Transcript Temporal Aspects of Visual Extinction

Comparing SPM and FSL
Chris Rorden
– Contrasting SPM to FSL
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FSL vs SPM5
We have focused on FSL
– Completely free
– Allows students to get a feel for fMRI analysis
SPM is the most popular tool
– Free, but requires Matlab to run
Here we contrast these tools
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FSL3.3 vs SPM5
SPM5
FSL
Motion Correction
Motion Correction
Slice Timing Correction
Slice Timing Correction
Normalization
Smoothing
Smoothing
Individual Statistics
Individual Statistics
Normalization
Group Statistics
Group Statistics
 Typical fMRI processing
pipeline is similar.
 In FSL, normalization is
done after initial statistics.
 Allows you to see
activation on original
raw scans.
 Faster, as we usually
super sample images
during normalization
(i.e. increase field of
view and resolution).
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Motion Correction (=realignment in SPM)
 Both SPM and FSL use rigid body registration. Different cost
functions are used (SPM: variance; FSL: Normalised
Correlation).
 By default, SPM aligns all images to first 4D volume, while
FSL aligns to the middle 4D volume.
 Optionally, SPM can ‘realign and unwarp’ which attempts to
correct head motion related changes in image intensity (see
spatial processing lecture).
– FSL’s optional solution is to add motion parameters to statistical
model (the Stats tab of FEAT).
– SPM’s solution more sophisticated, but time consuming.
– Both techniques can reduce noise, but will reduce power if head
motion correlates with task (e.g. head moves with button presses).
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Slice Timing Correction
 Slice Timing Correction attempts to make all slices in
a 3D volume appear as if they were collected
simultaneously (see temporal processing lecture).
– Dilemma: required but inaccurate for long TR, accurate but
not influential with short TR.
 SPM and FSL use the same algorithm. By
convention, most SPM users employ STC for event
related designs, while FSL users do not.
 We are fortunate to have a scanner that can provide
full brain coverage with a short TR. Therefore, I would
use a rapid (~2sec) TR for event related designs and
not use STC.
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Smoothing
During the ‘smoothing’ stage, both FSL and SPM
apply a spatial smoothing.
FSL also applies a 100s highpass temporal filter to
remove low frequency artefacts.
– SPM’s temporal filtering occurs during the individual
statistics stage, with a default 128s highpass.
– For both tools, a low pass filter is optional, and can
help block designs.
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Normalization
Normalization align’s the individual’s brain to
stereotaxic space (warping the orientation and
size), allowing comparison between people.
SPM and FSL have very different approaches to
normalization.
In general, FSL is very robust (always
approximately right), but pretty constrained (there
tends to be a lot of residual error).
SPM is very aggressive, and can do better than
FSL in ideal circumstances (i.e. good data).
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Normalization
 Two competing approaches for normalization (found both
in SPM and FSL):
–
Direct normalization:
1. Normalize T2* fMRI data directly to stereotaxic space
–
Indirect normalization:
1. Coregister T2* fMRI data to T1 scan
2. Normalize high resolution T1 to stereotaxic space
3. Use parameters from step 2 to normalize fMRI data to stereotaxic space
 The second approach is better in theory. However, it does
require a good structural scan, and has more chances to
fail catastrophically.
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Normalization
 FSL uses FLIRT (FMRIB's Linear Image Registration
Tool) to normalize
– Only linear normalization.
– By default, direct normalization uses a T1 template image,
so a between-modality (correlation ratio) cost function is
used.
– FSL’s analysis of DTI data uses a non-linear registration
tool (IRTK), but this is not typically employed for fMRI data.
 SPM’s normalization initially uses linear transforms,
and then applies non-linear transforms.
– By default, direct normalization uses a T2* template, so a
variance cost function can be applied.
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Normalization
SPM5 introduces a very aggressive indirect
normalization.
– The T1 scan is bias corrected and segmented to
gray matter, white matter and CSF probability
maps (see VBM lecture).
– Warping these tissue maps to standard space can
provide more accurate normalization (as non-brain
tissue does not influence parameters).
– FSL does ‘skull strip’ data for normalization, but
this is more constrained than SPM’s method.
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Individual Statistics
SPM and FSL apply general linear model to data
SPM models HRF using double gamma function
(blue); by default, FSL uses a single gamma
function (red).
Both include temporal derivatives
(turn these off for block designs)
SPM and FSL have different
approaches for
autocorrelation – see the
temporal processing lecture.
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Group statistics
 FSL uses estimates of each
individuals’ contrast parameter
estimates (copes) and variability
(varcopes), SPM only uses
estimates of contrast. (see
statistics lecture).
 In theory, FSL might be a bit
more sensitive. In practice, it is
much slower.
Z stats
Group
copes
copes
copes
copes
varcopes varcopes varcopes varcopes
Sub 1
Sub 2
Sub 3
Sub 4
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Learning SPM
SPM5 comes with an excellent manual
Chapter 25 walks you through analysis of a
block design.
Chapter 26 guides you through the analysis of
an event-related design.
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