Meeting Banner
Abstract #5480

Smooth global fMRI signals facilitate robust cross-subject classification of naturalistic movie stimuli

Hendrik Mandelkow1, Jacco de Zwart1, and Jeff Duyn1

1AMRI, LFMI, NINDS, NIH, Bethesda, MD, United States

The imprecision of anatomical alignment methods commonly limits the spatial resolution and sensitivity of conventional fMRI analysis based on statistical parametric mapping. Recently proposed machine-learning methods aim to circumvent the cross-subject (XS) alignment problem by computing a linear projection of the fMRI signal from each subject's anatomical space to a common albeit abstract "functional" space [1][2]. The success of these "hyperalignment" methods is often attributed to a spatially and functionally specific (linear) correspondence between the fMRI signal in different subjects under similar stimulation conditions. Cross-subject PCA of averaged fMRI data from repeated movie-viewing experiments reveals smooth globally distributed fMRI signal components that facilitate robust cross-subject classification by Linear Discriminant Analysis (LDA). Such global cortical network activity may contribute to the success of fMRI hyperalignment strategies.

This abstract and the presentation materials are available to members only; a login is required.

Join Here