1 research outputs found
Multiscale statistical testing for connectome-wide association studies in fMRI
Alterations in brain connectivity have been associated with a variety of
clinical disorders using functional magnetic resonance imaging (fMRI). We
investigated empirically how the number of brain parcels (or scale) impacted
the results of a mass univariate general linear model (GLM) on connectomes. The
brain parcels used as nodes in the connectome analysis were functionnally
defined by a group cluster analysis. We first validated that a classic
Benjamini-Hochberg procedure with parametric GLM tests did control
appropriately the false-discovery rate (FDR) at a given scale. We then observed
on realistic simulations that there was no substantial inflation of the FDR
across scales, as long as the FDR was controlled independently within each
scale, and the presence of true associations could be established using an
omnibus permutation test combining all scales. Second, we observed both on
simulations and on three real resting-state fMRI datasets (schizophrenia,
congenital blindness, motor practice) that the rate of discovery varied
markedly as a function of scales, and was relatively higher for low scales,
below 25. Despite the differences in discovery rate, the statistical maps
derived at different scales were generally very consistent in the three real
datasets. Some seeds still showed effects better observed around 50,
illustrating the potential benefits of multiscale analysis. On real data, the
statistical maps agreed well with the existing literature. Overall, our results
support that the multiscale GLM connectome analysis with FDR is statistically
valid and can capture biologically meaningful effects in a variety of
experimental conditions.Comment: 54 pages, 12 main figures, 1 main table, 10 supplementary figures, 1
supplementary tabl