1

Advanced Methods

Chris Rorden– Advanced fMRI designs

Adaptation fMRI Sparse fMRI Resting State fMRI

– Advanced fMRI analysis ICA Effective and Functional Connectivity Analysis

– Alternative measures of activation Perfusion msMRI

– Comparing SPM to FSL

Some slides from Peter Bandettinifim.nimh.nih.gov/presentations

CABI talk 18 November 2009

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Adaptation Designs (from Kanwisher)

Show two stimuli in rapid succession.

See if a brain region can discriminate if these stimuli are the same or different.

Classically, regions show adaptation – less time to process same information twice in a row.

a.ka. ‘repetition suppression’ paradigm.

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Adaptation Designs

FFA activates strongly to faces Does it discriminate – yes: we see adaptation

response. Similar adaptation is not seen for chairs, so

suggests special role in face processing.

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Sparse fMRI

Standard fMRI acquires data continuously.– Loud noises can make it difficult to examine auditory

stimuli.

Sparse imaging includes a delay between each fMRI volume, so stimuli can be presented while scanner is silent.

Time (sec)0 10

Continuous

Time (sec)0 10

Sparse

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Sparse fMRI

Typically, sparse design like a block design – each acquisition measures effect of single stimuli.

Stimuli must be presented ~5sec prior to acquisition. Sparse designs have less power than continuous

designs, and it is difficult to estimate latency of BOLD response.

Due to T1 effects, Sparse designs can still have good power.

Time (sec)0 10

BO

LD

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Resting State fMRI

Resting state fMRI allows us to estimate natural connectivity between regions: which regions cycle together.

Essentially, have individual lie in scanner resting while you collect a lot of fMRI data.

Must covary out low frequency scanner drift as well as high frequency physiological noise.

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Resting State Correlations

Rest: seed voxel in motor cortex

B. Biswal et al., MRM, 34:537 (1995)

Activation: hand movement

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Independent Component Analysis

In conventional analysis, we see if a HRF predicts our behavioral design.

FSL includes MELODIC for ICA, includes nice description:– www.fmrib.ox.ac.uk/analysis/research/melodic/

In ICA, we decompose fMRI data into different spatial and temporal components.– estimate the BOLD response.– estimate artifacts in the data, then run conventional analysis on

denoised data.– find areas of ‘activation’ which respond in a non-standard way.– analyse data for which no model of the BOLD response is available

(e.g. resting state fMRI).

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ICA vs Conventional Analysis

Conventional analysis is confirmatory: does my model predict data.

Results depend on model

ICA is exploratory: Is there anything interesting in the data?

Can give unexpected results.

What is the potential of ICA?FSL includes melodic, so you can examine our

data.Many use melodic to remove artifacts.

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Connectivity

Classic fMRI detects all regions involved with task– Motor task would elicit motor

cortex, cerebellum and supplementary motor area.

– It would be much more insightful if we could see the direction of connections

Examples include Dynamic Causal Modelling

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Psycho-physiological Interaction (from Henson)

Parametric, factorial design, in which one factor is psychological (eg attention)

...and other is physiological (viz. activity extracted from a brain region of interest)

AttentionAttention

V1V1

V5V5

SPM{Z}SPM{Z}

attention

no attention

V1 activityV1 activity

V5

acti

vity

timetime

V1

acti

vity

Attentional modulation of

V1 - V5 contribution

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Effective vs Functional Connectivity (Henson)

No connection between B and C,yet B and C correlated because of

common input from A, eg:A = V1 fMRI time-seriesB = 0.5 * A + e1C = 0.3 * A + e2

Correlations:

A B CA 1B 0.49 1C 0.30 0.12 1

A

B

C

0.49

0.31

-0.02

2=0.5, ns.Functional Functional

connectivityconnectivity

Effective connectivityEffective connectivity

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SPM2 Dynamic Causal Modelling (Henson)

V1 IFG

V5

SPC

Motion

Photic

Attention

.82(100%)

.42(100%)

.37(90%)

.69 (100%).47

(100%)

.65 (100%)

.52 (98%)

.56(99%)

Friston et al. (2003)

Büchel & Friston (1997)

EffectsEffects

Photic – dots vs fixationPhotic – dots vs fixationMotion – moving vs staticMotion – moving vs staticAttenton – detect changesAttenton – detect changes

• Attention modulates the backward-Attention modulates the backward-connections IFGconnections IFG→SPC and →SPC and SPC→V5SPC→V5

• The intrinsic connection V1→V5 is The intrinsic connection V1→V5 is insignificant in the absence of motioninsignificant in the absence of motion

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Functional Connectivity

Observe which region’s activity correlates.Can be done while resting in scanner

– Hampson et al., Hum. Brain. Map., 2002

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Perfusion imaging

Use Gd or blood as contrast agent. Allows us to measure perfusion

– Static images can detect stenosis and aneurysms (MRA)

– Dynamic images can measure perfusion (PWI) Measure latency – acute latency appears to be strong

predictor of functional deficits. Measure volume Can also measure task-related changes in blood flow

(ASL), similar to fMRI.

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ASL

MR signal is based proportion of atoms aligned with the magnet.

Slightly lower energy state aligned, so atoms preferentially align.

More alignment in higher fields

However, 180° pulse will reduce this signal.

=

3T Net Magnetization

=

3T NM after 180° pulse

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Arterial Spin Labeling

1. Tag inflowing arterial blood2. Acquire Tagged image3. Repeat scan without tag4. Acquire Control image5. Subtract Control image – Tagged

image

The difference in magnetization between tagged and control images is proportionalto regional cerebral blood flow

http://www.umich.edu/~fmri/asl.html

1

2

3

4

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Data from Trio

We collect 16 slices 3.5x3.5x6mm

TR 2.2sec (4.4sec for tag+control pair).

TE=12ms (very little BOLD artifact).

Not wise to collect ASL faster than 2sec (otherwise, not enough transit time between volumes. Wise to use slower TR for individuals with impaired perfusion (stroke).

Control

Tagged

Difference

Mean of 73 differences

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TI and TR influence contrast

time

TI (Inversion Time)

TR (Repeat Time) TR (Repeat Time)

TI must be long enough for tagged blood to wash in to tagged sliceTR must be long enough to allow tagged blood to wash out of control slice

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TR

Optimal TR depends on the individual’s blood transit time.– ~2.4s, the ‘tagged’ image has more tagged blood than the control image.– ~1.8s, very low contrast: tagged blood in both control and tagged image.– ~1.2s reverse contrast: tagged blood does not reach slice until the control image

(except fast arteries).

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Blood Transit Time

BTT varies in individuals If the TR is very short, the blood will

not yet reach the capillary beds. Therefore, the control image can

appear darker than the tagged image! In particular, very little signal when

BTT matches TR. Transit time actually faster during

active than rest. Either calculate BTT for each

individual MRM, 57, 661-669 or use a long TR (4s, e.g. 8 s for control+tag pair)

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Theory: Signal in ASL

Tagged image: Inflowing inverted spins within the blood reducing tissue magnetization: more flow = darker

Control: Inflowing blood has increased magnetization than saturated tissue: more flow = brighter

Mumford et al. (2006)

ControlTagged

ControlTagged

Acquisition

Perfusion Signal

Observation

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BOLD and Perfusion

ASL scans are designed to measure perfusion

However, because they are T2* scans, they also have a BOLD artifact.

To minimize BOLD, keep TE to a minimum

BOLD is present in BOTH tagged and control image

Because the tagged and control images are acquired several seconds apart, simple subtraction of tagged and control image is not a good idea for event related designs.

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Analysis Strategies

Simple subtraction– Subtract tagged image from subsequent control image– Halves the amount of samples (e.g. with 3sec TR, one sample

every 6sec).– Problem: leading edge and falling edge of HRF will have very

different signal in control and tagged image: poor choice for event-related designs.

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Analysis Strategies

Inter-trial subtraction– Subtract tagged image from control image acquired at the same

interval after task onset.– Halves the amount of samples (e.g. with 3sec TR, one sample

every 6sec).– Problem: events must be ordered to coincide with TRs (e.g. period

of on-off blocks is an odd number of TRs).

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Analysis Strategies

FSL interpolates controlled and tagged images to estimate signal for both control and tagged images.

The number of volumes is not halved,– analysis proceeds similar to fMRI data. Samples not completely independent, so DF is adjusted. The FSL difference signal is actually added to a mean image for all samples, so

that the relative signal-noise is similar to fMRI

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Analysis

Easy to analyze ASL data with FSL:– Select perfusion check box– FSL simply subtract tagged image from neighboring control

FSL is not optimal– Control and tagged image are not acquired simultaneously– Therefore, they sample different points of HRF.– There are alternatives

Sinc interpolate to estimate simultaneous signals (interp_asl) Intertrial subtraction: compare control image with tagged image that was

collected at same delay after event (Yang et al, 2000). Add both tagged and control images in a single model (Mumford et al,

2006).

– In general, FSL approach only good for block designs.

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Measuring the initial dip

0 6 12 18 24

2

1

0

Time (seconds)

‘Initial dip’ than signal increase seen 5 sec later.– No venous artefacts– Later overcompensation may not

be specific (‘watering a garden for the sake of a thirsty flower’).

Very small signal– Difficult to realize benefit if you

can’t achieve good spatial resolution.

– Remains controversial – best parameters unknown.

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Higher spatial resolution

Contrast to noise ratio dependent on volume of hydrogen:

– Standard T2* 3x3x3mm = 27mm3– 1.5*1.5x2mm = 4.5mm3= 17% of SNR

However, for small structures or edges, higher resolution reduces partial volume effects.

– Therefore, higher resolution can improve % signal change observed

For ideas on optimal voxelsize, see www.pubmed.com/17101280

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Arterial Spin Labelling

Benefits:– Direct measure of blood flow– Less drift: Better for assessment

of very slow (>1min) changes.– Data whiter (less dominated by

low frequency noise)– Signal more from tissue than

veins.– Less spatial distortion than

BOLD (BOLD requires long TE without spin-echo)

– Perhaps better statistical power for group analysis (calibrated measure has less variability).

Disadvantages– Requires two images:

tagged and subtraction, therefore TR is twice as long.

– Less statistical power for individual (fewer samples)

– Can not collect many slices: can only see portion of brain, normalization difficult (hurts group statistics)

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Super high resolution

Venous effects decrease with field strength (e.g. at 1.5T, capillary/venous ratio much smaller than at 7T).

Higher SNR with 7T can allow very high resolution imaging: – Example ocular dominance columns for left and

right eye projection to visual cortex.– 0.5x0.5x3mm (0.75mm3)– www.pubmed.com/17702606

Spin-echo sequences (HSE T2) can be used as well as traditional GE T2* at these field strengths to detect BOLD.

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Neural current MRI (Bandettini)

In theory, MRI phase maps should show the direct neural firing as detected by MEG. Intracellular

Current

Magnetic Field

Surface Field Distribution Across Spatial Scales

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magnetic source/neural current MRI

fMRI BOLD is very indirect measure.Can we directly measure brain activity?Neural firing influences magnetic field (e.g. MEG).Is this effect big enough to measure?

Very controversial.Most designs do not remove BOLD

confoundRecent work not encouraging

www.pubmed.com/19539040

Image

Phasemap