Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools.

Source: Magnetic Resonance Imaging - February 3, 2018 Category: Radiology Authors: Ravi Bansal, Bradley S. Peterson Tags: Original contribution Source Type: research