Imperial College London > Talks@ee.imperial > Featured talks > HiPEDS Seminar - The past and future of Random Field Theory for neuroimaging inference

HiPEDS Seminar - The past and future of Random Field Theory for neuroimaging inference

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  • UserProfessor Thomas E. Nichols, University of Warwick
  • ClockMonday 11 January 2016, 16:00-17:00
  • HouseHuxley room 217.

If you have a question about this talk, please contact Wiesia R Hsissen.

A fundamental goal in “brain mapping” with functional Magnetic Resonance Imaging (fMRI) is localising the parts of the brain activated by a task. The standard tool for making this inference has been Random Field Theory (RFT), a collection of results for Gaussian Processes of the null statistic image (implemented in the two most widely used packages, SPM & FSL ). RFT provides inference on individual voxels (voxel-wise) and sets of contiguous suprathreshold voxels (cluster-wise) while controlling the familywise error rate, the chance of one or more false positives over the brain. I will discuss how RFT methods have been used for the past 25 years, show some small-scale evaluations that pointed to problems with RFT when the degrees-of-freedom are low. I will then show results from a recent study based on the wealth of (1000’s of) publicly available resting-state fMRI datasets; these massive evaluations show that, even with n=20 or 40 subjects, RFT suffers from slightly conservative voxel-wise inferences and catastrophically liberal cluster-wise inferences. I will discuss the reasons for these failures of RFT and practical solutions going forward.

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