Unbiased Population-Based Statistics to Obtain Pathologic Burden of Injury after Experimental TBI

Abstract

Reproducibility of scientific data is a current concern throughout the neuroscience field. There are multiple on-going efforts to help resolve this problem. Within the preclinical neuroimaging field, the continued use of a region-of interest (ROI) type approaches combined with the well-known spatial heterogeneity of traumatic brain injury pathology is a barrier to the replicability and repeatability of data. Here we propose the conjoint use of an unbiased analysis of the whole brain after injury together with a population-based statistical analysis of sham-control brains as one approach that has been used in clinical research to help resolve this issue. The approach produces two volumes of pathology that are outside the normal range of sham brains, and can be interpreted as whole brain burden of injury. Using diffusion weighted imaging derived scalars from a tensor analysis of data acquired from adult, male rats at 2, 9 days, 1 and 5 months after lateral fluid percussion injury (LFPI) and in shams (n=73 and 12, respectively), we compared a data-driven, z-score mapping method to a whole brain and white matter-specific analysis, as well as an ROI-based analysis with brain regions preselected by virtue of their large group effect sizes. We show that the data-driven approach is statistically robust, providing the advantage of a large group effect size typical of a ROI analysis of mean scalar values derived from the tensor in regions of gross injury, but without the large multi-region statistical correction required for interrogating multiple brain areas, and without the potential bias inherent with using preselected ROIs. We show that the technique correctly captures the expected longitudinal time-course of the diffusion scalar volumes based on the spatial extent of the pathology and the known temporal changes in scalar values in the LFPI model.