Max
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fMRI-Adaptation and Getting to Know mrVista: Deconvolution & Multi-Voxel Patterns
Background
fMRI-Adaptation (fMRI-A) refers to the phenomenon that, as a region of cortex is activated by a stimulus repeatedly, it becomes less responsive overall to successive presentations of the stimulus. While the exact mechanisms of adaptation vary from region to region, fMRI-A in visual cortex has been studied in depth (see Grill-Spector et al., 2006, Weiner et al., 2010). In order study fMRI-A, stimuli must often be presented in rapid succession using event-related designs. These designs allow experimenters to study temporally shorter processes, but they come with several methodological complications, specifically in data analysis techniques.
The shorter inter-stimulus intervals involved in event-related designs lead to signal overlap across different trial types and may induce dependencies and nonlinearities between study events and/or conditions. Because of these issues, standard General Linear Model (GLM) analyses may be less precise and appropriate than usual. Choices as to how to analyze timecourse data in event-related designs can have important consequences for significance detection, data visualization, and relative activations to different types of stimuli. We hope to explore these differences in the current study.
Region of Interest (ROI) GLM analyses provide crucial information about regional selectivity and activation, yet they oversimplify data by averaging activation across an entire region down to a single timecourse. Multi-voxel patterns can be used to further investigate and characterize the distribution of activity within a region by cross-correlating activation on a voxel-by-voxel basis between trial types. In ROI analyses, different voxels may drive the same signal change within an ROI. Using MVPA, distributed patterns of activation may be correlated and one is able to test effects that a GLM would not be able to capture.
A second goal of this project was to familiarize the author with standard fMRI data analysis techniques.
Methods
MR acquisition
Data were obtained on a 3-Tesla GE scanner. 12 slices were acquired at a resolution of 1.5 x 1.5 x 3.0 mm. TR was 1,000 ms and TE was 30 ms. Slices were acquired using a two-shot T2*-sensitive spiral acquisition sequence. The flip angle was 77 degrees and the field of view was 192 mm.
MR Analysis
The MR data was analyzed using mrVista software tools.
Pre-processing
Data were pre-processed (motion corrected, slice time corrected, etc.) by Weiner and colleagues.
HRFs vs. Deconvolution
We analyzed 2 face-selective ROIs and one body part-selective ROI. Standard GLM analyses were run with several different built-in HRF functions (Boynton, Dale & Buckner, and SPM) as well as with deconvolution and results were compared and contrasted.
Multi-Voxel Patterns
Multi-voxel patterns were examined for the same ROIs.
Results
HRFs vs. Deconvolution
Face-Selective Region using Boynton HRF
Face-Selective Region using Dale & Buckner HRF
Face-Selective Region using SPM Difference-of-Gammas HRF
Face-Selective Region Using Deconvolution
Multi-Voxel Patterns
Face-Selective Region #1
Face-Selective Region #2
Face-Selective Region #3
Retinotopic models in group-averaged data projected back into native space
Some text. Some analysis. Some figures.
Conclusions
Here is where you say what your results mean.
References - Resources and related work
References
Software