A validation of a multi-spatialscale method for multivariate pattern analysis

Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Reg...

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Bibliographic Details
Published in2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
Main Authors Bulthe, Jessica, van den Hurk, Job, Daniels, Nicky, De Smedt, Bert, Op de Beeck, Hans P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Accuracy
Biology
Decoding
fMRI
Kernel
MVPA
Noise
Pattern analysis
ROI-based analysis
Searchlight
Support vector machines
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Summary:Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Regions Of Interest (ROI) based MVPA, and a small radius searchlight. The results of [1] clearly demonstrated the necessity of incorporating different spatial scales in MVPA analysis to draw conclusions on how the neural representations of the effects are distributed across the brain. We tested the validity of the method used in this empirical study by using three simulated fMRI datasets. Both simulated data and the real data [1] confirmed the relevance of analyzing data with MVPA on different spatial scales.
DOI:10.1109/PRNI.2014.6858513