Dr. Gang Chen, Mathematical Statistician,

Scientific & Statistical Computing Core (SSCC), National Institute of Mental Health

Abstract:  The conventional analytical approach in neuroimaging, massively univariate analysis, models each and every spatial element (voxel or matrix element) separately with two assumptions: 1) the voxels or elements are unrelated, and 2) no prior information is available about their effects. However, we do know that the spatial elements are related to some extent, and we do have some extent of prior knowledge about the relevant effects (e.g., BOLD response usually less than 3%). The inefficiency resulting from the first unrealistic assumption is only partially recouped through the correction for multiple comparisons, but the information waste from the second assumption fundamentally leads to the over-penalizing step of correction for multiple comparisons. In addition, dichotomous decisions through thresholding under null hypothesis significance testing are controversial in general and equally problematic in neuroimaging as well. For instance, the popular practice of only reporting “statistically significant” results in neuroimaging not only wastes data information, but also distorts the full results as well as perpetuates the reproducibility crisis because of the fact that the difference between a “significant” result and a “non-significant” one is not necessarily significant.

We believe that the heavy penalty lies in the low efficiency of univariate GLM. With the assumption that the effects associated with brain regions follow a Gaussian distribution, we build one model in which the information across brain regions is shared and regularized, resolving the issue of multiple comparisons that typically plagues the conventional statistical analysis in neuroimaging. In addition to higher modeling efficiency, the methodology provides a principled way to make statistical inferences, and allows us to emphasize the notion of full results reporting through "highlighting," instead of through the common practice of "hiding," thus minimizing loss of information while enhancing reproducibility. The modeling strategy can be applied to three types of neuroimaging data: 1) task-related FMRI experiments, 2) matrices of either correlation coefficients or DTI properties (mean diffusivity, fractional anisotropy, radial diffusivity and axial diffusivity), and 3) inter-subject correlation analysis for naturalistic scanning. The related work is elaborated in two recent manuscripts: https://www.biorxiv.org/content/early/2018/02/20/238998 and https://www.biorxiv.org/content/early/2018/11/01/459545   

Light refreshments will be served

Sponsored by the Maryland Neuroimaging Center

mnc.umd.edu