Background-Social cognitive ability is a significant determinant of functional outcome, and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.Objective-Using 'resting state' functional magnetic resonance imaging (rsfMRI) and a transdiagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.Methods-The study included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 45 healthy controls. All participants underwent a rsfMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis examined how each individual brain voxel's connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).Results-We identified a region in the left superior parietal lobule (SPL) where individual network topology is linked to emotional intelligence. Specifically, in high scoring individuals, this region is a node of the Default Mode Network and in low scoring individuals, it is a node of the Dorsal Attention Network. This relationship was observed in both schizophrenia and healthy comparison participants.

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

Schizophrenia, Schizoaffective, and Bipolar disorders share behavioral and phenomenological traits, intermediate phenotypes, and some associated genetic loci with pleiotropic effects. Volumetric abnormalities in brain structures are among the intermediate phenotypes consistently reported associated with these disorders. In order to examine the genetic underpinnings of these structural brain modifications, we performed genome-wide association analyses (GWAS) on 60 quantitative structural brain MRI phenotypes in a sample of 777 subjects (483 cases and 294 controls pooled together). Genotyping was performed with the Illumina PsychChip microarray, followed by imputation to the 1000 genomes multiethnic reference panel. Enlargement of the Temporal Horns of Lateral Ventricles (THLV) is associated with an intronic SNP of the gene NRXN1 (rs12467877, P = 6.76E–10), which accounts for 4.5% of the variance in size. Enlarged THLV is associated with psychosis in this sample, and with reduction of the hippocampus and enlargement of the choroid plexus and caudate. Eight other suggestively significant associations (P < 5.5E–8) were identified with THLV and 5 other brain structures. Although rare deletions of NRXN1 have been previously associated with psychosis, this is the first report of a common SNP variant of NRXN1 associated with enlargement of the THLV in psychosis.

Background: Neuroimaging of psychiatric disease is challenged by the difficulty of establishing the causal role of neuroimaging abnormalities. Lesions that cause mania present a unique opportunity to understand how brain network disruption may cause mania in both lesions and in bipolar disorder. Methods: A literature search revealed 23 case reports with imaged lesions that caused mania in patients without history of bipolar disorder. We traced these lesions and examined resting-state functional Magnetic Resonance Imaging (rsfMRI) connectivity to these lesions and control lesions to find networks that would be disrupted specifically by mania-causing lesions. The results were then used as regions-of-interest to examine rsfMRI connectivity in patients with bipolar disorder (n=16) who underwent imaging longitudinally across states of both mania and euthymia alongside a cohort of healthy participants scanned longitudinally. We then sought to replicate these results in independent cohorts of manic (n=26) and euthymic (n=21) participants with bipolar disorder. Results: Mania-inducing lesions overlap significantly in network connectivity. Mania-causing lesions selectively disrupt networks that include orbitofrontal cortex, dorsolateral prefrontal cortex, and temporal lobes. In bipolar disorder, the manic state was reflected in strong, significant, and specific disruption in network communication between these regions and regions implicated in bipolar pathophysiology: the amygdala and ventro-lateral prefrontal cortex. Limitations: There was heterogeneity in the clinical characterization of mania causing lesions. Conclusions: Lesions causing mania demonstrate shared and specific network disruptions. These disruptions are also observed in bipolar mania and suggest a convergence of multiple disorders on shared circuit dysfunction to cause mania.

Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p &lt; 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = −0.50; 95% CI −0.75 to −0.12, p &lt; 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p &lt; 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.

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29Background: Resting state fMRI (rsfMRI) demonstrates that the brain is organized into 30 distributed networks. Numerous studies have examined links between psychiatric 31 symptomatology and network functional connectivity. Traditional rsfMRI analyses 32 assume that the spatial organization of networks is invariant between individuals. This 33 dogma has recently been overturned by the demonstration that networks show 34 significant variation between individuals. We tested the hypothesis that previously 35 observed relationships between schizophrenia negative symptom severity and network 36 connectivity are actually due to individual differences in network spatial organization. 37Methods: 44 participants diagnosed with schizophrenia underwent rsfMRI scans and 38 clinical assessments. A multivariate pattern analysis determined how whole brain 39 functional connectivity correlates with negative symptom severity at the individual voxel 40 level. 41Results: Brain connectivity to a region of the right dorso-lateral pre-frontal cortex 42 correlates with negative symptom severity. This finding results from individual 43 differences in the topographic distribution of two networks: the default mode network 44 (DMN) and the task positive network (TPN). Both networks demonstrate strong (r~0.49) 45 and significant (p<0.001) relationships between topography and symptom severity. For 46 individuals with low symptom severity, this critical region is part of the DMN. In highly 47 symptomatic individuals, this region is part of the TPN. 48Conclusion: Previously overlooked individual variation in brain organization is tightly 49 linked to differences in schizophrenia symptom severity. Recognizing critical links 50 between network topography and pathological symptomology may identify key circuits 51 3 that underlie cognitive and behavioral phenotypes. Individual variation in network 52 topography likely guides different responses to clinical interventions that rely on 53 anatomical targeting (e.g. TMS). 54

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