Fundamental Units of Inter-Individual Variation in Resting State Connectomes
Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50-150, account for a sizable portion of inter-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. We demonstrate that these connectomic components enable prediction of a broad array of neurocognitive and clinical variables. In addition, using stochastic block modeling-based methods, we show these components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.