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NIPS
2007

Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression

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Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression
We propose a method for reconstruction of human brain states directly from functional neuroimaging data. The method extends the traditional multivariate regression analysis of discretized fMRI data to the domain of stochastic functional measurements, facilitating evaluation of brain responses to complex stimuli and boosting the power of functional imaging. The method searches for sets of voxel time courses that optimize a multivariate functional linear model in terms of R2 statistic. Population based incremental learning is used to identify spatially distributed brain responses to complex stimuli without attempting to localize function first. Variation in hemodynamic lag across brain areas and among subjects is taken into account by voxel-wise non-linear registration of stimulus pattern to fMRI data. Application of the method on an international test benchmark for prediction of naturalistic stimuli from new and unknown fMRI data shows that the method successfully uncovers spatially d...
Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W.
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Sennay Ghebreab, Arnold W. M. Smeulders, Pieter W. Adriaans
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