Functional MRI for Clinical Neuroscience

Danny JJ Wang, PhD, MSCE Ahmanson-Lovelace Brain Mapping Center

Department of Neurology UCLA

Outline 1. BOLD fMRI

• Functional hyperemia and common confounding factors

• Task activation BOLD fMRI – Memory encoding in epilepsy; Pre-surgical mapping for brain tumor

• Resting state BOLD fMRI - Functional connectivity, network analysis and applications

2. ASL perfusion MRI and clinical application 3. Calibrated fMRI and alternative fMRI

methods

Neurovascular Coupling

With neural activity, increases in oxygen and glucose consumption are followed by an increase in CBF. CBF and glucose consumption are similar

in magnitude, oxygen consumption increases much less than CBF

Fox P et al Science 1988

Mechanism of Neurovascular Coupling

Diagram of Neurovascular Coupling D'Esposito et al. Nat Neurosci Rev. 2003

• Diffusion of K+, H+, adenosine, NO from synapses

• Glutamate/glutamine cycle within astrocytes

• Direct innervation of smooth muscle cells by neurons/axons

• Linearity between CBF and neuronal activity is NOT always warranted

Biophysics Mechanism of BOLD fMRI

OxyHb is diamagnetic and deoxyHb is paramagnetic and destroy local magnetic field

Electrophysiological Correlates of BOLD

Local field potential (LFP) is the best correlate of BOLD signal Logothetis et al. Nature 2003

Structural MRI

BOLD fMRI

Genetic Effects on Neurovascular Coupling

Diagram of Neurovascular Coupling Iadecola & Nedergaard, Nat Neurosci 2007

Hahn et al. Cerebral Cortex 2011

Drug (Caffeine) Effects on Neurovascular Coupling

Chen & Parrish, Neuroimage 2009

20-30% CBF reduction by caffeine

Increased BOLD response by 30% at 2.5mg/kg dose

Hormonal (Estrogen) Effects on Neurovascular Coupling

Dietrich et al, Neuroimage 2001

Word stem completion Mental rotation

Male Male

Female peak estrogen Female peak estrogen low estrogen low estrogen

Pathophysiological Effects on Neurovascular Coupling

Pineiro et al, Stroke 2002 Roc et al, Stroke 2006

Standard Procedures of BOLD fMRI Gradient-echo EPI

Motion correction

Detrending /

Temporal filtering

Spatial smoothing

General Linear Model (GLM) analysis of fMRI

Design matrix

β

“Activation map” G =

Data (X)

Voxels ->

Modeled time course

Software: SPM FSL AFNI Brainvoyager

Seizure Localization using fMRI

WADA Test Rabin et al Brain 2004

Memory encoding fMRI

Prediction of memory decline using fMRI

Rabin et al Brain 2004

Correlation between fMRI asymmetry and WADA test

Prediction of post-surgery memory change using fMRI

Prediction of memory decline using fMRI

Dupont et al Radiology 2010

ROC curve using delayed recall fMRI as predictor of memory change post surgery

Binder et al Neurosurg Clin N Am 2011

Pre-surgical mapping of brain tumor using fMRI

Sunaert S. JMRI 2006

Landmarks were used localize functional areas (Left: motor cortex; Right: Language areas including Broca’s and Wernicke’s area)

Pre-surgical mapping of brain tumor using fMRI

Sunaert S. JMRI 2006

FMRI procedures for pre-surgical mapping of language, auditory and motor functions, as well as the combination with DTI

Comparison of fMRI with direct cortical stimulation

Roux et al. Neurosurgery 2003

Imperfect correlation between fMRI and DCS was found for language areas (Sensitivity = ~30%, Specificity = 97%)

The agreement between fMRI and DCS for motor centers was found to be 84%; for sensory centers it was 83%.

Majos et al. Eur J Radiol 2005

BOLD fMRI is based on neurovascular coupling and interplay of CBF, CBV and CMRO2.

Clinical BOLD fMRI is feasible and show promising results in epilepsy and pre-surgical mapping of brain tumor.

Caveats exist for interpreting BOLD signals.

BOLD signal may be limited by sensitivity and spatiotemporal resolution of fMRI methods.

Summary of Task Activation BOLD FMRI

Resting State fMRI and Functional connectivity

Task Activation Resting State FC

Biswal et al MRM 1995

Resting Brain Function

Activation

Functional connectivity (FC) analysis based on cross-correlation revealed brain networks with synchronized activity

Default Mode Network (DMN) and Functional Connectivity

Intrinsic correlations in spontaneous fluctuations in the fMRI BOLD signal between a seed region in the posterior cingulate cortex (PCC) and all other voxels in the brain. Functional and structural connectivity may not overlap.

Fox MD et al. PNAS 2005; Greicius MD et al. Cereb Cortex 2009

Default Mode Network (DMN) in AD

Reduced DMN activity in PCC and hippocampus in AD vs. elderly control subjects

Greicius MD et al. PNAS 2004; Cereb Cortex 2011

Control AD

DMN (yellow) and PiB (blue) overlap (red)

Independent Component Analysis (ICA) of Resting State FMRI

Calhoun & Adali IEEE Rev BME 2012

Control AD

Common software: GIFT, FSL (MELODIC)

Independent Component Analysis (ICA) of Resting State FMRI

Smith S M et al. PNAS 2009

Ten well-matched pairs of networks from the 20-component analysis of the 29,671-subject BrainMap activation database and (a completely separate analysis of) the

36-subject resting FMRI dataset.

Graph Theoretical Network Metrics

• Small world / biological networks have relatively good clustering (high local efficiency) with short cuts (low path length/high global efficiency), good fault tolerance, integrated processing, and information efficiency.

Graph Metric Regular/Lattice

Random Small World/ Biological Networks

Global Efficiency Low High High

Local Efficiency High Low High

Degree (# edges) Distribution

Unimodal Gaussian Power law, truncated power law

Betweenness Centrality

Unimodal Gaussian Nodes with really high BC (hubs)

Modularity Low Low High

Graph Theory: Complex Brain Networks

Sporns & Bullmore, Nat Neurosci Rev 2009

http://en.wikipedia.org

H(X) is Shannon entropy p(xi) is the probability mass function of outcome xi b is the base of the logarithm used

Information Theory and Entropy

H(X) is conditional entropy p(xi,yj) is the probability that X=xi and Y=yj. This quantity should be understood as the amount of randomness in the random variable X given that you know the value of Y

Liu et al. JMRI (2013); Smith et al BIB (2014)

Multi-Scale Entropy (MSE) of RS-FMRI in Aging

Mean GM MSE difference between young and elderly subjects

Regional MSE difference between 25 MCI (CDR=0.5) and 25 control (CDR=0)

Resting state fMRI is synchronized between brain regions belonging to networks

RS-fMRI can be analyzed by cross-correlation, ICA, graph theory and information theory

Clinical applications in aging and dementia are promising

Need derive reliable clinical markers.

Summary of Resting State BOLD FMRI

Imaging Slice

Arterial Tagging Plane

Continuous Adiabatic Inversion Geometry

Control Inversion Plane

B F

ield

Gra

dien

t

Single Slice Perfusion Image about 1% effect

Control - Label

Detre et al. MRM 1992, Williams et al. PNAS 1992

Arterial Spin Labeled (ASL) Perfusion MRI

CBF in “classical” units of ml/100g/min

ASL Strategies

pCASL EPI pCASL GRASE PET CBF

Comparison of ASL and FDG-PET

CBF (pCASL) CMRglc (FDG-PET)

Cha et al. JCBFM (2013)

20 healthy volunteers (23-59yrs) participated both ASL MRI and FDG-PET scans

Perfusion MRI in Alzeimer’s Dementia

AD vs. CONTROL

Alsop et al Ann Neuroloy (2000)

AD vs. CONTROL

MCI vs. CONTROL

Johnson et al Radiology (2005) Alsop et al Neuroimage (2008)

Representative AIS cases showing hypo-perfusion lesions

Wang et al Stroke (2012)

Multi-delay Multi-parametric ASL in Acute Ischemic Stroke

Wang et al NI: Clinical (2013)

PCASL 3D GRASE with 4 delays (1.5, 2, 2.5, 3s) allows

estimation of ATT, CBF and arterial CBV (aCBV)

3T CASL in Brain Tumor

Tumor grading and biopsy guiding Wolf et al. JMRI (2005)

Delineation of Shunted flow in AVM

Wolf et al, AJNR 2008

Seizure Localization using ASL

Oishi et al, Neurorad 2012

Ictal Perfusion

Interictal Perfusion

Vessel Encoded pCASL (VE-PCASL)

Apply Gx or Gy in pCASL to encode L/R ICA and VA using Hadamard scheme

Wong EC MRM (2007)

Vessel-encoded pCASL in AVM

VE-PCASL DSA Yu et al AJNR (Pre-accepted)

AUC = 0.95

Standard and custom labeling efficiencies are used to estimate supply fractions of feeding arteries.

behavior or drug neural

function

metabolism

blood flow

biophysics***

***site/scan effects

Physiological Basis of fMRI/phMRI

disease

blood volume

BOLD fMRI ASL fMRI

Cortical responses to amphetamine exposure studied by pCASL MRI

Nortin et al, Neuroimage, 2013

subject 1

0 2 4 6 8 10

subject 2

3050

70

subject 3

3050

70

subject 4 subject 5 subject 6

subject 7 subject 8

3050

70

subject 9

0 2 4 6 8 10

3050

70

subject 10 subject 11

0 2 4 6 8 10

subject 12

Cer

ebra

l Blo

od F

low

(m

L/10

0g/m

in)

Time after dose (h)

• 12 healthy subjects double blinded design • 6 20mg d-amphetamine; 6 placebo • ASL and blood samples were collected 10 time points during 10hr after dose

ASL Vascular Reactivity in Hypertension

Hajjar et al Hypertension 2010

NC HT

Baseline 5% CO2 100% O2

Calibrated fMRI with concurrent ASL/BOLD

Hoge et al PNAS 1999

Concurrent ASL/BOLD with CO2 and visual stimulation

Concurrent ASL/BOLD fMRI

Gauthier et al Neurobio Aging 2013

Stroop Task CO2

BOLD

CBF

BOLD CBF

ASL/BOLD Vascular Reactivity in AD

Yezhuvath et al Neurobio Aging 2012

BOLD CO2 reactivity ASL resting Perfusion

CBV based fMRI - VASO

Lu et al MRM 2003

Inversion-recovery null the blood signal, VASO fMRI shows negative signal changes in response to brain activation

ASL perfusion fMRI is an important quantitative tool complementing BOLD fMRI ASL perfusion fMRI has unique value in characterizing baseline brain function and pharmacological manipulations Concurrent ASL/BOLD is a promising tool for clinical fMRI Alternative fMRI methods exist such as VASO

Summary of ASL Perfusion MRI

BOLD fMRI remains the main tool for clinical fMRI, and resting state fMRI is particularly promising.

ASL perfusion MRI is an important quantitative imaging method complementing BOLD fMRI. Combinations of BOLD/ASL and other imaging modalities (DTI, structural MRI) offer comprehensive evaluation of brain function.

Conclusion

Acknowledgement

UCLA UPenn Jeff Alger, PhD John Detre, MD Lirong Yan, PhD Hee Kwon Song, PhD Emily Kilroy, MS Yiqun Xue, PhD David Liebeskind, MD Robert Smith, PhD International collaborators Liana Apostolova, MD Yan Zhuo, MS Noriko Salamon, MD PhD Matthias Guenther, PhD John Ringman, MD Songlin Yu, MD Collin Liu, MD Zengtao Zuo, BS NIH grants R01-MH080892, R01-NS081077 and R01-EB014922 Siemens Healthcare, Biogen IDEC