Beyond Artifacts: Rethinking Motion-Related Signals in Resting-State fMRI Analysis

Resting-state functional magnetic resonance imaging (rsfMRI) plays a pivotal role in estimating intrinsic brain functional connectivity within healthy and clinical populations. However, the pervasive impact of head motion confounds the interpretation of rsfMRI data and is typically addressed through preprocessing without further exploration. This investigation aims to scrutinize the intricate interplay between head motion and neurobiologically relevant BOLD signal as well as its potential clinical implications. Here, we use independent component analysis (ICA) to extract large-scale brain networks from BOLD fMRI and modeled head motion time series for 508 subjects sourced from three major psychosis projects. Our approach uncovers the presence of latent network information within modeled head motion data. Moreover, we find altered functional network connectivity (FNC) between healthy controls (HC) and individuals with schizophrenia (SZ) for BOLD and motion networks, revealing that projections of BOLD time series onto network features extracted from head motion data reflect cohort-specific information. Our approach challenges conventional perspectives by treating motion-related signals not as mere noise, but as potential repositories of valuable insights into functional brain connectivity across diverse populations.