Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps

Ongoing large-scale human brain studies are generating complex neuroimaging data from thousands of individuals that can be leveraged to derive data-driven, anatomically accurate brain parcellations. However, despite their promise and many strengths, these data are highly heterogeneous, a characteristic that may affect the anatomical accuracy and generalization of the template but has received relatively little attention. Using multiple similarity measures and thresholding approaches, this study investigated the topological intra- and inter-individual variability of restingstate (rs) functional edge maps (often used for brain parcellation), estimated from rs-fMRI connectivity in n = 5878 children from the Adolescent Brain Cognitive Development (ABCD) study. Findings from this initial investigation indicate that choosing a subject- vs cohort-based threshold for estimating edge maps from connectivity matrices does not significantly impact the map topology. In contrast, the choice of similarity measure and non-linear relationship between similarity and edge map sparsity may have a significant impact on map classification and the generation of parcellation atlases. Multi-level classification revealed multiple clusters with a potentially complex mapping onto biological variables beyond simple demographics.Clinical Relevance- Case-control neuroimaging studies should use domain-specific (e.g., demographics-specific) atlases for parcellating the brain, to improve accuracy and rigor of cohort comparisons. To be generalizable, such atlases need to be derived from large datasets, which are inherently heterogeneous. In a cohort of 5878 children (age ~9-10 years), this study systematically assessed the impact of heterogeneity and similarity of edge maps, which are derived from rs-fMRI connectivity and typically used to generate parcellation atlases.