Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function

摘要

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyper- and hypo-connectivity within the DMN, and both increased and decreased DMN coupling with the multi-modal saliency network (SN) and dorsal attention network (DAN). The present study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multi-site dataset (n=325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network structure were used to characterize healthy and patients with schizophrenia groups, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings highlight the role of the DMN core and its relation to canonical networks in schizophrenia and underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients.