Importance: The clinical heterogeneity of schizophrenia has hindered neurobiological investigations aimed at identifying neural correlates of the disorder.
Objective: To identify network-based biomarkers across the spectrum of impairment present in schizophrenia by separately evaluating individuals with deficit and nondeficit subtypes of this disorder.
Design, setting, and participants: A university hospital network-based neuroimaging study was conducted between February 1, 2007, and February 28, 2012. Participants included patients with schizophrenia (n = 128) and matched healthy controls (n = 130) from two academic centers and patients with bipolar I disorder (n = 39) and matched healthy controls (n = 43) from a third site. Patients with schizophrenia at each site in the top quartile on the proxy scale for the deficit syndrome were classified as having deficit schizophrenia and those in the bottom quartile were classified as having nondeficit schizophrenia.
Exposure: All participants underwent magnetic resonance brain imaging.
Main outcomes and measures: Network-level properties of cortical thickness were assessed in each group. Interregional cortexwide coupling was compared among the groups, and graph theoretical approaches were used to assess network density and node degree, betweenness, closeness, and eigenvector centrality.
Results: Stronger frontoparietal and frontotemporal coupling was found in patients with deficit schizophrenia compared with those with nondeficit schizophrenia (17 of 1326 pairwise relationships were significantly different, P < .05; 5% false discovery rate) and in patients with deficit schizophrenia compared with healthy controls (49 of 1326 pairwise relationships were significantly different, P < .05; 5% false discovery rate). Participants with nondeficit schizophrenia and bipolar I disorder did not show significant differences in coupling relative to those in the control groups (for both comparisons, 0 of 1326 pairwise relationships were significantly different, P > .05; 5% false discovery rate). The networks formed from patients with deficit schizophrenia demonstrated increased density of connections relative to controls and nondeficit patients (range, 0.07-0.45 in controls, 0.09-0.43 in the nondeficit group, and 0.18-0.67 in the deficit group). High centrality nodes were identified in the supramarginal, middle, and superior temporal and inferior frontal regions in deficit schizophrenia networks based on ranking of 4 centrality metrics. High centrality regions were identified as those that ranked in the top 10 in 50% or more of the thresholded networks in 3 or more of the centrality measures. Network properties were similar in patients with deficit schizophrenia from both study sites.
Conclusions and relevance: Patients with schizophrenia at one end of a spectrum show characteristic signatures of altered intracortical relationships compared with those at the other end of that spectrum, patients with bipolar I disorder, and healthy individuals. Cortical connectomic approaches can be used to reliably identify neural signatures in clinically heterogeneous groups of patients.