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bioRxiv · 10.1101/122945

Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

Powell, M.·Garcia, J.·Yeh, F.-C.·Vettel, J.·Verstynen, T.
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Abstract

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N=841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.\n\nAuthor SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.

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Powell, M., Garcia, J., Yeh, F.-C., Vettel, J., Verstynen, T.. 2017-03-31. Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors. https://doi.org/10.1101/122945

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