Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder

Abstract

Importance: Psychotic disorders (including schizophrenia, schizoaffective disorder, and psychotic bipolar disorder) are devastating illnesses characterized by breakdown in the integration of information processing. Recent advances in neuroimaging allow for the estimation of brain networks on the basis of intrinsic functional connectivity, but the specific network abnormalities in psychotic disorders are poorly understood.

Objective: To compare intrinsic functional connectivity across the cerebral cortex in patients with schizophrenia spectrum disorders or psychotic bipolar disorder and healthy controls.

Design, setting, and participants: We studied 100 patients from an academic psychiatric hospital (28 patients with schizophrenia, 32 patients with schizoaffective disorder, and 40 patients with bipolar disorder with psychosis) and 100 healthy controls matched for age, sex, race, handedness, and scan quality from December 2009 to October 2011.

Main outcomes and measures: Functional connectivity profiles across 122 regions that covered the entire cerebral cortex.

Results: Relative to the healthy controls, individuals with a psychotic illness had disruption across several brain networks, with preferential reductions in functional connectivity within the frontoparietal control network (P < .05, corrected for family-wise error rate). This functionally defined network includes portions of the dorsolateral prefrontal cortex, posteromedial prefrontal cortex, lateral parietal cortex, and posterior temporal cortex. This effect was seen across diagnoses and persisted after matching patients and controls on the basis of scan quality.

Conclusions and relevance: Our study results support the view that cortical information processing is disrupted in psychosis and provides new evidence that disruptions within the frontoparietal control network may be a shared feature across both schizophrenia and affective psychosis.

Figure 1. Functional Connectivity Correlation Matrices in Patients and Controls

Each 61 × 61 grid shows the Pearson correlation between resting blood oxygenation level–dependent activity in intrahemispheric regional pairs for controls (A) and patients (B). Regions are ordered based on their network groupings adapted from Yeo et al. Diagonal white lines represent network boundaries. DorsAttn indicates dorsal attention; L, left hemisphere; R, right hemisphere; Sal, salience; SomMot, somatomotor; and VentAttn, ventral attention.

Figure 2. Functional Connectivity Differences Between Patients and Controls

A, The 61 × 61 grid shows the differences in resting blood oxygenation level–dependent correlation between controls and patients for each intrahemispheric regional pair. Differences were obtained by an analysis of variance of z-transformed Pearson correlation values after linear regression of the effects of age, sex, race, and handedness. Regions are ordered based on their network groupings adapted from Yeo et al. Diagonal white lines represent network boundaries. B, Manhattan plot showing associated network-wide P values of psychosis-related differences in functional connectivity. The y-axis shows the −log₁₀ P values of 240 within-network regional pairs, and the x-axis shows their network positions. The horizontal red line represents the threshold of P = 1.37 × 10⁻⁵ for Bonferroni-corrected significance; the horizontal blue line represents the threshold of P = 7.8 × 10⁻³ that corresponds to the false discovery rate (q < 0.05). See Figure 1 legend for explanation of abbreviations.

Figure 3. Network Disruptions in the Distributed Regions of the Frontoparietal Control Network

A, Functional connectivity differences for the 4 lateral regions in the B component of the frontoparietal network, shown using a conventional seed-based approach. Maps are color-coded based on group differences in z-transformed Pearson correlation (controls-patients), computed between each mean regional time course and the time course at every vertex on the cortical mesh and thresholded for significant differences (P < .01 uncorrected). As above, differences were obtained after linear regression of the effects of age, sex, race, and handedness. B, Conjunction maps showing the degree of overlap in thresholded (P < .01) functional connectivity difference maps for the 5 left hemisphere regions (regions shown in A and PFCmp) within the control B network shown on lateral (left) and medial (right) inflated views of the cerebral cortex. Green and white lines indicate boundaries of control A and control B regions, respectively. C, Functional connectivity matrix for the 14 left and right hemisphere regions of the frontoparietal control network. Bold text indicates control B regions. Diagonal lines represent boundaries among the A, B, and C components of the frontoparietal control network. CingC indicates the C component of the cingulate gyrus; IPL, inferior parietal lobule; IPS, intraparietal sulcus; OFC, orbitofrontal cortex; PFCd, dorsal prefrontal cortex; PFCla, lateral anterior prefrontal cortex; PFClp, lateral posterior prefrontal cortex; PFCmp, medial posterior prefrontal cortex; PostTemp, posterior temporal; Precun, precuneus; and Temp, temporal.

Figure 4. Spatial Network Model of 3 Cortical Association Networks in Psychosis

Spring-loaded graphs showing selected nodes of the frontoparietal control network, dorsal attention network, and default network in controls (A) and patients (B). Node size is based on nodal degree; edge connection strength is represented by grayscale value and line thickness. Controls had a more segregated pattern clustering of frontoparietal and default networks (represented with nonoverlapping colored halos); by contrast, patients had less clustering within default and frontoparietal control networks and evidence of extension of frontoparietal nodes into the default cluster (represented with blended red-orange halos). FEF indicates frontal eye fields; InfParOcc, inferior parieto-occipital; IPLa, lateral inferior parietal lobule; IPLp, posterior inferior parietal lobule; PCC, posterior cingulate; pCUN, precuneus; PFCdA, dorsal anterior prefrontal cortex; PFCdB, B component of dorsal prefrontal cortex; PFCl, lateral prefrontal cortex; PFCm, medial prefrontal cortex; PFCmpA, A component of medial posterior prefrontal cortex; PFCmpB, B component of medial posterior prefrontal cortex; PostC, postcentral gyrus; PostTempOcc, posterior temporal occipital; PrCv, ventral precentral gyrus; SupPar, superior parietal lobule; Temp, temporal cortex; TempA, A component of temporal cortex; TempB, B component of temporal cortex. See Figure 3 legend for explanation of other abbreviations.

Figure 5. Equivalent Disruption of Frontoparietal Control Network Connectivity in Bipolar Disorder and Schizophrenia

Functional connectivity difference matrices for the 14 left and right hemisphere regions of the frontoparietal control network shown for schizophrenic patients relative to controls (A), bipolar patients relative to controls (B), and schizophrenic patients relative to bipolar patients (C). Differences significant at false discovery rate q < 0.05 are shown in each panel just to the lower right of the unthresholded matrix. D, Histograms show the mean correlation between components of the frontoparietal control network in controls and patients with bipolar disorder or schizophrenia. Error bars denote SE. See Figure 3 and Figure 4 captions for explanation of other abbreviations.