Basics of Structural & Functional MRI Brain Imaging · Basics of Structural & Functional MRI Brain...

Basics of Structural & Functional MRI Brain Imaging

Stefan Sunaert

MR Research Centre Department of Radiology University Hospitals Leuven, Belgium

With lots of lovely, colourful slides from our colleagues:

Judith Verhoeven, Ronald Peters, Alexander Leemans & Thijs Dhollander

Overview

Part 1: Structural MRI

– what is MRI? (basic physics)

– segmentation techniques

– voxel-based morphometry

– diffusion tensor imaging

– summary

Part 2: Functional MRI

– what is fMRI (basic physics/physiology)

– image processing & pitfalls

– experimental design

– what is resting-state MRI? (physics/analysis)

– summary

Part I: Structural neuroimaging

MR Contrast: not all black and white

• There are many types of image contrast

– T1 – longitudinal relaxation, spin-lattice interactions

– T2/* - transverse relaxation, spin-spin interactions

– Proton density (PD) - the no. of protons in each pixel i.e. More protons, more signal, brighter image

– Susceptibility-weighted (SWI) – used for imaging veins, iron

– Diffusion

• Images are a mixture of each contrast, but are weighted to one type e.g. T1, by tissue relaxation properties

• Contrast agents e.g. gadolinium, alter T1 and T2 characteristics of tissue to enhance structures of interest

T1 T2 PD

MR Angiogram Diffusion SWI

Segmentation

• Manual or automatic segmentation of structures of interest

• Generates volumetric

measures that can be analysed statistically between groups e.g. patients versus healthy controls

eg. Do schizophrenics have smaller hippocampi than controls?

Velakoulis et al, 2006, Arch Gen Psych

Automated approaches for investigating the cortex

Cortical Reconstruction

and Automatic Labeling Inflation and

Functional Mapping

Surface Flattening Surface-based Intersubject

Alignment and Statistics

Automatic Subcortical

Gray Matter Labelling

Automatic Gyral White

Matter Labelling

B. Fischl, http://surfer.nmr.mgh.harvard.edu/docs/ftp/pub/docs/freesurfer.intro.2009.ppt

eg

Rimol et al, 2010, Biol psych

Are there differences in

cortical thickness in

schizophrenia and bipolar disorder?

Diffusion tensor imaging

and beyond!....

Courtesy of Thijs Dhollander

Time

start position end position

D is equal in all directions isotropic diffusion

Courtesy of Alexander Leemans

D is not equal in all directions anisotropic diffusion

Time

start position end position


Dxx Dxy Dxz

Dyx Dyy Dyz

Dzx Dzy Dzz

D =

Courtesy of Alexander Leemans & Wim Van Hecke

Tbe

D

k k

k 0S S g g

Stejskal - Tanner

Courtesy of Alexander Leemans/Wim Van Hecke

left-right

anterior -posterior

up-down


Fractional Anisotropy (FA) diffusion anisotropy

high FA

low FA

fibers


microstructure diffusion

FA


microstructure diffusion

FA



Are there any areas of FA reduction in obsessive compulsive disorder?

Szeszko et al, 2005, Arch Gen Psych


left – right anterior – posterior up – down


Ex vivo

dissection

in vivo


Commissural

fiber bundles

Projection

fiber bundles

Association

fiber bundles


The resolution of DTI data sets is limited,

typically to 2x2x2 mm3

Approximate reconstruction of major axon

bundles, not individual axons.

Fiber crossing, kissing, branching, etc. will

therefore occur within a single voxel

Images courtesy of Ben Jeurissen & Alexander Leemans

?

commisural fibers

association fibers

projection fibers Courtesy of Ben Jeurissen

e.g. are there any diffusivity changes in limbic white

matter in remitted bipolar I disorder?

occipital

SPLENIUM

GENU

parietal

dorsal- posterior

dorsal- anterior

subgenual

FORNIX

CINGULUM

Emsell et al, 2011

Part II: Functional brain imaging

32

Visualising the brain at work

• fMRI is a technique capable of visualizing brain function

– Discovered ~ 1990

– Visualize differential activity between 2 (or more) “brain states”

33


34

BLOOD FLOW

HEMOGLOBIN

OXYGEN

Increase in

neuronal activation

slight increase in O2

extraction (1)

+

large increase in perfusion (2)

increase oxyHb / deoxyHb (3)

increase in T2* MR signal

1

2

3


-2

0

2

4

6

8

10

12

0 5 10 15 20

TIME (s)

Ox

yH

b C

on

ce

ntr

ati

on

35

BLOOD FLOW

HEMOGLOBIN

OXYGEN

Increase in

neuronal activation

slight increase in O2

extraction (1)

+

large increase in perfusion (2)

increase oxyHb / deoxyHb (3)

increase in T2* MR signal

1

2

3

BOLD fMRI Physics - Deoxy Hemoglobin

• Magnetic status of Hemoglobin in RBC: (O2)-Fe-Heme OxyHb: Diamagnetic DeoxyHb: Paramagnetic

(Hb: Gd-chelate like)

36

MRI Basis: Subtraction

37

- =

finger

movement rest activation Changes are smaller

than a few percent

?

Acquisition

38

... ...

Acquiring several

images per

condition increases

signal-to-noise

39

BOLD fMRI Physics - which MRI sequence?

• Requirement: Ultra-fast imaging, heavily T2* weigthed+++

– to follow the hemodynamic response (TR=2-3 s) – with whole brain coverage (30+ slices) – robust to motion artifacts (single shot sequence)

TR

BOLD response

mostly used sequence = Echo-Planar Imaging (EPI)

Conditions

• Motor Experiments

– rest / movement (foot, finger, tongue, lip)

– movement 1 / movement 2 (complex, simple)

• Auditory/Language Experiments

– background noise / auditory stimulus (tones, words)

– nonsense words / sense words

– semantic decision / tone decision

• MANY MORE : The sky is the limit...

40

Patient set-up +++

• Stimulation hardware – vision, audition, taste, ….

– Compatible by MRI equipment

– Synchronization with MRI scanner

• Task performance control – Mouse/joystick (yes/no response, reaction times)

– Eye motion recording, EEG,...

magnet

synchro

box

projector lens

magnet

synchro

box

projector lens

Motor Paradigm

2 conditions are alternated:

– Rest (R)

– Movement (MOV)

MOV R motor

30 s 30 s 6x

blocked fMRI design?

• = boxcar design

• “off-on” principle : two (or more) conditions are alternated in blocks

– On = task e.g. presenting pictures

– Off = baseline e.g. black screen

Off

10 scans

Off

10 scans

Off

10 scans

Off

10 scans

Off

10 scans

On

10 scans

On

10 scans

On

10 scans

On

10 scans

Event-related fMRI design?

• Event-related designs associate brain processes with discrete events, which may occur at any point in the scanning session.

– Event 1 = task e.g. pictures of candy

– Event 2 = task e.g. pictures of fruit

– Event 3 = task e.g. control pictures

– Event 4 = baseline e.g. black screen

Pro and con? • Blocked designs:

+ Powerful for detecting activation (good SNR)

+ Simple to perform

+ Clinical +++

– Habituation, fatigue, anticipation

• Event-related designs: + Prevention of habituation and

fatigue

+ sorting of trials according to performance; type,...

– Requires excellent synchronisation

– Requires advanced processing

– Less powerfull to detect activation

– Clinical ---

EVENT Conditions

• Single Events

– sudden presentation of a short stimulus

Lie detection • Modern polygraph

breathing

heart rate

blood pressure

skin conductance

• Possible Issue?

similar autonomic responses with anxiety, fear,…

Simulated Deception

• “Choose an envelope”

• Content: 15 euro

5 of Clubs

• Subject does not know that each envelope contains 5 of clubs

• Task: conceal the card you have during computer test

Simulated Deception: TRUE

Do you have this card?

Simulated deception: LIE

Do you have this card?

Simulated deception

3.25 s

Waar

Rust

NT

Rust

Leugen

Rust

Controle

• Subjects respond with L/R push buttons

• Total scan time of 6,5 minutes

Brain areas activated during lying

Lie

True

Exploring fear - Design

55

.......

Comparison: spiders > neutral

56

>

Controls Phobics

Exploring fear – Follow-up

57

baseline scan

therapy session

post scan

1 week

Resting state MRI

NATURE REVIEWS | NEUROLOGY VOLUME 6 | JANUARY 2010 | 17

can provide non-overlapping information reflecting

various spatial levels of neuronal synchrony.

Spatial levels of neuronal synchrony

Synchrony in intrinsic activity is typically characterized

to be network specific, but a closer inspection reveals

synchrony at multiple spatial levels. At the whole-brain

level, the global signal (averaged across all brain regions)

is positively correlated with much of the gray matter.

Moreover, the global signal has been shown to represent

more than just the average of network-specific signals,33

and might be influenced by global changes, such as the

level of arousal (Figure 2a, left-hand panel). Moving

towards the direction of greater spatial specificity, syn-

chrony in intrinsic activity also exists between networks

such as the default mode network (DMN) and the dorsal

attention network (DAN; Figure 2a, middle panel).7,14,26,34

Even within a network, heterogeneity of neuronal syn-

chrony exists,7,35,36 although this heterogeneity is often

poorly characterized. By use of seed-based mapping,

many networks can be reproducibly generated using a

variety of canonical seed locations, although detectable

dif ferences in the correlation maps can exist depending

on the exact seed used. One way to study these dif ferences

is to use partial correlation mapping (Figure 2a, right-

hand panel),35 which can be conceptualized as simu-

lating a functional lesion by mathematically removing

the neuronal activity contribution from a specific ROI

(see Zhang et al.37 for an exact mathematical definition of

partial correlation). In the context of using intrinsic acti-

vity as a biomarker for disease, one must keep in mind

that functional connectivity occurs at multiple spatial

levels, and that various diseases might be sensitive to

connectivity changes on different spatial scales.

Various specialized methods exist to distinguish

and visualize the multiple levels of spatial integration.

Frequently, a special type of seed-based correlation is

used. Multiple ROIs, often termed nodes, are defined in

representative regions of multiple functional networks,

and the neuronal activity time course from each ROI

is extracted. By calculating the correlation in neuronal

acti vity among these nodes, one can construct a tree

that represents the relatedness of the nodes, using algo-

rithms such as hierarchical clustering (Figure 2b).38 A

concep tually similar tree can be derived through ICA by

systematically varying the number of a priori-defined

components. In effect, this approach varies the threshold

of statistical independence for the separate components

a

b

Defaultmode

Control

Visual

Sensorimotor

0 7Auditory

Dorsalattention

Corticalseed ROIs

t

s c

t

s c

Figure 1 | Intrinsic neuronal activity is synchronous within

neuroanatomically and functionally related regions of the

brain. a | By comparing the neuronal activity between a seed

region (each blue circle) and the rest of the brain, one can

generate a correlation map of brain regions that share

similar neuronal activity to that of the seed. Here, we show

six of the major networks: visual, sensorimotor, auditory,

default mode, dorsal attention, and executive control. The

scale numbered 0–7 indicates relative correlation strength.

b | Correlations in intrinsic neuronal activity are not confined

to the cortex but extend to subcortical regions such as the

thalamus and the cerebellum. The top left panel shows

the cortex partitioned into multiple regions: prefrontal

(dark blue), motor and premotor (orange), somatosensory

(light blue), parietal and occipital (yellow), and temporal

cortex (green). In the right-hand panels, the thalamus and

the cerebellum are colored according to the cortical partition

that is most correlated with each subcortical region.

Correlations in intrinsic activity closely match connectional

anatomy derived from nonhuman primates. For an expanded

discussion, see Supplementary Figure Legend 1 online.

Abbreviations: c, coronal; s, sagittal; t, transverse.

Permission obtained from Oxford University Press ©

Zhang, D. et al. Cereb. Cortex doi:10.1093/ cercor/ bhp182.

▶

REVIEWS

nrneurol_198_JAN10.indd 17 11/12/09 13:05:26

© 20 Macmillan Publishers Limited. All rights reserved10

Zhang & Raichle, 2009, Disease & the brain’s dark energy, Nat Rev Neurology

fMRI during ‘wakeful’ rest i.e. BOLD response during non-task related activity

Most of brain’s energy used for intrinsic neuronal signaling

intrinsic neuronal signaling spontaneous fluctuations in

BOLD signal

synchronicity between neuroanatomically- &

functionally-related regions

used to assess

Functional Connectivity (fcMRI)

Deco and Corbetta. Neuroscientist. 2011 Feb;17(1):107-23


in their goal of characterizing synchrony in intrinsic neu-

ronal activity and often generate similar results.8,28,32 To

be considered clinically useful, functional connec tivity

results must be spatially consistent and statistically robust

across individuals and scanning sessions. Several studies

that used either seed-based or ICA approaches have

demon strated these desired properties.20,47,53,54 The dura-

tion of fMRI data acquisition varies widely among studies,

ranging from <1 min to >30 min. As a general rule, studies

that employ scan times of ≥15 min and examine 15 or

more individuals produce reliable maps of major func-

tional networks. During image acquisition, individuals

usually rest quietly in the scanner, and often visually

fixate on a crosshair to minimize major state transitions

between wakefulness and sleep during the scan. After

the functional connectivity results are generated, many

statis tical methods are available to quantify any popula-

tion differences observed and to test the diagnostic power

of these differences (Box 3).

Alzheimer disease

One of the first studies to use fcMRI to examine disease

pathophysiology was performed by Li et al.55 in patients

with either Alzheimer disease (AD) or mild cogni-

tive impairment (MCI). As the hippocampus is prone

to structural atrophy and neuropathological lesions in

AD, this hypothesis-driven study examined left–right

hippo campal functional connectivity in the two patient

populations. Compared with an appropriate age-matched

control group, patients with AD showed decreased

bi lateral hippo campal connectivity, as measured using

a seed-based ROI approach. This fcMRI study was

also one of the first to test the diagnostic value of using

intrinsic brain activity as a biomarker that distinguishes

patients from healthy controls by calculating sensitivity

and specificity using a receiver operating characteristic

curve (ROC). Subsequently, by means of ICA, Greicius

et al.28 related hippocampal connectivity to a larger collec-

tion of brain regions within the DMN,56 and showed

that DMN connec tivity was reduced in the AD group

compar ed with healthy individuals. Although the study

a

b

c

a

b

c

Figure 2 | Functional connectivity on different spatial

scales visualized using various complementary

techniques. a | Seed-based correlation mapping. The

global signal (seed, left inset) demonstrates widespread—

albeit nonuniform—correlations throughout the gray matter

(left-hand image). At the network level, a map of the default

mode network (middle image, yellow; note cross-network

anticorrelations in blue; global signal regressed) can be

generated with a seed in the left lateral parietal cortex

(yellow circle, middle inset). For a finer dissociation of

subnetwork structure (right-hand image), partial correlation

is performed. The seed is again in the left lateral parietal

cortex, but now the shared signal contributed by the right

lateral parietal cortex (red cross, right inset) is eliminated

(compare with corpus callosotomy in Figure 3b).

b | Independent component analysis decomposition and

hierarchical clustering in three of the most robustly

observed networks (sensorimotor, visual and default

mode). By using 30 and 130 independent component

decompositions, networks and subnetworks can be

hierarchically clustered. c | Graph network stereogram

(animated online155). Canonical nodes of major functional

networks (orange circles) are used to construct this

topological graph. The blue and green lines represent

positive and negative correlations, respectively.

Correlations are strongest within a functional network but

nevertheless span across networks. For an expanded


▶

REVIEWS



seed-based correlation mapping

Zhang & Raichle, 2009


in their goal of characterizing synchrony in intrinsic neu-

ronal activity and often generate similar results.8,28,32 To

be considered clinically useful, functional connec tivity

results must be spatially consistent and statistically robust

across individuals and scanning sessions. Several studies

that used either seed-based or ICA approaches have

demon strated these desired properties.20,47,53,54 The dura-

tion of fMRI data acquisition varies widely among studies,

ranging from <1 min to >30 min. As a general rule, studies

that employ scan times of ≥15 min and examine 15 or

more individuals produce reliable maps of major func-

tional networks. During image acquisition, individuals

usually rest quietly in the scanner, and often visually

fixate on a crosshair to minimize major state transitions

between wakefulness and sleep during the scan. After

the functional connectivity results are generated, many

statis tical methods are available to quantify any popula-

tion differences observed and to test the diagnostic power

of these differences (Box 3).

Alzheimer disease

One of the first studies to use fcMRI to examine disease

pathophysiology was performed by Li et al.55 in patients

with either Alzheimer disease (AD) or mild cogni-

tive impairment (MCI). As the hippocampus is prone

to structural atrophy and neuropathological lesions in

AD, this hypothesis-driven study examined left–right

hippo campal functional connectivity in the two patient

populations. Compared with an appropriate age-matched

control group, patients with AD showed decreased

bi lateral hippo campal connectivity, as measured using

a seed-based ROI approach. This fcMRI study was

also one of the first to test the diagnostic value of using

intrinsic brain activity as a biomarker that distinguishes

patients from healthy controls by calculating sensitivity

and specificity using a receiver operating characteristic

curve (ROC). Subsequently, by means of ICA, Greicius

et al.28 related hippocampal connectivity to a larger collec-

tion of brain regions within the DMN,56 and showed

that DMN connec tivity was reduced in the AD group

compar ed with healthy individuals. Although the study

a

b

c

a

b

c

Figure 2 | Functional connectivity on different spatial

scales visualized using various complementary

techniques. a | Seed-based correlation mapping. The

global signal (seed, left inset) demonstrates widespread—

albeit nonuniform—correlations throughout the gray matter

(left-hand image). At the network level, a map of the default

mode network (middle image, yellow; note cross-network

anticorrelations in blue; global signal regressed) can be

generated with a seed in the left lateral parietal cortex

(yellow circle, middle inset). For a finer dissociation of

subnetwork structure (right-hand image), partial correlation

is performed. The seed is again in the left lateral parietal

cortex, but now the shared signal contributed by the right

lateral parietal cortex (red cross, right inset) is eliminated

(compare with corpus callosotomy in Figure 3b).

b | Independent component analysis decomposition and

hierarchical clustering in three of the most robustly

observed networks (sensorimotor, visual and default

mode). By using 30 and 130 independent component

decompositions, networks and subnetworks can be

hierarchically clustered. c | Graph network stereogram

(animated online155). Canonical nodes of major functional

networks (orange circles) are used to construct this

topological graph. The blue and green lines represent

positive and negative correlations, respectively.

Correlations are strongest within a functional network but

nevertheless span across networks. For an expanded


▶

REVIEWS



Independent component analysis (ICA) decomposition & hierarchical clustering

Zhang & Raichle, 2009

Are there differences in connectivity in the language network in autistic children?

Verhoeven et al, 2011 Figure courtesy of Judith Verhoeven

Some help!..

Block or

Event-related?

Voxel-based

analysis

Connectivity

matrix

T1 (anatomy) fcMRI DTI fMRI

Tractography

Connectivity

Design

Scan

type

Analysis

type

Quantitative

measure

Stimulus?

Threshold –

FWE v FDR

SPM –

F-contrast

Difficulty

level?

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