Better techniques for untangling correlations in big fMRI data

2016

Challenge

Functional Magnetic Resonance Imaging (fMRI) has revolutionized our ability to measure and assess brain activity. However, when researchers try to compare fMRI data collected from subjects in naturalistic conditions (such as while watching a movie or listening to music), current methods produce false positive rates so high that the validity of these methods is called into question.

Advance

In part 1 of this two-part study, IRP researchers led by Robert W. Cox, Ph.D., identified ‘subject-wise bootstrapping’ (SWB) as the best nonparametric method for making inter-subject correlation inferences within a single group, while ‘subject-wise permutation’ (SWP) was found to be both more reliable and required far less computation than the cumbersome method traditionally used to define statistics of the inter-subject correlations in two groups. In part 2, researchers identified a parametric approach—linear mixed-effects modeling (LME)—that is more efficient, more adaptable, easier to use, and more robust than traditional nonparametric methods.

Impact

This comprehensive upgrade of modeling and analysis techniques for fMRI data provides both theoretical and practical help to scientists in many different fields who manage big datasets as part of their research. In order to further advance the field, Dr. Cox's team also offers researchers an open source option in the AFNI (Analysis of Functional NeuroImages) suite, where they can access programs that process, analyze and display fMRI data.

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