TY - JOUR T1 - Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors JF - bioRxiv DO - 10.1101/122945 SP - 122945 AU - Michael A. Powell AU - Javier O. Garcia AU - Fang-Cheng Yeh AU - Jean M. Vettel AU - Timothy Verstynen Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/03/31/122945.abstract N2 - 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 14 out of 36 subject attributes measured, including a large set of 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 the sensitivity of the local connectome to predict both individualized and shared structural variability between subjects related to genetic and experiential factors.Author Summary The local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel, and reflects the 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 the local connectome has both the sensitivity and specificity to be used as a phenotypic marker for subject-specific attributes. ER -