Advanced Methods

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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

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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.

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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.

4
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.

Continuous
Time (sec)
0
10
Sparse
Time (sec)
0
10
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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.

Sparse
Time (sec)
0
10
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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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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)

V1 activity
time
Attention
attention
V5 activity
no attention
Attentional modulation of V1 - V5 contribution
V1 activity
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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-series
• B 0.5 A e1
• C 0.3 A e2

Correlations A B C A 1 B 0.49 1 C
0.30 0.12 1
0.49
-0.02
?20.5, ns.
0.31
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SPM2 Dynamic Causal Modelling (Henson)
Attention
Photic
.52 (98)
.37 (90)
.42 (100)
.56 (99)
.69 (100)
.47 (100)
Büchel Friston (1997)
.82 (100)
Motion
.65 (100)
Friston et al. (2003)
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Functional Connectivity

• Observe which regions 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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Arterial Spin Labeling

• Tag inflowing arterial blood
• Acquire Tagged image
• Repeat scan without tag
• Acquire Control image
• Subtract Control image Tagged image

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1
The difference in magnetization between tagged
and control images is proportional to regional
cerebral blood flow http//www.umich.edu/fmri/
asl.html
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3
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ASL

• MR signal is based proportion of atoms aligned
  with the magnet than without.
• 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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Data from MCBI

• We collect 16 slices 3.5x3.5x6mm
• TR 2.2sec (4.4sec for tagcontrol pair).
• TE12ms (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

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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

Control Tagged
Acquisition
Perfusion Signal
Control Tagged
Observation
Mumford et al. (2006)
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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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Arterial Spin Labelling

• Benefits
• Direct measure of blood flow
• Less drift Better for assessment of very slow
  (gt1min) 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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Magnetic Source MRI

• fMRI BOLD is very indirect measure.
• Can we directly measure brain activity?
• Neural firing influences magnetic field (e.g.
  MEG).

• Potential of msMRI
• Stroke patients where blood flow is abnormal
• Is this effect big enough to measure?
• How would you design a msMRI study?

Image
Phasemap