Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BACKGROUND

Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD.

METHODS

Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability.

RESULTS

Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01).

CONCLUSIONS

Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Evidence for cytokine dysregulation in schizophrenia spectrum disorders: A comparison of cerebrospinal fluid and blood samples

Abnormalities in immune function have been described in schizophrenia but few studies have investigated cytokines in cerebrospinal fluid (CSF) and their correlation with blood levels. In this cross-sectional study, cytokines were measured in CSF and plasma of 30 subjects with schizophrenia spectrum disorder (SSD) diagnosis and 23 healthy volunteers (HV). Results showed that CSF TNFα was increased in SSD subjects compared to HV and there were no correlations between CSF and plasma cytokine levels. The present findings provide evidence of dysregulation of TNFα in CSF of schizophrenia. These results identify elevated CSF TNFα levels as a potential biomarker in schizophrenia.

Bayesian Optimization of Neurostimulation (BOONStim)

BACKGROUND

Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed.

OBJECTIVE

The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline.

METHODS

BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization.

RESULTS

Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time.

CONCLUSIONS

BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Replication of a neuroimaging biomarker for striatal dysfunction in psychosis

To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.

Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls

BACKGROUND AND HYPOTHESIS

Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s).

STUDY DESIGN

We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD.

STUDY RESULTS

Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes.

CONCLUSIONS

The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis

OBJECTIVE

Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.

METHODS

In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.

RESULTS

The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.

CONCLUSIONS

This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography

BACKGROUND

Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.

METHODS

We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).

RESULTS

The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected).

CONCLUSION

Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders

In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n = 17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.

Effects of phase encoding direction on test-retest reliability of human functional connectome

The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.

Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization

Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder

Objective

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies.

Methods

In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci.

Results

Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation.

Conclusions

Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

Contributions of Parasympathetic Arousal-Related Activity to Cognitive Performance in Patients With First-Episode Psychosis and Control Subjects

BACKGROUND

Cognitive impairment is integral to the pathophysiology of psychosis. Recent findings implicate autonomic arousal-related activity in both momentary fluctuations and individual differences in cognitive performance. Although altered autonomic arousal is common in patients with first-episode psychosis (FEP), its contribution to cognitive performance is unknown.

METHODS

A total of 24 patients with FEP (46% male, age = 24.31 [SD 4.27] years) and 24 control subjects (42% male, age = 27.06 [3.44] years) performed the Multi-Source Interference Task in-scanner with simultaneous pulse oximetry. First-level models included the cardiac-blood oxygen level-dependent regressor, in addition to task (congruent, interference, and error) and nuisance (motion and CompCor physiology) regressors. The cardiac-blood oxygen level-dependent regressor reflected parasympathetic arousal-related activity and was created by convolving the interbeat interval at each heartbeat with the hemodynamic response function. Group models examined the effect of group or cognitive performance (reaction times × error rate) on arousal-related and task activity, while controlling for sex, age, and framewise displacement.

RESULTS

Parasympathetic arousal-related activity was robust in both groups but localized to different regions for patients with FEP and healthy control subjects. Within both groups, arousal-related activity was significantly associated with cognitive performance across occipital and temporal cortical regions. Greater arousal-related activity in the bilateral prefrontal cortex (Brodmann area 9) was related to better performance in healthy control subjects but not patients with FEP.

CONCLUSIONS

Autonomic arousal circuits contribute to cognitive performance and the pathophysiology of FEP. Arousal-related functional activity is a novel indicator of cognitive ability and should be incorporated into neurobiological models of cognition in psychosis.

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call "meta-loci", showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System

Importance

Serious mental illnesses, including schizophrenia, bipolar disorder, and depression, are heritable, highly multifactorial disorders and major causes of disability worldwide.

Objective

To benchmark the penetrance of current neuropsychiatric polygenic risk scores (PRSs) in the Veterans Health Administration health care system and to explore associations between PRS and broad categories of human disease via phenome-wide association studies.

Design, Setting, and Participants

Extensive Veterans Health Administration's electronic health records were assessed from October 1999 to January 2021, and an embedded cohort of 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder were found. The performance of schizophrenia, bipolar disorder, and major depression PRSs were compared in participants of African or European ancestry in the Million Veteran Program (approximately 400 000 individuals), and associations between PRSs and 1650 disease categories based on ICD-9/10 billing codes were explored. Last, genomic structural equation modeling was applied to derive novel PRSs indexing common and disorder-specific genetic factors. Analysis took place from January 2021 to January 2022.

Main Outcomes and Measures

Diagnoses based on in-person structured clinical interviews were compared with ICD-9/10 billing codes. PRSs were constructed using summary statistics from genome-wide association studies of schizophrenia, bipolar disorder, and major depression.

Results

Of 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years) and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Among 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder, 8962 (95.6%) were correctly identified using ICD-9/10 codes (2 or more). Among those of European ancestry, PRSs were robustly associated with having received a diagnosis of schizophrenia (odds ratio [OR], 1.81 [95% CI, 1.76-1.87]; P < 10-257) or bipolar disorder (OR, 1.42 [95% CI, 1.39-1.44]; P < 10-295). Corresponding effect sizes in participants of African ancestry were considerably smaller for schizophrenia (OR, 1.35 [95% CI, 1.29-1.42]; P < 10-38) and bipolar disorder (OR, 1.16 [95% CI, 1.11-1.12]; P < 10-10). Neuropsychiatric PRSs were associated with increased risk for a range of psychiatric and physical health problems.

Conclusions and Relevance

Using diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRSs, the validity of an electronic health records-based phenotyping approach in US veterans was demonstrated, highlighting the potential of PRSs for disentangling biological and mediated pleiotropy.

Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study

Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).

A longitudinal multi-scanner multimodal human neuroimaging dataset

Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Effect of Body Position and Size of Pleural Effusion on Oxygenation Status in Spontaneously Breathing Patients with Unilateral Pleural Effusion

Abvims and Dr Rml Hospital, Delhi In unilateral pleural effusion it is necessary to find the best position for unilateral pleural effusion so that recovery can be hastened. There is scarcity of literature on the effect of body positional variations on oxygenation, and current guidelines do not address the necessity. The objective of this study is to assess the effect of body position and size of pleural effusion on oxygenation status in spontaneously breathing patients with unilateral pleural effusion.

MATERIAL

This was a hospital based observational cross-sectional study having a sample size of 90 patients of unilateral pleural effusion and on the basis of severity, they were divided into 2 classes- small and large. Ipsilateral and Contralateral oxygenation were analysed separately in small and large effusions using Mann-Whitney and Wilcoxon signed rank test and final analysis was made using SPSS software.

OBSERVATION

It was observed that oxygenation was better when patient lies on contralateral side in small effusion (PaO2 82.4±8.83 vs 85.01±8.24 p value &lt;0.0001) while in cases of large effusion, oxygenation was better on ipsilateral side of effusion (82.35±10.4 vs 78.06±9.92, p value&lt;0.0001). No significant difference was noted in case of PaCO2 levels in small and large effusion in ipsilateral and contralateral position. It was also noted that there was a significant difference in oxygen saturation.

CONCLUSION

There is significant effect of body position and size of pleural effusion on oxygenation status in spontaneously breathing patient with unilateral pleural effusion.

Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders

BACKGROUND

Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.

METHODS

We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH.

RESULTS

Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10-8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10-6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10-7; rs73033497, p = 8.8 × 10-7; rs7914279, p = 6.4 × 10-7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10-7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10-7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10-7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05).

CONCLUSIONS

In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

ECT-induced cognitive side effects are associated with hippocampal enlargement

Electroconvulsive therapy (ECT) is of the most effective treatments available for treatment-resistant depression, yet it is underutilized in part due to its reputation of causing cognitive side effects in a significant number of patients. Despite intensive neuroimaging research on ECT in the past two decades, the underlying neurobiological correlates of cognitive side effects remain elusive. Because the primary ECT-related cognitive deficit is memory impairment, it has been suggested that the hippocampus may play a crucial role. In the current study, we investigated 29 subjects with longitudinal MRI and detailed neuropsychological testing in two independent cohorts (N = 15/14) to test if volume changes were associated with cognitive side effects. The two cohorts underwent somewhat different ECT study protocols reflected in electrode placements and the number of treatments. We used longitudinal freesurfer algorithms (6.0) to obtain a bias-free estimate of volume changes in the hippocampus and tested its relationship with neurocognitive score changes. As an exploratory analysis and to evaluate how specific the effects were to the hippocampus, we also calculated this relationship in 41 other areas. In addition, we also analyzed cognitive data from a group of healthy volunteers (N = 29) to assess practice effects. Our results supported the hypothesis that hippocampus enlargement was associated with worse cognitive outcomes, and this result was generalizable across two independent cohorts with different diagnoses, different electrode placements, and a different number of ECT sessions. We found, in both cohorts, that treatment robustly increased the volume size of the hippocampus (Cohort 1: t = 5.07, Cohort 2: t = 4.82; p < 0.001), and the volume increase correlated with the neurocognitive T-score change. (Cohort 1: r = -0.68, p = 0.005; Cohort 2: r = -0.58; p = 0.04). Overall, our research indicates that novel treatment methods serving to avoid hippocampal volume increase may result in a better side effect profile.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

Transcranial Magnetic Stimulation for Positive Symptoms in Schizophrenia: A Systematic Review

Transcranial magnetic stimulation (TMS) has been proposed as a potential treatment add-on for positive symptoms in schizophrenia. To summarize the current evidence for its efficacy, we reviewed clinical trials from the last 20 years that investigated TMS for positive symptoms. We performed a search on the PubMed database for clinical trials that used TMS for the treatment of positive symptoms published in peer-reviewed journals. We excluded reviews, case reports, and opinion papers. Of the 30 studies included, the majority (n = 25) investigated auditory verbal hallucinations. Twelve studies found evidence for a positive treatment effect of TMS on positive symptoms, while 18 did not find enough evidence to conclude that TMS is effective for positive symptoms. However, the small sample size of the majority of studies is a limiting factor for the reliability of previous findings. In conclusion, evidence for an effect of TMS on positive symptoms was mixed. Since most of the studies were performed in patients with auditory verbal hallucinations, further research of TMS for other positive symptoms including thought disorder and delusions is warranted.

Elucidating the relationship between white matter structure, demographic, and clinical variables in schizophrenia-a multicenter harmonized diffusion tensor imaging study

White matter (WM) abnormalities are repeatedly demonstrated across the schizophrenia time-course. However, our understanding of how demographic and clinical variables interact, influence, or are dependent on WM pathologies is limited. The most well-known barriers to progress are heterogeneous findings due to small sample sizes and the confounding influence of age on WM. The present study leverages access to the harmonized diffusion magnetic-resonance-imaging data and standardized clinical data from 13 international sites (597 schizophrenia patients (SCZ)). Fractional anisotropy (FA) values for all major WM structures in patients were predicted based on FA models estimated from a healthy population (n = 492). We utilized the deviations between predicted and real FA values to answer three essential questions. (1) "Which clinical variables explain WM abnormalities?". (2) "Does the degree of WM abnormalities predict symptom severity?". (3) "Does sex influence any of those relationships?". Regression and mediator analyses revealed that a longer duration-of-illness is associated with more severe WM abnormalities in several tracts. In addition, they demonstrated that a higher antipsychotic medication dose is related to more severe corpus callosum abnormalities. A structural equation model revealed that patients with more WM abnormalities display higher symptom severity. Last, the results exhibited sex-specificity. Males showed a stronger association between duration-of-illness and WM abnormalities. Females presented a stronger association between WM abnormalities and symptom severity, with IQ impacting this relationship. Our findings provide clear evidence for the interaction of demographic, clinical, and behavioral variables with WM pathology in SCZ. Our results also point to the need for longitudinal studies, directly investigating the casualty and sex-specificity of these relationships, as well as the impact of cognitive resiliency on structure-function relationships.

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Pramipexole to Improve Cognition in Bipolar Disorder: A Randomized Controlled Trial

BACKGROUND

Adults with bipolar disorder (BD) often experience neurocognitive impairment that negatively impacts functioning and quality of life. Previous trials have found that dopamine agonist agents improve cognition in healthy volunteers and that adults with BD who have stable mood and mild cognitive deficits may also benefit. We hypothesized that pramipexole, a dopamine agonist, would improve neurocognitive function in patients with BD.

METHODS

We recruited 60 adults (aged 18-65 years) with a diagnosis of BD I or II for an 8-week, double-blind, placebo-controlled trial (NCT02397837). All had stable mood and clinically significant neurocognitive impairment at baseline. Participants were randomized to receive pramipexole (n = 31) or a placebo (n = 29), dose was initiated at 0.125 mg 2 times a day and increased to a target of 4.5 mg/d.

RESULTS

At trial end, the primary outcome, MATRICS Consensus Cognitive Battery composite score, had not improved more in the pramipexole group (mean [SD] = 1.15 [5.4]) than in the placebo group (mean [SD] = 4.12 [5.2], Cohen's d = 0.56, P = 0.049), and mixed models, controlling for symptoms, showed no association between treatment group and MATRICS Consensus Cognitive Battery scores. No serious adverse events were reported.

CONCLUSIONS

These results suggest that pramipexole is not an efficacious cognitive enhancement agent in BD, even in a sample enriched for characteristics that were associated with a beneficial response in prior work. There are distinct cognitive subgroups among adults with BD and may be related differences in neurobiology that affect response to pramipexole. Additional research to better understand the onset and nature of the cognitive deficits in people with BD will be an important step toward a more personalized approach to treatment.

Novel ultra-rare exonic variants identified in a founder population implicate cadherins in schizophrenia

The identification of rare variants associated with schizophrenia has proven challenging due to genetic heterogeneity, which is reduced in founder populations. In samples from the Ashkenazi Jewish population, we report that schizophrenia cases had a greater frequency of novel missense or loss of function (MisLoF) ultra-rare variants (URVs) compared to controls, and the MisLoF URV burden was inversely correlated with polygenic risk scores in cases. Characterizing 141 "case-only" genes (MisLoF URVs in ≥3 cases with none in controls), the cadherin gene set was associated with schizophrenia. We report a recurrent case mutation in PCDHA3 that results in the formation of cytoplasmic aggregates and failure to engage in homophilic interactions on the plasma membrane in cultured cells. Modeling purifying selection, we demonstrate that deleterious URVs are greatly overrepresented in the Ashkenazi population, yielding enhanced power for association studies. Identification of the cadherin/protocadherin family as risk genes helps specify the synaptic abnormalities central to schizophrenia.

A 16-week randomized placebo-controlled trial investigating the effects of omega-3 polyunsaturated fatty acid treatment on white matter microstructure in recent-onset psychosis patients concurrently treated with risperidone

We examined the impact of treatment with fish oil (FO), a rich source of omega-3 polyunsaturated fatty acids (n-3 PUFA), on white matter in 37 recent-onset psychosis patients receiving risperidone in a double-blind placebo-controlled randomized clinical trial. Patients were scanned at baseline and randomly assigned to receive 16-weeks of treatment with risperidone + FO or risperidone + placebo. Eighteen patients received follow-up MRIs (FO, n = 10/Placebo, n = 8). Erythrocyte levels of n-3 PUFAs eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and docosapentaenoic acid (DPA) were obtained at both time points. We employed Free Water Imaging metrics representing the extracellular free water fraction (FW) and fractional anisotropy of the tissue (FA-t). Analyses were conducted using Tract-Based-Spatial-Statistics and nonparametric permutation-based tests with family-wise error correction. There were significant positive correlations of FA-t with DHA and DPA among all patients at baseline. Patients treated with risperidone + placebo demonstrated reductions in FA-t and increases in FW, whereas patients treated with risperidone + FO exhibited no significant changes in FW and FA-t reductions were largely attenuated. The correlations of DPA and DHA with baseline FA-t support the hypothesis that n-3 PUFA intake or biosynthesis are associated with white matter abnormalities in psychosis. Adjuvant FO treatment may partially mitigate against white matter alterations observed in recent-onset psychosis patients following risperidone treatment.

Towards a framework to develop neuroimaging biomarkers of relapse in schizophrenia

Schizophrenia is a chronic disorder that often requires long-term relapse-prevention treatment. This treatment is effective for most individuals, yet approximately 20-30 % of them may still relapse despite confirmed adherence. Alternatively, for about 15 % it may be safe to discontinue medications over the long term, but since there are no means to identify who those individuals will be, the recommendation is that all individuals receive long-term relapse-prevention treatment with antipsychotic maintenance. Thus, the current approach to prevent relapse in schizophrenia may be suboptimal for over one third of individuals, either by being insufficient to protect against relapse, or by unnecessarily exposing them to medication side effects. There is great need to identify biomarkers of relapse in schizophrenia to stratify treatment according to the risk and develop therapeutics targeting its pathophysiology. In order to develop a line of research that meets those needs, it is necessary to create a framework by identifying the challenges to this type of study as well as potential areas for biomarker identification and development. In this manuscript we review the literature to create such a framework.

History of childhood maltreatment is associated with reduced fractional anisotropy of the accumbofrontal 'reward' tract in healthy adults

The deleterious outcomes associated with exposure to childhood maltreatment (CM) are well known and may be at least partially mediated by self-harm behaviors. It has been suggested that these self-harm behaviors serve as a means of decreasing negative mood states but the effects of CM on health outcomes may be much more sinister. A wealth of data suggest that CM may lead to experience-dependent changes in neural circuits underlying reward processes; processes associated with many harmful behaviors. The present study examined the relationship between a history of CM and the microstructure of a white matter tract that may be central to reward processes. Healthy adults (N = 122) were assessed with a diffusion tensor imaging (DTI) exam and the Childhood Trauma Questionnaire (CTQ). Probabilistic tractography was used to delineate the accumbofrontal "reward" tract, connecting the orbitofrontal cortex and nucleus accumbens, and measures of white matter microstructure were extracted. We then examined whether variation in CTQ scores were associated with variation in the microstructure of this tract as measured by fractional anisotropy (FA). After accounting for the effects of age and sex, the CTQ total score accounted for approximately 6% of the variance of FA in the accumbofrontal tract (F(3, 121) = 5.74; p = .001). Post hoc analyses indicated that the overall severity of CM, rather than a specific type of maltreatment, drove this result. These findings indicate that CM influences white matter microstructure in a fiber tract that is likely central to reward processes and adds to a growing literature implicating CM in long-term health-related outcomes.

Social Cognitive Networks and Social Cognitive Performance Across Individuals With Schizophrenia Spectrum Disorders and Healthy Control Participants

BACKGROUND

Schizophrenia spectrum disorders (SSDs) feature social cognitive deficits, although their neural basis remains unclear. Social cognitive performance may relate to neural circuit activation patterns more than to diagnosis, which would have important prognostic and therapeutic implications. The current study aimed to determine how functional connectivity within and between social cognitive networks relates to social cognitive performance across individuals with SSDs and healthy control participants.

METHODS

Participants with SSDs (n = 164) and healthy control participants (n = 117) completed the Empathic Accuracy task during functional magnetic resonance imaging as well as lower-level (e.g., emotion recognition) and higher-level (e.g., theory of mind) social cognitive measures outside the scanner. Functional connectivity during the Empathic Accuracy task was analyzed using background connectivity and graph theory. Data-driven social cognitive networks were identified across participants. Regression analyses were used to examine network connectivity-performance relationships across individuals. Positive and negative within- and between-network connectivity strengths were also compared in poor versus good social cognitive performers and in SSD versus control groups.

RESULTS

Three social cognitive networks were identified: motor resonance, affect sharing, and mentalizing. Regression and group-based analyses demonstrated reduced between-network negative connectivity, or segregation, and greater within- and between-network positive connectivity in worse social cognitive performers. There were no significant effects of diagnostic group on within- or between-network connectivity.

CONCLUSIONS

These findings suggest that the neural circuitry of social cognitive performance may exist dimensionally. Across participants, better social cognitive performance was associated with greater segregation between social cognitive networks, whereas poor versus good performers may compensate via hyperconnectivity within and between social cognitive networks.

White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study

Several prominent theories of schizophrenia suggest that structural white matter pathologies may follow a developmental, maturational, and/or degenerative process. However, a lack of lifespan studies has precluded verification of these theories. Here, we analyze the largest sample of carefully harmonized diffusion MRI data to comprehensively characterize age-related white matter trajectories, as measured by fractional anisotropy (FA), across the course of schizophrenia. Our analysis comprises diffusion scans of 600 schizophrenia patients and 492 healthy controls at different illness stages and ages (14-65 years), which were gathered from 13 sites. We determined the pattern of age-related FA changes by cross-sectionally assessing the timing of the structural neuropathology associated with schizophrenia. Quadratic curves were used to model between-group FA differences across whole-brain white matter and fiber tracts at each age; fiber tracts were then clustered according to both the effect-sizes and pattern of lifespan white matter FA differences. In whole-brain white matter, FA was significantly lower across the lifespan (up to 7%; p < 0.0033) and reached peak maturation younger in patients (27 years) compared to controls (33 years). Additionally, three distinct patterns of neuropathology emerged when investigating white matter fiber tracts in patients: (1) developmental abnormalities in limbic fibers, (2) accelerated aging and abnormal maturation in long-range association fibers, (3) severe developmental abnormalities and accelerated aging in callosal fibers. Our findings strongly suggest that white matter in schizophrenia is affected across entire stages of the disease. Perhaps most strikingly, we show that white matter changes in schizophrenia involve dynamic interactions between neuropathological processes in a tract-specific manner.

Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world's population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572.

METHODS

We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date.

RESULTS

Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10-30) and African American (P < .0005) participants in CSP #572.

CONCLUSIONS

We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

Psychosis relapse during treatment with long-acting injectable antipsychotics in individuals with schizophrenia-spectrum disorders: an individual participant data meta-analysis

BACKGROUND

Most individuals with schizophrenia-spectrum disorders have relapses, which increase the risk of morbidity and mortality. Because non-adherence to antipsychotic maintenance treatment could affect more than half of individuals with schizophrenia-spectrum disorders, psychosis relapse can often be confounded by unnoticed treatment interruption. Research of relapse during confirmed antipsychotic exposure has basic clinical and neurobiological implications, but data are scarce. We aimed to generate reliable estimates of incidence and predictors of relapse during assured antipsychotic treatment.

METHODS

We did a systematic review and individual participant data (IPD) meta-analysis of clinical trials of long-acting injectable antipsychotics (LAIs) for psychosis relapse-prevention, following IPD-PRISMA guidelines. Datasets were identified by searching relevant repositories from inception to Aug 1, 2019. Each LAI group was reanalysed as a separate cohort, further identifying subcohorts of individuals with and without prospectively determined symptom remission (PSR). Summary incidence rate of relapse, incidence rate ratios (IRRs) of relapse between individuals with and without PSR, hazard ratios (HRs) of covariates on risk of relapse, and standardised mean difference (SMDs) in changes in overall functioning associated with relapse were generated by pooling results from the harmonised reanalysis of each study. This study is registered with PROSPERO, number CRD42019137439.

FINDINGS

19 treatment cohorts consisting of 5130 individuals (2938 with PSR, 2192 without PSR), with 3959·53 observed participant-years, were meta-analysed. Pooled incidence of relapse was 22·97 per 100 participant-years (14·76 per 100 participant-years for the PSR subcohort, 31·51 per 100 participant-years for the non-PSR subcohort), with an IRR of 0·19 (95% CI 0·07 to 0·54). Relapse was associated with functional decline (overall SMD -0·76, 95% CI -1·14 to -0·37; PSR SMD -0·52, 95% CI -0·80 to -0·21; non-PSR SMD -0·72, 95% CI -1·18 to -0·26). The strongest predictor of relapse was tardive dyskinesia at treatment onset (HR 2·39, 95% CI 1·05 to 5·42).

INTERPRETATION

Despite the established efficacy of antipsychotics in preventing relapse, these data indicate that these drugs might not prevent subsequent exacerbations for a proportion of individuals whose illness is stabilised on continuous antipsychotic treatment. Tardive dyskinesia in particular might have pathophysiological implications for relapse.

FUNDING

Northwell Health.

A new genetic locus for antipsychotic-induced weight gain: A genome-wide study of first-episode psychosis patients using amisulpride (from the OPTiMiSE cohort)

BACKGROUND

Antipsychotic-induced weight gain is a common and debilitating side effect of antipsychotics. Although genome-wide association studies of antipsychotic-induced weight gain have been performed, few genome-wide loci have been discovered. Moreover, these genome-wide association studies have included a wide variety of antipsychotic compounds.

AIMS

We aim to gain more insight in the genomic loci affecting antipsychotic-induced weight gain. Given the variable pharmacological properties of antipsychotics, we hypothesized that targeting a single antipsychotic compound would provide new clues about genomic loci affecting antipsychotic-induced weight gain.

METHODS

All subjects included for this genome-wide association study (n=339) were first-episode schizophrenia spectrum disorder patients treated with amisulpride and were minimally medicated (defined as antipsychotic use <2 weeks in the previous year and/or <6 weeks lifetime). Weight gain was defined as the increase in body mass index from before until approximately 1 month after amisulpride treatment.

RESULTS

Our genome-wide association analyses for antipsychotic-induced weight gain yielded one genome-wide significant hit (rs78310016; β=1.05; p=3.66 × 10^-08; n=206) in a locus not previously associated with antipsychotic-induced weight gain or body mass index. Minor allele carriers had an odds ratio of 3.98 (p=1.0 × 10^-03) for clinically meaningful antipsychotic-induced weight gain (⩾7% of baseline weight). In silico analysis elucidated a chromatin interaction with 3-Hydroxy-3-Methylglutaryl-CoA Synthase 1. In an attempt to replicate single-nucleotide polymorphisms previously associated with antipsychotic-induced weight gain, we found none were associated with amisulpride-induced weight gain.

CONCLUSION

Our findings suggest the involvement of rs78310016 and possibly 3-Hydroxy-3-Methylglutaryl-CoA Synthase 1 in antipsychotic-induced weight gain. In line with the unique binding profile of this atypical antipsychotic, our findings furthermore hint that biological mechanisms underlying amisulpride-induced weight gain differ from antipsychotic-induced weight gain by other atypical antipsychotics.

Genome-wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder

Cognitive impairment is a frequent and serious problem in patients with various forms of severe mental illnesses (SMI), including schizophrenia (SZ) and bipolar disorder (BP). Recent research suggests genetic links to several cognitive phenotypes in both SMI and in the general population. Our goal in this study was to identify potential genomic signatures of cognitive functioning in veterans with severe mental illness and compare them to previous findings for cognition across different populations. Veterans Affairs (VA) Cooperative Studies Program (CSP) Study #572 evaluated cognitive and functional capacity measures among SZ and BP patients. In conjunction with the VA Million Veteran Program, 3,959 European American (1,095 SZ, 2,864 BP) and 2,601 African American (1,095 SZ, 2,864 BP) patients were genotyped using a custom Affymetrix Axiom Biobank array. We performed a genome-wide association study of global cognitive functioning, constructed polygenic scores for SZ and cognition in the general population, and examined genetic correlations with 2,626 UK Biobank traits. Although no single locus attained genome-wide significance, observed allelic effects were strongly consistent with previous studies. We observed robust associations between global cognitive functioning and polygenic scores for cognitive performance, intelligence, and SZ risk. We also identified significant genetic correlations with several cognition-related traits in UK Biobank. In a diverse cohort of U.S. veterans with SZ or BP, we demonstrate broad overlap of common genetic effects on cognition in the general population, and find that greater polygenic loading for SZ risk is associated with poorer cognitive performance.

Electroconvulsive Therapy Augmentation in Clozapine-Resistant Schizophrenia: A Prospective, Randomized Study

(Reprinted with permission from Am J Psychiatry 2015; 172:52-58).

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Interaction of Cannabis Use Disorder and Striatal Connectivity in Antipsychotic Treatment Response

Antipsychotic (AP) medications are the mainstay for the treatment of schizophrenia spectrum disorders (SSD), but their efficacy is unpredictable and widely variable. Substantial efforts have been made to identify prognostic biomarkers that can be used to guide optimal prescription strategies for individual patients. Striatal regions involved in salience and reward processing are disrupted as a result of both SSD and cannabis use, and research demonstrates that striatal circuitry may be integral to response to AP drugs. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the relationship between a history of cannabis use disorder (CUD) and a striatal connectivity index (SCI), a previously developed neural biomarker for AP treatment response in SSD. Patients were part of a 12-week randomized, double-blind controlled treatment study of AP drugs. A sample of 48 first-episode SSD patients with no more than 2 weeks of lifetime exposure to AP medications, underwent a resting-state fMRI scan pretreatment. Treatment response was defined a priori as a binary (response/nonresponse) variable, and a SCI was calculated in each patient. We examined whether there was an interaction between lifetime CUD history and the SCI in relation to treatment response. We found that CUD history moderated the relationship between SCI and treatment response, such that it had little predictive value in SSD patients with a CUD history. In sum, our findings highlight that biomarker development can be critically impacted by patient behaviors that influence neurobiology, such as a history of CUD.

Parasympathetic arousal-related cortical activity is associated with attention during cognitive task performance

Parasympathetic arousal is associated with states of heightened attention and well-being. Arousal may affect widespread cortical and subcortical systems across the brain, however, little is known about its influence on cognitive task processing and performance. In the current study, healthy adult participants (n ​= ​20) underwent multi-band echo-planar imaging (TR ​= ​0.72 ​s) with simultaneous pulse oximetry recordings during performance of the Multi Source Interference Task (MSIT), the Oddball Task (OBT), and during rest. Processing speed on both tasks was robustly related to heart rate (HR). Participants with slower HR responded faster on both the MSIT (33% variance explained) and the OBT (25% variance explained). Within all participants, trial-to-trial fluctuations in processing speed were robustly related to the heartbeat-stimulus interval, a metric that is dependent both on the concurrent HR and the stimulus timing with respect to the heartbeat. Models examining the cardiac-BOLD response revealed that a distributed set of regions showed arousal-related activity that was distinct for different task conditions. Across these cortical regions, activity increased with slower HR. Arousal-related activity was distinct from task-evoked activity and it was robust to the inclusion of additional physiological nuisance regressors into the models. For the MSIT, such arousal-related activity occurred across visual and dorsal attention network regions. For the OBT, this activity occurred within fronto-parietal regions. For rest, arousal-related activity also occurred, but was confined to visual regions. The pulvinar nucleus of the thalamus showed arousal-related activity during all three task conditions. Widespread cortical activity, associated with increased parasympathetic arousal, may be propagated by thalamic circuits and contributes to improved attention. This activity is distinct from task-evoked activity, but affects cognitive performance and therefore should be incorporated into neurobiological models of cognition and clinical disorders.

Electric field causes volumetric changes in the human brain

Recent longitudinal neuroimaging studies in patients with electroconvulsive therapy (ECT) suggest local effects of electric stimulation (lateralized) occur in tandem with global seizure activity (generalized). We used electric field (EF) modeling in 151 ECT treated patients with depression to determine the regional relationships between EF, unbiased longitudinal volume change, and antidepressant response across 85 brain regions. The majority of regional volumes increased significantly, and volumetric changes correlated with regional electric field (t = 3.77, df = 83, r = 0.38, p=0.0003). After controlling for nuisance variables (age, treatment number, and study site), we identified two regions (left amygdala and left hippocampus) with a strong relationship between EF and volume change (FDR corrected p<0.01). However, neither structural volume changes nor electric field was associated with antidepressant response. In summary, we showed that high electrical fields are strongly associated with robust volume changes in a dose-dependent fashion.

Electroconvulsive therapy, or ECT for short, can be an effective treatment for severe depression. Many patients who do not respond to medication find that their symptoms improve after ECT. During an ECT session, the patient is placed under general anesthesia and two electrodes are attached to the scalp to produce an electric field that generates currents within the brain. These currents activate neurons and make them fire, causing a seizure, but it remains unclear how this reduces symptoms of depression.

For many years, researchers thought that the induced seizure must be key to the beneficial effects of ECT, but recent studies have cast doubt on this idea. They show that increasing the strength of the electric field alters the clinical effects of ECT, without affecting the seizure. This suggests that the benefits of ECT depend on the electric field itself.

Argyelan et al. now show that electric fields affect the brain by making a part of the brain known as the gray matter expand. In a large multinational study, 151 patients with severe depression underwent brain scans before and after a course of ECT. The scans revealed that the gray matter of the patients’ brains expanded during the treatment. The patients who experienced the strongest electric fields showed the largest increase in brain volume, and individual brain areas expanded if the electric field within them exceeded a certain threshold. This effect was particularly striking in two areas, the hippocampus and the amygdala. Both of these areas are critical for mood and memory.

Further studies are needed to determine why the brain expands after ECT, and how long the effect lasts. Another puzzle is why the improvements in depression that the patients reported after their treatment did not correlate with changes in brain volume. Disentangling the relationships between ECT, brain volume and depression will ultimately help develop more robust treatments for this disabling condition.

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.