Fundamental Differences: A Basis Set for Characterizing Inter-Individual Variation in Resting State Connectomes

Resting state functional connectomics holds the promise of illuminating and predicting individual differences in behavioral and clinical phenotypes. To realize this goal, however, it is critical to gain greater understanding of the nature, kind, and extent of population-wide interindividual connectomic variation. We examined whole-brain resting state functional connectomes from healthy young adults from the Human Connectome Project 1200 release. We found clear evidence of low rank structure in which a modest number of connectomic components, around 50-150, account for a sizable portion of cross-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. In addition, we show that these connectomic components enable prediction of a broad array of neurocognitive and clinical symptom variables at levels comparable to a leading method that is trained on the whole connectome. Qualitative observation reveals that these connectomic components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We provide quantitative validation of this observation using novel stochastic block model-based methods. We propose that the fundamental connectivity units identified in this study form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral and clinical phenotypes.