Abstract

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler low-dimensional statistics is largely unknown. To explore this question, we examine resting state fMRI (rs-fMRI) data using complex topology measures from network neuroscience. We show that spatial and temporal autocorrelation are reliable statistics which explain numerous measures of network topology. Surrogate timeseries with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely-used complexity measures may help link them to neurobiology.