Abstract
Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.
Highlights
1234567890();,: INTRODUCTION Efforts to identify imaging markers for psychotic disorders have been impeded by two factors: a focus on traditionally defined psychotic syndromes that have overlapping genetic, psychological, and neurobiological features [1,2,3], and a focus on regional changes such as gray matter volume [4, 5] or brain activity [6], or altered functional connectivity between pairs of regions [7, 8], rather than comprehensively on whole-brain networks that are believed to be fundamentally involved in the pathogenesis of psychosis [9,10,11]
There were no significant differences in psychosis probands or their nonpsychotic relatives compared with healthy controls in global network properties, including global/ local efficiency, clustering coefficient, characteristic path length, normalized clustering coefficient, or normalized characteristic path length
Nonpsychotic relatives showed significantly lower nodal efficiency in right middle frontal gyrus (MFG), right orbital inferior frontal gyrus (IFG), left hippocampus, and higher nodal efficiency in the left posterior cingulate gyrus (PCG) and left thalamus compared with healthy controls
Summary
Efforts to identify imaging markers for psychotic disorders have been impeded by two factors: a focus on traditionally defined psychotic syndromes that have overlapping genetic, psychological, and neurobiological features [1,2,3], and a focus on regional changes such as gray matter volume [4, 5] or brain activity [6], or altered functional connectivity between pairs of regions [7, 8], rather than comprehensively on whole-brain networks that are believed to be fundamentally involved in the pathogenesis of psychosis [9,10,11]. Graph theory approaches based on functional connectivity and white matter connectivity have been used to characterize network graphs in patients with psychotic disorders [12, 14,15,16,17] and their relatives [18, 19]. Approaches using brain gray matter anatomy to investigate brain networks in psychotic disorders may reveal more stable phenotypes related to altered anatomical organization [23,24,25,26]. The potential importance of this type of analysis for evaluating behaviorally relevant features of brain anatomy is reflected in observations that individual variation in morphometric similarity networks can account for ~40% of the individual differences in IQ scores in healthy young people [25]
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