Transcript Temporal Aspects of Visual Extinction

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 Bandettini
fim.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.
Continuous
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Time (sec)
10
0
Time (sec)
10
Sparse
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Sparse fMRI
BOLD
 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.
0
Time (sec)
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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Resting State Correlations
Activation:
hand movement
Rest:
seed voxel in motor cortex
B. Biswal et al., MRM, 34:537 (1995)
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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)
 ...and other is physiological
(viz. activity extracted from
a brain region of interest)
V1
Attentional modulation of
V1 - V5 contribution
time
V5 activity
Attention
SPM{Z}
V1 activity
 Parametric, factorial design,
in which one factor is
psychological (eg attention)
attention
no attention
V5
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:
Correlations:
A = V1 fMRI time-series
B = 0.5 * A + e1
C = 0.3 * A + e2
A B
A 1
B 0.49 1
C 0.30 0.12
1
B
0.49
A
-0.02
2=0.5, ns.
0.31
Effective connectivity
C
Functional
connectivity
C
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SPM2 Dynamic Causal Modelling (Henson)
Attention
Photic
.52 (98%)
.37
(90%)
.42
(100%)
.56
(99%)
V1
Büchel & Friston (1997)
Motion
Effects
Photic – dots vs fixation
Motion – moving vs static
Attenton – detect changes
SPC
.69 (100%)
.47
(100%)
.82
(100%)
.65 (100%)
IFG
V5
Friston et al. (2003)
• Attention modulates the backwardconnections IFG→SPC and
SPC→V5
• The intrinsic connection V1→V5 is
insignificant 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.
2.
3.
4.
5.
Tag inflowing arterial blood
Acquire Tagged image
2
Repeat scan without tag
Acquire Control image
Subtract Control image – Tagged 1
image
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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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 slice
TR 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
Acquisition
Control
Tagged
Perfusion Signal
Observation
Control
Tagged
Mumford et al. (2006)
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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 eventrelated 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
 ‘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’).
2
1
 Very small signal
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Time (seconds)
0
6
12
– 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.
Magnetic Field
Intracellular
Current
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
confound
Recent work not encouraging
www.pubmed.com/19539040
Image
Phasemap
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