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With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities-structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI-in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of cross-modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age-related sources replicates across two independent and separately analyzed datasets. The same set also presented age-adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

The associations of suicidality with stress are poorly studied in schizophrenia. The study aimed to determine whether suicidality was correlated with perceived chronic stress and the cortisol fluctuations under stress tasks in schizophrenia. High suicidality was defined as a lifetime history of suicide attempts or suicidal ideation in the past 2 weeks. Individuals with schizophrenia and high suicidality (SZ-HS, n = 59), with low suicidality (SZ-LS, n = 207), and healthy controls (HC, n = 196) finished the Perceived Stress Scale. Then, they participated in an experiment that induced stress using the Paced Auditory Serial Addition Task and the Mirror Tracing Persistence Task. Negative affect was measured at baseline and after finishing each task. The salivary cortisol was collected before-, after 20 min, and after 40 min of the tasks. SZ-HS had elevated perceived stress than SZ-LS and HC. Mixed effect models showed that stress tasks induced cortisol changes in all groups; cortisol of SZ-LS was reduced more than HC, but SZ-HS and SZ-LS did not differ in cortisol fluctuations. SZ-HS and SZ-LS experienced similar negative affect changes during tasks and the difference in withdrawal rates was nonsignificant. SZ-HS had an increased error rate than SZ-LS. In conclusion, suicidality was correlated with high-level perceived stress but did not result in differences in cortisol reactivities under stress tasks. It suggests the inconsistency between appraisal of stress and biological stress system disturbance among SZ-HS compared to SZ-LS., (© 2024 John Wiley & Sons Ltd.)

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state-of-the-art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l 2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification., Methods: We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data., Results: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks., Conclusions: Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

N-acetylasparate and lactate are two prominent brain metabolites closely related to mitochondrial functioning. Prior research revealing lower levels of NAA and higher levels of lactate in the cerebral cortex of patients with schizophrenia suggest possible abnormalities in the energy supply pathway necessary for brain function. Given that stress and adversity are a strong risk factor for a variety of mental health problems, including psychotic disorders, we investigated the hypothesis that stress contributes to abnormal neuroenergetics in patients with schizophrenia. To test this hypothesis, we used the Stress and Adversity Inventory (STRAIN) to comprehensively assess the lifetime stressor exposure profiles of 35 patients with schizophrenia spectrum disorders and 33 healthy controls who were also assessed with proton magnetic resonance spectroscopy at the anterior cingulate cortex using 3 Tesla scanner. Consistent with the hypothesis, greater lifetime stressor exposure was significantly associated with lower levels of N-acetylasparate (β = -0.36, p = .005) and higher levels of lactate (β = 0.43, p = .001). Moreover, these results were driven by patients, as these associations were significant for the patient but not control group. Though preliminary, these findings suggest a possible role for stress processes in the pathophysiology of abnormal neuroenergetics in schizophrenia., Competing Interests: Declaration of competing interest LEH has received, or plans to receive, research funding or consulting fees on research projects from Mitsubishi, Your Energy Systems LLC, Neuralstem, Taisho, Heptares, Pfizer, Luye Pharma, IGC Pharma, Sound Pharma, Regeneron, and Takeda. Other authors declare no conflicts of interest with respect to this work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia., (© 2024. The Author(s).)

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell&#95;0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks., (© The Author 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The smoking rate is high in patients with schizophrenia. Brain stimulation targeting conventional brain circuits associated with nicotine addiction has also yielded mixed results. We aimed to identify alternative circuitries associated with nicotine addiction in both the general population and schizophrenia, and then test whether modulation of such circuitries may alter nicotine addiction behaviors in schizophrenia. In Study I of 40 schizophrenia smokers and 51 non-psychiatric smokers, cross-sectional neuroimaging analysis identified resting state functional connectivity (rsFC) between the dorsomedial prefrontal cortex (dmPFC) and multiple extended amygdala regions to be most robustly associated with nicotine addiction severity in healthy controls and schizophrenia patients (p = 0.006 to 0.07). In Study II with another 30 patient smokers, a proof-of-concept, patient- and rater-blind, randomized, sham-controlled rTMS design was used to test whether targeting the newly identified dmPFC location may causally enhance the rsFC and reduce nicotine addiction in schizophrenia. Although significant interactions were not observed, exploratory analyses showed that this dmPFC-extended amygdala rsFC was enhanced by 4-week active 10Hz rTMS (p = 0.05) compared to baseline; the severity of nicotine addiction showed trends of reduction after 3 and 4 weeks (p ≤ 0.05) of active rTMS compared to sham; Increased rsFC by active rTMS predicted reduction of cigarettes/day (R = -0.56, p = 0.025 uncorrected) and morning smoking severity (R = -0.59, p = 0.016 uncorrected). These results suggest that the dmPFC-extended amygdala circuit may be linked to nicotine addiction in schizophrenia and healthy individuals, and future efforts targeting its underlying pathophysiological mechanisms may yield more effective treatment for nicotine addiction., Competing Interests: Declaration of Competing interest LEH has received or plans to receive research funding or consulting fee on research projects from Mitsubishi, Your Energy Systems LLC, Neuralstem, Taisho, Heptares, Pfizer, Sound Pharma, IGC Pharma, Regeneron, and Takeda. All other authors declare no conflict of interest. The content is solely the responsibility of the authors. The views expressed are the authors’ own and do not necessarily represent the views of the National Institutes of Health, the Department of Health, or the US government. The ClinicalTrials.gov Identifier for this study is NCT03281629., (Published by Elsevier Inc.)

A clear understanding of the pathophysiology of schizophrenia and related spectrum disorders has been limited by clinical heterogeneity. We investigated whether relative severity and predominance of one or more delusion subtypes might yield clinically differentiable patient profiles. Patients (N = 286) with schizophrenia spectrum disorders (SSD) completed the 21-item Peters et al. Delusions Inventory (PDI-21). We performed factor analysis followed by k-means clustering to identify delusion factors and patient subtypes. Patients were further assessed via the Brief Psychiatric Rating Scale, Brief Negative Symptom Scale, Digit Symbol and Digit Substitution tasks, use of cannabis and tobacco, and stressful life events. The overall patient sample clustered into subtypes corresponding to Low-Delusion, Grandiose-Predominant, Paranoid-Predominant, and Pan-Delusion patients. Paranoid-Predominant and Pan-Delusion patients showed significantly higher burden of positive symptoms, while Low-Delusion patients showed the highest burden of negative symptoms. The Paranoia delusion factor score showed a positive association with Digit Symbol and Digit Substitution tasks in the overall sample, and the Paranoid-Predominant subtype exhibited the best performance on both tasks. Grandiose-Predominant patients showed significantly higher tobacco smoking severity than other subtypes, while Paranoid-Predominant patients were significantly more likely to have a lifetime diagnosis of Cannabis Use Disorder. We suggest that delusion self-report inventories such as the PDI-21 may be of utility in identifying sub-syndromes in SSD. From the current study, a Paranoid-Predominant form may be most distinctive, with features including less cognitive impairment and a stronger association with cannabis use., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:LEH has received or plans to receive research funding or consulting fees on research projects from Mitsubishi, Your Energy Systems LLC, Neuralstem, Taisho, Heptares, Pfizer, Luye Pharma, IGC Pharma, Sound Pharma, Regeneron, and Takeda. All other authors declare no conflict of interest., (Published by Elsevier Ltd.)

Background: Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences., Methods: 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives., Results: Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms., Conclusions: These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum., Competing Interests: Declaration of competing interest The authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

Background and Hypothesis: Low-grade neural and peripheral inflammation are among the proposed pathophysiological mechanisms of schizophrenia. White matter impairment is one of the more consistent findings in schizophrenia but the underlying mechanism remains obscure. Many cerebral white matter components are sensitive to neuroinflammatory conditions that can result in demyelination, altered oligodendrocyte differentiation, and other changes. We tested the hypothesis that altered immune-inflammatory response system (IRS) and compensatory immune-regulatory reflex system (IRS/CIRS) dynamics are associated with reduced white matter integrity in patients with schizophrenia., Study Design: Patients with schizophrenia (SCZ, 70M/50F, age = 40.76 ± 13.10) and healthy controls (HCs, 38M/27F, age = 37.48 ± 12.31) underwent neuroimaging and plasma collection. A panel of cytokines were assessed using enzyme-linked immunosorbent assay. White matter integrity was measured by fractional anisotropy (FA) from diffusion tensor imaging using a 3-T Prisma MRI scanner. The cytokines were used to generate 3 composite scores: IRS, CIRS, and IRS/CIRS ratio., Study Results: The IRS/CIRS ratio in SCZ was significantly higher than that in HCs (P = .009). SCZ had a significantly lower whole-brain white matter average FA (P < .001), and genu of corpus callosum (GCC) was the most affected white matter tract and its FA was significantly associated with IRS/CIRS (r = 0.29, P = .002). FA of GCC was negatively associated with negative symptom scores in SCZ (r = -0.23, P = .016). There was no mediation effect taking FA of GCC as mediator, for that IRS/CIRS was not associated with negative symptom score significantly (P = .217) in SCZ., Conclusions: Elevated IRS/CIRS might partly account for the severity of negative symptoms through targeting the integrity of GCC., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background and Hypothesis: Mounting evidence supports cerebrovascular contributions to schizophrenia spectrum disorder (SSD) but with unknown mechanisms. The blood-brain barrier (BBB) is at the nexus of neural-vascular exchanges, tasked with regulating cerebral homeostasis. BBB abnormalities in SSD, if any, are likely more subtle compared to typical neurological insults and imaging measures that assess large molecule BBB leakage in major neurological events may not be sensitive enough to directly examine BBB abnormalities in SSD., Study Design: We tested the hypothesis that neurovascular water exchange (Kw) measured by non-invasive diffusion-prepared arterial spin label MRI (n = 27 healthy controls [HC], n = 32 SSD) is impaired in SSD and associated with clinical symptoms. Peripheral vascular endothelial health was examined by brachial artery flow-mediated dilation (n = 44 HC, n = 37 SSD) to examine whether centrally measured Kw is related to endothelial functions., Study Results: Whole-brain average Kw was significantly reduced in SSD (P = .007). Exploratory analyses demonstrated neurovascular water exchange reductions in the right parietal lobe, including the supramarginal gyrus (P = .002) and postcentral gyrus (P = .008). Reduced right superior corona radiata (P = .001) and right angular gyrus Kw (P = .006) was associated with negative symptoms. Peripheral endothelial function was also significantly reduced in SSD (P = .0001). Kw in 94% of brain regions in HC positively associated with peripheral endothelial function, which was not observed in SSD, where the correlation was inversed in 52% of brain regions., Conclusions: This study provides initial evidence of neurovascular water exchange abnormalities, which appeared clinically associated, especially with negative symptoms, in schizophrenia., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Familial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children., Methods: We used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child's cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control differences., Results: In children with a family history of SSD, the regional vulnerability index captured significantly more variance in ancestral history than traditional whole-brain and regional brain measurements. In children with and without family history of SSD, the regional vulnerability index also captured more variance associated with negative pre/perinatal environment and early-life experiences than traditional brain measurements., Conclusions: In summary, in a cohort in which most children will not develop SSD, familial, pre/perinatal, and early developmental risks can alter brain patterns in the direction observed in adult patients with SSD. Individual similarity to adult SSD patterns may provide an early biomarker of the effects of genetic and developmental risks on the brain prior to psychotic or prodromal symptom onset., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Abnormalities in subcortical brain structures may reflect higher suicide risk in mood disorders, but less is known about its associations for schizophrenia. This cross-sectional imaging study aimed to explore whether the history of suicide attempts was associated with subcortical changes among individuals with schizophrenia. We recruited 44 individuals with schizophrenia and a history of suicide attempts (SZ-SA) and 44 individuals with schizophrenia but without a history of suicide attempts (SZ-NSA) and 44 healthy controls. Linear regression showed that SZ-SA had smaller volumes of the hippocampus (Cohen's d = -0.72), the amygdala (Cohen's d = -0.69), and some nuclei of the amygdala (Cohen's d, -0.57 to -0.72) than SZ-NSA after adjusting for age, sex, illness phase, and intracranial volume. There was no difference in the volume of the subfields of the hippocampus. It suggests the history of suicide attempts is associated with subcortical volume alterations in schizophrenia., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I 2  = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions., (© 2022. The Author(s).)

Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset <18 years). However, as neuroimaging methods vary and sample sizes are modest, results remain inconclusive. Using harmonized data processing protocols and a mega-analytic approach, we compared white matter microstructure in EOP and healthy controls using diffusion tensor imaging (DTI). Our sample included 321 adolescents with EOP (median age = 16.6 years, interquartile range (IQR) = 2.14, 46.4% females) and 265 adolescent healthy controls (median age = 16.2 years, IQR = 2.43, 57.7% females) pooled from nine sites. All sites extracted mean fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for 25 white matter regions of interest per participant. ComBat harmonization was performed for all DTI measures to adjust for scanner differences. Multiple linear regression models were fitted to investigate case-control differences and associations with clinical variables in regional DTI measures. We found widespread lower FA in EOP compared to healthy controls, with the largest effect sizes in the superior longitudinal fasciculus (Cohen's d = 0.37), posterior corona radiata (d = 0.32), and superior fronto-occipital fasciculus (d = 0.31). We also found widespread higher RD and more localized higher MD and AD. We detected significant effects of diagnostic subgroup, sex, and duration of illness, but not medication status. Using the largest EOP DTI sample to date, our findings suggest a profile of widespread white matter microstructure alterations in adolescents with EOP, most prominently in male individuals with early-onset schizophrenia and individuals with a shorter duration of illness., (© 2022. The Author(s).)

This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia., Competing Interests: Declaration of Competing Interest Dr. Daniel H. Mathalon consulted for Neurocrine Biosciences, Gilgamesh Pharmaceuticals, and Recognify Life Sciences, and served on a data safety monitoring/advisory board for Neurocrine Biosciences. The other authors have no conflict of interest to report., (Copyright © 2023. Published by Elsevier B.V.)

Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry., (© 2023. The Author(s).)

Aberrant gamma frequency neural oscillations in schizophrenia have been well demonstrated using auditory steady-state responses (ASSR). However, the neural circuits underlying 40 Hz ASSR deficits in schizophrenia remain poorly understood. Sixty-six patients with schizophrenia spectrum disorders and 85 age- and gender-matched healthy controls completed one electroencephalography session measuring 40 Hz ASSR and one imaging session for resting-state functional connectivity (rsFC) assessments. The associations between the normalized power of 40 Hz ASSR and rsFC were assessed via linear regression and mediation models. We found that rsFC among auditory, precentral, postcentral, and prefrontal cortices were positively associated with 40 Hz ASSR in patients and controls separately and in the combined sample. The mediation analysis further confirmed that the deficit of gamma band ASSR in schizophrenia was nearly fully mediated by three of the rsFC circuits between right superior temporal gyrus-left medial prefrontal cortex (MPFC), left MPFC-left postcentral gyrus (PoG), and left precentral gyrus-right PoG. Gamma-band ASSR deficits in schizophrenia may be associated with deficient circuitry level connectivity to support gamma frequency synchronization. Correcting gamma band deficits in schizophrenia may require corrective interventions to normalize these aberrant networks., (© 2023. The Author(s).)

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Brain functional networks identified from resting functional magnetic resonance imaging (fMRI) data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale (ICA dimensionality, i.e., ICA model order). In this work, we present an approach using multi-objective optimization scICA with reference algorithm (MOO-ICAR) to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales, which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N > 1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into scICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. The classification model built on the msFNC features obtained up to 85% F1 score, 83% precision, and 88% recall, indicating the strength of the proposed framework in detecting group differences between schizophrenia and the control group. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Aim: Approximately a third of patients with schizophrenia fail to adequately respond to antipsychotic medications, a condition known as treatment resistance (TR). We aimed to assess cognitive and cortical thickness deficits and their relationship to TR in schizophrenia., Method: We recruited patients with schizophrenia (n = 127), including patients at treatment initiation (n = 45), treatment-responsive patients (n = 40) and TR patients (n = 42), and healthy controls (n = 83). Clinical symptoms, neurocognitive function, and structural images were assessed. We performed group comparisons, and explored association of cortical thickness and cognition with TR., Results: The TR patients showed significantly more severe clinical symptoms and cognitive impairment relative to the treatment-responsive group. Compared to healthy controls, 56 of 68 brain regions showed significantly reduced cortical thickness in patients with schizophrenia. Reductions in five regions were significantly associated with TR (reduction in TR relative to treatment-responsive patients), i.e. in the right caudal middle frontal gyrus, superior frontal cortex, fusiform gyrus, pars opercularis of the inferior frontal cortex, and supramarginal cortex. Cognition deficits were also significantly correlated with cortical thickness in these five regions in patients with schizophrenia. Cortical thickness of the right caudal middle frontal gyrus, superior frontal cortex and pars opercularis of the inferior frontal cortex also significantly mediated effects of cognitive deficits on TR., Conclusion: Treatment resistance in schizophrenia was associated with reduced thickness in the right caudal middle frontal gyrus, superior frontal cortex, fusiform gyrus, pars opercularis of the inferior frontal cortex, and supramarginal cortex. Cortical abnormalities further mediate cognitive deficits known to be associated with TR., (© 2022 The Authors. Psychiatry and Clinical Neurosciences © 2022 Japanese Society of Psychiatry and Neurology.)

Background and Hypothesis: Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway., Study Designs: Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances., Study Results: PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes' expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains., Conclusions: Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Widespread white matter abnormalities and alterations in glutamate levels have been reported in patients with schizophrenia. We hypothesized that alterations in white matter integrity and glutamate levels in individuals at clinical high risk (CHR) for psychosis are associated with the subsequent development of psychosis., Methods: Participants included 33 antipsychotic naïve CHR (Female 7/Male 26, Age 19.55 (4.14) years) and 38 healthy controls (Female 10/Male 28, Age 20.92 (3.37) years). Whole brain diffusion tensor imaging for fractional anisotropy (FA) and right frontal white matter proton magnetic resonance spectroscopy for glutamate levels were acquired. CHR participants were clinically followed for 2 years to determine conversion to psychosis., Results: CHR participants that transitioned to psychosis (N = 7, 21%) were characterized by significantly lower FA values in the posterior thalamic radiation compared to those who did not transition and healthy controls. In the CHR group that transitioned to psychosis only, positive exploratory correlations between glutamate levels and FA values of the posterior thalamic radiation and the retrolenticular part of the internal capsule and a negative correlation between glutamate levels and the cingulum FA values were found., Conclusion: The results of the present study highlight that alterations in white matter structure and glutamate are related with the conversion to psychosis., Competing Interests: Declaration of competing interest Pablo León-Ortiz has served as a speaker for Shire Pharmaceuticals, Francisco Reyes-Madrigal has served as a speaker for Janssen (Johnson & Johnson) and AstraZeneca, R. Mora-Durán has served as a speaker for Schwabe Pharmaceuticals, Laura M. Rowland has served as a consultant for Otsuka America Pharmaceutical Inc., and Camilo de la Fuente-Sandoval has served as a consultant for Janssen (Johnson & Johnson). The rest of the authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Patients with treatment-resistant schizophrenia (TRS) suffer severe, long-term psychotic symptoms and chronic stress. Salivary kynurenic acid (KYNA) and choroid plexus were evidenced to relate to psychological stress. We hypothesized that TRS patients would have higher salivary KYNA levels than patients who respond to antipsychotics (NTRS) and healthy controls (HC), and increased salivary KYNA levels are associated with clinical phenotypes and choroid plexus volume. A total of 66 HC participants, 53 patients with TRS and 46 with NTRS were enrolled. Salivary KYNA levels were measured by liquid chromatography-tandem mass spectrometry, choroid plexus volume by magnetic resonance imaging, and cognitive functions with the MATRICS Consensus Cognitive Battery. The TRS group had significantly higher salivary KYNA levels than the NTRS group (p = 0.003), who in turn had higher salivary KYNA than HC (p = 0.02). Higher salivary KYNA levels were associated with larger choroid plexus volume (r = 0.48, p = 0.004); lower attention/vigilance (r = -0.44, p = 0.004), verbal learning (r = -0.44, p = 0.004), total MCCB score (r = -0.42, p = 0.005); and a higher total PANSS score (r = 0.48, p = 0.004) in TRS patients. An enlarged choroid plexus also related to worse attention/vigilance (r = -0.39, p = 0.03), verbal learning (r = -0.55, p = 0.001), total MCCB score (r = -0.41, p = 0.02) and clinical symptoms (r = 0.48, p = 0.004) in TRS patients only. We conclude that elevated salivary KYNA levels and associated choroid plexus enlargement are clinically relevant indicators of TRS, with salivary KYNA being particularly valuable as a peripheral marker. Our findings should benefit TRS research and benefit the improvement of personalized treatment., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: LEH has received or plans to receive research funding or consulting fees on research projects from Mitsubishi, Your Energy Systems LLC, Neuralstem, Taisho, Heptares, Pfizer, Luye Pharma, Sound Pharma, Takeda, and Regeneron. None was involved in the design, analysis or outcomes of the study. All other authors declared that they have no conflicts of interest to this work and do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia., (© 2022. The Author(s).)

Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Individuals with schizophrenia have higher rates of suicide attempts than the general population. Specific cortical abnormalities (e.g., the cortical surface area and thickness) may be associated with a history of suicide attempts. We recruited 74 individuals with schizophrenia (37 suicide attempters were individually matched with 37 non-attempters on age, sex, phase of illness, and study center) and 37 healthy volunteers. The cortical surface area and thickness data were extracted from structural MRI and compared between the groups. Suicide attempters showed significantly smaller surface areas in the whole brain (p = .028, Cohen's d = -0.54) than non-attempters. No association was found between the cortical surface area of individual brain regions and a history of suicide attempts. The mean cortical thickness did not differ significantly between the groups; however, suicide attempters demonstrated a thinner cortex in the right superior temporal gyrus (p < .001, q = 0.037, Cohen's d = -0.88). These findings indicate that a history of suicide attempts among individuals with schizophrenia is associated with a reduction in the global cortical surface area and specific cortical thinning of the right superior temporal gyrus. The morphometric alteration of the right superior temporal gyrus may represent a biomarker of suicidal behavior in individuals with schizophrenia., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes., (© 2022 The Authors. Psychiatry and Clinical Neurosciences © 2022 Japanese Society of Psychiatry and Neurology.)

Background: Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms., Methods: People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits., Results: Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms., Conclusions: First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation., (Copyright © 2021 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Early-onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early-onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early-onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early-onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed-effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We observed significantly lower intracranial (Cohen's d = -0.39) and hippocampal (d = -0.25) volumes, and higher caudate (d = 0.25) and pallidum (d = 0.24) volumes in patients relative to controls. Intracranial volume was lower in both early-onset schizophrenia (d = -0.34) and affective psychosis (d = -0.42), and early-onset schizophrenia showed lower hippocampal (d = -0.24) and higher pallidum (d = 0.29) volumes. Patients who were currently treated with antipsychotic medication (n = 193) had significantly lower intracranial volume (d = -0.42). The findings demonstrate a similar pattern of brain alterations in early-onset psychosis as previously reported in adult psychosis, but with notably low intracranial volume. The low intracranial volume suggests disrupted neurodevelopment in adolescent early-onset psychosis., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta-Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia-specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6-20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10 -6 ). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease-duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Background: About a third of patients with schizophrenia do not respond adequately to currently available antipsychotics and thus experiences symptoms of greater severity, known as treatment-resistant schizophrenia (TRS). Some evidence suggests that the tryptophan (TRP) pathway (comprising 5-HT and kynurenine sub-pathways) has an important influence on response to antipsychotics. We therefore hypothesized that TRS is linked to metabolites of TRP pathway., Methods: We measured TRP metabolites in 54 patients with TRS and compared them to 49 age- and sex-matched patients who responded to antipsychotics (NTRS), and 62 healthy controls using liquid chromatography-tandem mass spectrometry. Psychopathology and clinical symptoms were assessed by means of schizophrenia positive and negative scales. Working memory abilities, cortical thickness and white matter diffusion tensor imaging fractional anisotropy were appraised in enrolled subjects by neurophysiological tests, as spatial span and digital sequencing tests, and 3T magnetic resonance imaging., Results: Patients with TRS had a significantly higher 5-HT/TRP ratio (p = 0.009) than patients with NTRS. However, the two groups did not differ in kynurenine-pathway metabolites or ratios. Additionally, 5-HT/TRP was positively correlated with disorganized symptoms in TRS (r = 0.59, p < 0.001), and negatively correlated with digit-sequencing test scores (r = -0.34, p = 0.02). These correlations were insignificant among patients with NTRS and healthy controls. In patients with TRS, 5-HT/TRP was strongly linked to the right supramarginal cortex (t = -3.2, p = 0.003), and in healthy controls, to the right transverse temporal (t = 3.40, p = 0.001), but significance disappeared after FDR correction., Conclusions: Present results indicate that an upregulated 5-HT biosynthetic pathway can be associated to TRS, suggesting that targeting mechanisms of 5-HT conversion from tryptophan could shed light on the development of new pharmacological approaches of TRS., (Copyright © 2021. Published by Elsevier Ltd.)

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Stress is implicated in psychosis etiology and exacerbation, but pathogenesis toward brain network alterations in schizophrenia remain unclear. White matter connects limbic and prefrontal regions responsible for stress response regulation, and white matter tissues are also vulnerable to glucocorticoid aberrancies. Using a novel psychological stressor task, we studied cortisol stress responses over time and white matter microstructural deficits in schizophrenia spectrum disorder (SSD). Cortisol was measured at baseline, 0-, 20-, and 40-min after distress induction by a psychological stressor task in 121 SSD patients and 117 healthy controls (HC). White matter microstructural integrity was measured by 64-direction diffusion tensor imaging. Fractional anisotropy (FA) in white matter tracts were related to cortisol responses and then compared to general patterns of white matter tract deficits in SSD identified by mega-analysis. Differences between 40-min post-stress and baseline, but not acute reactivity post-stress, was significantly elevated in SSD vs HC, time × diagnosis interaction F 2.3,499.9  = 4.1, p = 0.013. All SSD white matter tracts were negatively associated with prolonged cortisol reactivity but all tracts were positively associated with prolonged cortisol reactivity in HC. Individual tracts most strongly associated with prolonged cortisol reactivity were also most impacted in schizophrenia in general as established by the largest schizophrenia white matter study (r = -0.56, p = 0.006). Challenged with psychological stress, SSD and HC mount similar cortisol responses, and impairments arise in the resolution timeframe. Prolonged cortisol elevations are associated with the white matter deficits in SSD, in a pattern previously associated with schizophrenia in general., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features., (© 2021. The Author(s).)

Stress and abnormal stress response are associated with schizophrenia spectrum disorder (SSD), but the brain mechanisms linking stress to symptomatology remain unclear. In this study, we used a stress-based functional neuroimaging task, reverse-translated from preclinical studies, to test the hypothesis that abnormal corticolimbic processing of stressful threat anticipation is associated with psychosis and affective symptoms in SSD. Participants underwent an MRI-compatible ankle-shock task (AST) in which the threat of mild electrical shock was anticipated. We compared functional brain activations during anticipatory threat periods from N = 18 participants with SSD (10 M/8F) to those from N = 12 community controls (9 M/3F). After family-wise error correction, only one region, the ventral anterior cingulate cortex (vACC), showed significantly reduced activation compared with controls. vACC activation significantly correlated with clinical symptoms measured by the Brief Psychiatric Rating Scale total score (r = 0.54) and the psychosis subscale (r = 0.71), and inversely correlated with trait depression measured by the Maryland Trait and State Depression scale (r=-0.48). Deficient activation in vACC under stress of anticipated threat may lead to aberrant interpretation of such threat, contributing to psychosis and mood symptoms in SSD. This experimental paradigm has translational potential and may identify circuitry-level mechanisms of stress-related mental illness, leading to more targeted treatment., (Copyright © 2021. Published by Elsevier B.V.)

Reduced cortical gray matter integrity and cognitive abilities are among core deficits in schizophrenia. We hypothesized that higher allostatic load (AL) that accounts for exposure to chronic stress is a contributor to structural and cognitive deficits in schizophrenia. One hundred and sixty-seven schizophrenia patients who were on average with normal weight, normal systolic, and diastolic blood pressure and 72 healthy controls were enrolled in the study. Group differences in subclinical cardiovascular, metabolic, immune, and neuroendocrine biological markers as indexed by AL and contribution of AL components to the structural and cognitive deficits in schizophrenia were explored. Compared with controls, schizophrenia patients who were normotensive, normoweight, and had low total cholesterol levels still had significantly higher AL mainly due to lower high-density lipoprotein cholesterol and higher heart rate, waist-hip ratio, hemoglobinA1c, hypersensitive C-reactive protein, and overnight-urine cortisol levels. Patients also had decreased whole-brain mean cortical thickness, and lower cognition assessed by the MATRICS consensus cognitive battery. AL was inversely correlated with mean cortical thickness and cognition in schizophrenia, while none of these relationships existed in controls. Mediation analyses showed the effect of AL on cognitive deficits in schizophrenia was significantly mediated by cortical thinning, and the most significant mediating cortical area was the left superior frontal gyrus. Cortical thickness may act as a mediator between AL and cognitive deficits in schizophrenia. Early intervention strategies to reduce cortical thinning and cognitive dysfunction in schizophrenia should target specific aspects of their high AL in addition to weight gain, hypertension and high cholesterol levels., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Norepinephrine has both central and peripheral origins and is known to influence cognitive processes in attention, learning, and working memory, but the research regarding the impact of norepinephrine on cognition in schizophrenia remains sparse, and mainly focuses on centrally regulated noradrenergic effects. This study examined the relationship between cumulative overnight norepinephrine levels in the urine and working memory in patients with schizophrenia and healthy controls., Methods: Urinary catecholamines were collected overnight in patients with schizophrenia (n = 75) and healthy controls (n = 33). Working memory was assessed using the digit sequencing task., Results: Patients showed significantly higher average levels of overnight norepinephrine (t(103.10) = -3.16, p = 0.002) and reduced working memory performance (t(90) = 3.87, p = 0.001) compared with healthy individuals. There was a significant negative correlation between norepinephrine and working memory in patients (r = -0.38, p = 0.005), but not in controls (r = 0.08, p = 0.67). After controlling for age, sex, antipsychotic medications, and serotonin-norepinephrine reuptake inhibitor-based antidepressants, the correlation remained significant (r = -0.41, p = 0.004)., Conclusions: High peripheral overnight levels of urinary norepinephrine are associated with lower working memory performance in patients with schizophrenia. These results parallel previous studies suggesting that high levels of central norepinephrine may result in working memory impairments. As norepinephrine rapidly breaks down and usually does not pass through the blood-brain barrier, the potential effect of peripheral cumulative norepinephrine on working memory is intriguing, and needs to be further investigated., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

A number of tryptophan metabolites known to be neuroactive have been examined for their potential associations with cognitive deficits in schizophrenia. Among these metabolites, kynurenic acid (KYNA), 5-hydroxyindole (5-HI), and quinolinic acid (QUIN) are documented in their diverse effects on α-7 nicotinic acetylcholine receptor (α7nAChR) and/or N-methyl-D-aspartate receptor (NMDAR), two of the receptor types thought to contribute to cognitive impairment in schizophrenia. In this study, serum levels of KYNA, 5-HI, and QUIN were measured in 195 patients with schizophrenia and in 70 healthy controls using liquid chromatography-tandem mass spectrometry; cognitive performance in MATRICS Consensus Cognitive Battery and cortical thickness measured by magnetic resonance imaging were obtained. Patients with schizophrenia had significantly lower serum KYNA (p < 0.001) and QUIN (p = 0.02) levels, and increased 5-HI/KYNA (p < 0.001) and QUIN/KYNA ratios (p < 0.001) compared with healthy controls. Multiple linear regression showed that working memory was positively correlated with serum 5-HI levels (t = 2.10, p = 0.04), but inversely correlated with KYNA concentrations (t = -2.01, p = 0.05) in patients. Patients with high 5-HI and low KYNA had better working memory than other subgroups (p = 0.01). Higher 5-HI levels were associated with thicker left lateral orbitofrontal cortex (t = 3.71, p = 2.94 × 10 -4 ) in patients. The different effects of 5-HI and KYNA on working memory may appear consistent with their opposite receptor level mechanisms. Our findings appear to provide a new insight into the dynamic roles of tryptophan pathway metabolites on cognition, which may benefit novel therapeutic development that targets cognitive impairment in schizophrenia.

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = -0.176, p = 0.007) and processing speed (β = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance., Competing Interests: Declaration of competing interest LEH has received or plans to receive research funding or consulting fees on research projects from Mitsubishi, Your Energy Systems LLC, Neuralstem, Taisho, Heptares, Pfizer, Luye Pharma, Sound Pharma, Takeda, and Regeneron. None was involved in the design, analysis or outcomes of the study. Paul Thompson and Neda Jahanshad received grant support from Biogen, Inc. (Boston, USA) for research unrelated to the topic of this manuscript. All other authors declare no conflicts of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: We evaluated the patient-control differences and predictive value of the retina as potential biomarkers for schizophrenia., Methods: The institutional study included both eyes of 58 schizophrenia spectrum disorder (SSD) patients (age 37.2 ± 12.3 years) and 35 controls (age 41.1 ± 15.2 years). Retinal nerve fiber layer (RNFL), ganglion cell-inner plexiform layer, outer retinal photoreceptor complex, and total macula thicknesses were measured by optical coherence tomography (OCT). Anterior segment parameters including central corneal thickness, anterior chamber depth, and axial length were measured to rule out confounds on the retinal measures., Results: The peripapillary RNFL was overall significantly thinner in SSD relative to controls (F = 3.97, P = 0.049), most pronounced in the temporal (5.2 µm difference, F = 6.95, P = 0.010) and inferior quadrants (12.1 µm difference, F = 7.32, P = 0.009). There were no significant group differences in thickness for the macular RNFL, ganglion, or photoreceptor cell related measures (P > 0.05). Peripapillary RNFL, central macula, and outer photoreceptor complex thicknesses were together able to classify SSD patients with 80% sensitivity and 71% specificity; area under the curve = 0.82 (95% confidence interval, 0.75-0.88)., Conclusions: SSD patients exhibited significant RNFL thinning relative to controls. Notably, retinal thickness measures including both peripapillary and macular data exhibited improved diagnostic accuracy for SSD as compared to these regions alone., Translational Relevance: This is the first study to evaluate the predictive value of both the inner and outer retina in SSD. OCT retinal thickness measures including peripapillary data in conjunction with macular data may provide an informative, noninvasive in vivo ocular biomarker for schizophrenia.

Cognitive impairment is a core characteristic of schizophrenia, but its underlying neural mechanisms remain poorly understood. Reduced brain-derived neurotrophic factor (BDNF), a protein critical for neural plasticity and synaptic signaling, is one of the few molecules consistently associated with cognitive deficits in schizophrenia although the etiological pathway leading to BDNF reduction in schizophrenia is unclear. We examined microRNA-195 (miR-195), a known modulator of BDNF protein expression, as a potential mechanistic component. One-hundred and eighteen first-episode patients with schizophrenia either antipsychotic medication-naïve or within two weeks of antipsychotic medication exposure and forty-seven age- and sex-matched healthy controls were enrolled. MiR-195 and BDNF mRNA and BDNF protein levels in peripheral blood were tested. Cognitive function was assessed using the MATRICS Consensus Cognitive Battery (MCCB). MiR-195 was significantly higher (p = 0.01) whereas BDNF mRNA (p < 0.001) and protein (p = 0.016) levels were significantly lower in patients compared with controls. Higher miR-195 expression was significantly correlated to lower BDNF protein levels in patients (partial r = -0.28, p = 0.003) and lower BDNF protein levels were significantly associated with poorer overall cognitive performance by MCCB and also in speed of processing, working memory, and attention/vigilance domains composite score (p = 0.002-0.004). The subgroup of patients with high miR-195 and low BDNF protein showed the lowest level of cognitive functions, and miR-195 showed significant mediation effects on cognitive functions through BDNF protein. Elevated miR-195 may play a role in regulating BDNF protein expression thereby influencing cognitive impairments in schizophrenia, suggesting that development of cognition enhancing treatment for schizophrenia may consider a micro-RNA based strategy.