cortex – BA17 -19; Attention areas – BA20-22; Wernicke’s area – BA39, 40; Auditory cortex – BA41, 42; Broca’s area – BA44,45; Caudate – Caudate Body (CB), Caudate Head (CH), Caudate Tail (CT); Central brain regions – Anterior Commissure (AC), Anterior Nucleus (AN), Corpus Callosum (CC), Dendate, Hypothalamus, Lateral Dorsal Nucleus (LDN), Lateral Geniculum Body (LGB), Lateral Globus Pallidus (LGP), Lateral Posterior Nucleus (LPN), Mammillary Body (MB), Medial Dorsal Nucleus (MDN), Medial Geniculum Body (MGB), Medial Globus Pallidus (MGP), Midline Nucleus (MN), Putamen, Pulvinar, Red Nucleus (RN), Substantia Nigra, Subthalamic Nucleus (SN), Ventral Anterior Nucleus (VAN), Ventral Lateral Nucleus (VL), Ventral Posterior Lateral Nucleus (VP), Ventral Posterior Medial Nucleus (VPM). The numbers 1-47 represents the corresponding Brodmann areas BA01-47. Meta mean correlation scale: -0.3 to +0.9.

91

The whole-brain connectome fingerprint, based on meta-mean correlations across the two

hemispheres in a single-subject, is shown in Fig. 6.1. a & b. As each seed ROI (brain region)

consists of extended functional brain regions, the resulting connectivity matrix was not symmetric

and had a non-unitary diagonal. Using the motor, frontal, and visual cortices as seed regions, the

strength of the connectivity is maximum across the central brain regions. Anti-correlations were

observed between the auditory cortex and the rest of the brain. Functional connectivity patterns

were similar across hemispheres, but left hemisphere showed higher anti-correlations across the

auditory cortex relative to the right hemisphere. The whole-brain connectivity fingerprint is

abridged to represent the 12 major functional systems across the 140 brain regions (Fig. 6.2). The

insular and visual cortices showed lower connectivity strength, whereas amygdala and

hippocampus showed higher connectivity strength. In general, the central brain regions showed

overall higher connectivity strength across the 12 systems.

6.3. Whole-brain Connectivity Fingerprints

This method facilitates quantification of the functional connectomics at whole-brain level which

does not require a priori knowledge of seed locations. These connectome fingerprints can be

extended from functional brain region level (140×140 matrix) to single-voxel level (e.g.,

24,682×24,682 matrix). The major time delay during online processing is due to the image

reconstruction on the scanner itself, particularly at high temporal resolutions, therefore, such high

dimensional mapping during an ongoing scan requires enormous computational power and

accumulates significant delay in real-time processing. These higher dimensional connectomics

have potential applications in mapping the global information processing by providing a

visualization tool for probing connectivity dynamics. The method enables mapping of anti-

correlations in single subjects without global signal regression, as shown in a previous study [7].

Using pattern classification algorithms [8] in real-time, this method can segregate different

functional connectivity states known in healthy brain and disease related abnormalities, improving

individualized diagnosis in patients with neurological and psychiatric disorders. We are currently

investigating novel brain parcellation [9] and individualized seed optimization methods to improve

the computational efficiency of wSCA for generating the connectome fingerprints.

92

References

1. Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., Murray-Krezan, C., et al. (2012). Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. Neuroimage 61, 115-130. doi: 10.1016/j.neuroimage.2012.02.059.

2. Gembris, D., Taylor, G. J., Schor, S., Frings, W., Suter, D., and Posse, S (2000). Functional Magnetic Resonance Imaging in Real-Time (FIRE): Sliding-Window Correlation Analysis and Reference Vector Optimization, Mag. Reson. Med. 43:259-268.

3. Lin, F. H., Nummenmaa, A., Witzel, T., Polimeni, J. R., Zeffiro, T. A., Wang, F. N., et al. (2011). Physiological noise reduction using volumetric functional magnetic resonance inverse imaging. Hum. Brain. Mapp. 33, 2815-2830. doi:10.1002/hbm.21403.

4. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT, "Automated Talairach Atlas labels for functional brain mapping". Human Brain Mapping 10:120-131, 2000.

5. Posse, S., Ackley, E., Mutihac, R., Zhang, T., Hummatov, R., Akhtari, M., Chohan, M., Fisch, B., and Yonas, H (2013). High-speed real-time fMRI using multi-slab echo-volumar imaging. Front. Hum. Neurosci. 7:479. doi:10.3389/fnhum.2013.00479.

6. Vakamudi, K., Ackley, E., Posse, S (2014). Resting-state connectivity dynamics in real-time fMRI using a novel seed-based approach, Proceedings of the 20th Annual Meeting of the OHBM. June 2014 Sub. No. 3916.

7. Chai, X. J., Castanon, A. N., Ongur, D., Whifield-Gabriell, S (2011). Anti-correlations in resting-state networks without global signal regression. Neuroimage. 2012 January 16; 59(2): 1420-1428. doi: 10.1016/j.neuroimage.2011.08.048.

8. Zheng, W., Ackley, E. S., Martinez-Ramon, M., and Posse, S (2013). Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas. Magn. Reson. Imaging 31, 247-261. doi: 10.1016/j.mri.2012.07.010.

9. Blumensath, T., Jbabdi, S., Glasser, M. F., Van Essen, D. C., Ugurbil, K., Behrens, T. E., and Smith, S. M. (2013). Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. Neuroimage, vol. 76, pp. 313-324, 2013, 3758955.

93

Chapter 7 Real-time Neurofeedback

7.1. Introduction

Self-regulation of functional networks using real-time fMRI has recently been demonstrated using

ROI-based approaches [1-3], however, neurofeedback based modulation of signal correlation

within and among the resting-state networks (RSNs) has not yet been studied. Seed-based

connectivity analysis (SCA) is suitable for real-time connectivity analysis, but it is sensitive to

head movement and confounding signal sources [4] as discussed in previous chapters.

Furthermore, it is desirable to use high-speed fMRI acquisition methods, which have been shown

to increase sensitivity for detecting resting-state connectivity [5,6]. This chapter presents a novel

whole-brain SCA approach to study the RSN connectivity dynamics in real-time using ultra-high-

speed fMRI in the context of neurofeedback. The process flow of the proposed neurofeedback

experiment is as shown in Fig. 7.1. The methodology enables sensitive mapping of connectivity

changes in RSNs at time scales as short as 10-15s.

Fig. 7.1. A comprehensive process flow for neurofeedback using TurboFIRE and its integration

with the scanner.

94

Fig. 7.2. Intra-, Inter-RSN connectivity dynamics displaying distinct connectivity patterns

characteristics of high positive connectivity (red circle), high negative connectivity (blue

circle) and combination of positive and negative connectivity (green circle), captured at

every 4 s of the scan.

95

7.2. Real-time Windowed SCA

Six ROIs were selected to display a 6x6 correlation coefficient matrix in real-time. Resting-state

data (eyes open) in 9 healthy controls were collected on a 3 T scanner using ultra-high-speed echo-

volumar imaging (TR/TE 136/35 ms, 5 min scan) [7]. Data were analyzed in TurboFIRE [8,9]

using a 2 s moving average temporal low-pass filter [10], spatial normalization, sliding-windows

(range: 1–120 s), and cumulative meta-statistics [7]. Real-time monitoring of intra- and inter-

network connectivity in 6 RSNs (attention ATN, auditory AUN, default-mode DMN, frontal FRN,

sensorimotor SMN and visual VSN) was performed using seeds from Allen et al., 2011 [11].

Analysis of 28 RSNs was performed offline. Inter-network NxN correlation matrices were

computed, where N is the number of ROIs being investigated.

7.3. Connectivity Dynamics of Resting-state Networks

The real-time wSCA revealed RSNs in time frames as short as 20-30 s using sliding-windows as

short as 4 s (Fig.7.2). Cumulative meta-statistics reduced spurious correlations in non-gray matter

areas to the noise level after approximately 90 s.

Fig. 7.3. a) The inter-RSN real-time meta-statistics among six major RSNs; b) the

corresponding connectivity states throughout the same scan, showing distinct dynamical

connectivity patterns.

96

7.4. Pattern Classification and Brain States

Mapping of RSN connectivity dynamics in real-time using a set of six seed regions enable intra-,

inter-RSN connectivity dynamics for networks of interest. An inter-network connectivity pattern

among six resting-state seed regions is shown in Fig. 7.3a. Such dynamically changing

connectivity patterns can be monitored throughout the ongoing scan in TurboFIRE, showing

distinct transient periods of hyper- and hypo-connectivity. Additionally, it shows periods of strong

anti-correlations between RSNs, which self-repeat over the time scales of minutes, as shown in

Fig.7.3b. The wSCA is shown to be a highly sensitive approach (Chapter 4) for mapping the RSN

connectivity dynamics in individual subjects in real-time. Generating intra- and inter-RSN

functional connectivity matrices during an ongoing resting-state fMRI scan enables sensitive

detection of network connectivity dynamics. Inter-and intra-RSN connectivity allows for

characterization of brain state dynamics, novel self-regulation [12,13], and neuro-feedback

methods [14].

7.5. Real-time Neurofeedback

We demonstrate the feasibility of real-time mapping of intra- and inter-network connectivity

dynamics at time scales of seconds using ultra-high-speed fMRI in an exploratory study. Six seed

regions of DMN were selected as ROIs and the resulting connectivity matrix was shown to the

subject on the scanner computer screen over regular intervals of time (4 s). This enabled the subject

to attempt regulation of the DMN by modulating their attention, as shown in Fig. 7.4. We

Fig. 7.4. Real-time neurofeedback circuit.

97

encountered a lag between the real-time streaming of the images from the scanner and the

computation of connectivity matrix in TurboFIRE, which increased with time. However, this

approach enables sensitive detection of FNC dynamics in individual subjects and may lead to novel

neurofeedback approaches, which are currently under investigation to mitigate the scanner

hardware and computational limitations. This approach is also suitable for monitoring data quality

in resting-state fMRI, e.g. to assess artifacts due to head motion, for which there are no currently

available tools.

98

References

1. Emmert, K., Breimhorst, M., BAuermann, T., Birklein, F., Van De Ville, D., Haller, S. (2014). Comparison of anterior vs. insular cortex as targets for real-time fMRI regulation during pain stimulation. Front. Beh. Neurosci. Vol 8, 350, doi: 10.3389/fnbeh.2014.00350.

2. Hinds, O., Thompson, T. W., Ghosh, S., Yoo, J. J., Whitfield-Gabrieli, S., Triantafyllou, C., et al., 2013. Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI. J Neurophysiol 109: 1250 –1258.

3. Shen, J., Zhang, G., Yao, L., Zhao, X. (2015). Real-time fMRI training-induced changes in regional connectivity mediating verbal working memory behavioral performance. Neuroscience 289 (2015) 114-152. http://dx.doi.org/10.1016/j.neuroscience.2014.12.071.

4. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlagger, et al., 2014. Methods to detect, characterize, and remove motion artifact in resting-state fMRI. NI, 84 320-341.

5. Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., et al, 2010. Multiplexed echo-planar imaging for sub-second whole-brain fMRI and fast diffusion imaging. PLoS ONE 5(12): e 15710. doi: 10.1371/journal.pone.0015710.

6. Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., Murray-Krezan, C., et al, 2012. Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage 61, 115-130.

7. Posse, S., Ackley, E., Mutihac, R., Zhang, T., Hummatov, R., Akhtari, M., Chohan, M., Fisch, B., and Yonas, H., 2013. High-speed real-time fMRI using multi-slab echo-volumar imaging. Front. Hum. Neurosci. 7:479. doi:10.3389/fnhum.2013.00479.

8. Gao, K., and Posse, S., 2004. TurboFIRE: Real-time fMRI with online generation of reference vectors. Proceedings of the 10th Annual Meeting of the Organization for Human Brain Mapping (Budapest: Organization for Human Brain Mapping), WE177.

9. Gembris, D., Taylor, G. J., Schor, S., Frings, W., Suter, D., and Posse, S., 2000. Functional Magnetic Resonance Imaging in Real-Time (FIRE): Sliding-Window Correlation Analysis and Reference Vector Optimization. Mag. Reson. Med. 43:259-268.

10. Lin, F. H., Nummenmaa, A., Witzel, T., Polimeni, J. R., Zeffiro, T. A., Wang, F. N., et al, 2011. Physiological noise reduction using volumetric functional magnetic resonance inverse imaging. Hum. Brain. Mapp. 33, 2815-2830. doi:10.1002/hbm.21403.

11. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner. W., Segall, et al, 2011. A baseline for the multivariate comparison of resting-state networks. Front. Syst. Neurosci. 5:2.

12. Weiskopf, N., Sitaram, R., Josephs, O., Veit, R., Scharnowski, F., et al, 2007. Real-time functional magnetic resonance imaging: methods and applications. Mag. Reso. Imaging 25 (2007) 989-1003. doi: 10.1016/j.mri.2007.02.007.

13. Sitaram, R., Lee, S., Ruiz, S., & Birbaumer, N. 2011. Real-time regulation and detection of brain states from fMRI signals. In R. Coben, & J. Evans (Eds.), Neurofeedback and neuromodulation techniques and applications (pp. 227–253). London: Elsevier Inc.

14. Koush, Y., Rosa, M. J., Robineau, F., Heinen, K., Rieger, S. W et al., 2013. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. NeuroImage 81, 422-430. http://dx.doi.org/10.1016/j.neuroimage.2013.05.010.

99

Chapter 8 Conclusions and Future Directions

8.1. Conclusions

The wSCA approach provides a flexible framework for probing the spatial-temporal dynamics of

whole-brain connectivity in individual networks and in clusters of networks at time scales as short

as 10-20 s. It has been demonstrated that the confound suppression performance of wSCA for

mapping RSN connectivity dynamics compares with that of conventional SCA using regression,

and serves as an alternative to the existing real-time fMRI approaches. This study benefits from

the higher temporal resolution provided by high-speed fMRI in order to improve the detection of

connectivity and dynamic changes thereof, as shown in several recent studies [1-3]. The

methodology developed in this study provides unprecedented sensitivity for mapping the dynamics

of the functional connectome in single subjects at time scales as short as 30 s. The wSCA enables

highly specific mapping of connectivity in single networks, as well as long-range connectivity in

spatially distributed clusters of networks. The identification of regions with pathology related

alterations in RSN connectivity in neurological and psychiatric patient populations is expected to

be facilitated by combining dual thresholding of connectivity maps (see 5.5.3) and pattern

classification. The application of this automated wSCA in patients with brain lesions complements

task-based pre-surgical mapping of eloquent brain regions. The interpretation of RSN connectivity

measured with this method will benefit from multi-modal integration with other functional

imaging techniques that map resting-state connectivity, such as EEG, MEG and fNIRS [3]. This

will ultimately lead to a novel approach to understand the neurophysiological mechanisms that

govern dynamic RSN fluctuations and their effect on brain function.

8.2. Future Directions

Automated seed selection methods

Although wSCA is largely automated, this study relied on previous studies for the selection of

seed regions. The seeds adopted from the group-ICA study [4] do not account for the inter-subject

variability of anatomy. Thus, we are working on novel automated seed identification methods that

100

account for the anatomy of an individual subject, thereby maximizing the functional connectivity

measures.

Integrated windowed-regression SCA approach

The aim of this study is to combine the current wSCA approach with the conventional regression-

based approaches for offline analysis of the fMRI data. This is expected to provide a more accurate

mapping of functional connectivity without the loss of the advantages offered by the wSCA.

Pattern classification and neurofeedback

Further development of pattern classification based approaches to investigate the functional

connectivity dynamics from the perspective of brain states. This will allow for the assessment of

the modulation and regulation of brain function through real-time neurofeedback.

101

References 1. Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., et al, 2010.

Multiplexed echo-planar imaging for sub-second whole-brain fMRI and fast diffusion imaging. PLoS ONE 5(12): e 15710. doi: 10.1371/journal.pone.0015710.

2. Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., et al, 2012. Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage 61, 115-130. doi: 10.1016/j.neuroimage.2012.02.059.

3. Smith, S. M., 2012. The future of fMRI connectivity. NeuroImage 62 (2012) 1257-1266. doi: 10.1016/j.neuroimage.2012.01.022.


Resting-state Connectivity Dynamics in the Human Brain ...

Documents

Transcript of Resting-state Connectivity Dynamics in the Human Brain ...