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DTSTART:20150101T000000
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DTSTART;TZID=UTC:20160111T160000
DTEND;TZID=UTC:20160111T170000
DTSTAMP:20260429T163702
CREATED:20160107T124743Z
LAST-MODIFIED:20160129T153930Z
UID:1238-1452528000-1452531600@wp.doc.ic.ac.uk
SUMMARY:Seminar: The past and future of Random Field Theory for neuroimaging inference
DESCRIPTION:Speaker name: Prof. Thomas E. Nichols \nAbstract: 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. \nSeminar Slides from Prof. Nichols’ Talk
URL:https://wp.doc.ic.ac.uk/hipeds/event/seminar-the-past-and-future-of-random-field-theory-for-neuroimaging-inference/
LOCATION:Huxley Building\, Room 217/218\, Imperial College London\, London\, SW7 2AZ\, United Kingdom
ORGANIZER;CN="Ira Ktena":MAILTO:ira.ktena@imperial.ac.uk
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