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sparse temporal sampling
Here's a few suggestions for analysis of sparse fMRI data. I don't know of
a single methods paper that explains everything. You might to read a paper
like Hall et al (1999, Human Brain Mapping).
Most of the preprocessing and analysis steps are as usual for fMRI data. A
few points of difference between sparse and standard fMRI analysis are as
1. Don't perform slice acquisition time correction. Given the 15s delay
between scans, interpolation between successive scans is ineffective and
will damage your data.
2. At the analysis stage, I often use an FIR basis set of duration 15
seconds (equivalent to your scan repetition rate), with a single time bin.
This models the data as a box-car covering the single scan following each
condition. This is a simpler model to set up than the typical haemodynamic
response model, but usually effective for sparse data.
3. If you do want to use an HRF model - for instance, if you have
differences in the timing of trials within your silent period - then you
also need to take care over the specification of SPM.xBF.T and SPM.xBF.T0
in your model.
4. You need to take care in specifying the low-pass filter and AR(1)
parameters in your model. I often turn these off entirely since my goal is
to do second level, group analyses rather than computing single subject
statistics. The reason for concern is that scan-to-scan auto-correlation
is greatly reduced with a long TR design, and the slow changes in
activation between conditions are at a much lower frequency in sparse
On 15/07/2011 05:06, "AUDITORY automatic digest system"
>Date: Thu, 14 Jul 2011 13:44:23 -0700
>From: theresa veltri <theresaveltri@xxxxxxxxxxx>
>Subject: sparse temporal sampling
>Hi, I am currently starting some fMRI analysis in SPM8. Unfortunately I
>unsure how to account for the sparse temporal sampling design that was
>For example, each trial was 15s, but TR is only 4s. Does anyone have expe
>rience with specifying such parameters in SPM? Does anyone have any
>ces the could suggest? any help would be appreciated.
>MSc in Cognitive and Computational Neuroscience
>University of Sheffield, UK