Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium

Lithium and valproate affect subcortical brain volumes in individuals with bipolar disorder: Mega-analysis of 235 individuals

INTRODUCTION

The mega-analysis conducted by the ENIGMA Bipolar Disorder Working Group revealed significant volume increment effects of lithium on the hippocampus in individual with bipolar disorder. However, the study did not assess other medications and other subcortical regions.

METHODS

Data of 235 individuals with bipolar disorder were taken from a mega-analysis conducted by the COCORO consortium in Japan. The effects of psychotropic prescriptions (lithium, valproate, antipsychotics, antidepressants, benzodiazepines) were assessed using a linear mixed-effects model with volumes of subcortical structures as dependent variables, and age, sex, intracranial volume, duration of illness, and psychotropic prescriptions as independent variables; the type of protocol was incorporated as a random effect.

RESULTS

Prescriptions of lithium was associated with larger left amygdala volume (Effect size (ES, Cohen's d) = 0.36, p = 0.001). Prescriptions of valproate was associated with smaller left amygdala volume (ES = -0.45, p = 0.001), and larger bilateral ventricle volumes (ES = 0.68, p < 0.001 (left), ES = 0.70, p < 0.001 (right)). Prescriptions of antipsychotics were associated with larger left globus pallidus volume (ES = 0.33, p = 0.014) and smaller left hippocampus volume (ES = -0.33, p = 0.024). Prescriptions of benzodiazepines were associated with smaller left lateral ventricle (ES = -0.40, p = 0.029). Prescriptions of antidepressants were associated with smaller right accumbens volume (ES = -0.22, p = 0.043), bilateral caudate volumes (ES = -0.38, p = 0.013 (left), ES = -0.25, p = 0.050 (right)) and right putamen volume (ES = -0.23, p = 0.024).

CONCLUSION

We confirmed the association between prescription of valproate and smaller amygdala and larger lateral ventricle volumes in a large sample for the first time. Large sample size, uniform data collection methodology, and robust statistical analysis are strengths of the current study.

An ontogenetic role for Broca's and related speech areas in schizophrenic auditory hallucinations? Evidence from sulcal pits analysis

The pathogenesis of auditory verbal hallucinations (AVH), a core symptom of schizophrenia, is unknown, but there is increasing interest in the possibility that it involves brain structural changes. We used a novel whole brain structural imaging technique, sulcal pits analysis, to measure different parameters of sulcal pits, the deepest points in each fold of the cerebral cortex, in 24 schizophrenia patients with continuous or near-continuous hallucinations (AVH-frequent) and 22 matched patients who had never experienced AVH (AVH-never). A matched group of 58 healthy controls (HC) were also examined. In comparison to the AVH-never patients, the AVH-frequent patients exhibited increased depth of sulcal pits in a region of the left inferior frontal cortex and anterior insula, overlapping with Broca's Area and Brodmann Area 47. A nearly identical result emerged in the comparison between AVH-frequent patients and the HC. These findings suggest that a neurodevelopmentally stable brain structural alteration may contribute to the manifestation of AVH in individuals later diagnosed with schizophrenia. This finding also has relevance for theoretical approaches to AVH, a leading example of which relates them to inner speech.

Highly demarcated structural alterations in the brain and impaired social incentive learning in Tbx1 heterozygous mice

Copy number variants (CNVs) are robustly associated with psychiatric disorders and changes in brain structures. However, because CNVs contain many genes, the precise gene-phenotype relationship remains unclear. Although various volumetric alterations in the brains of 22q11.2 CNV carriers have been identified in humans and mouse models, it is unknown how each gene encoded in the 22q11.2 region contributes to structural alterations, associated mental illnesses, and their dimensions. Our previous studies identified Tbx1, a T-box family transcription factor encoded in the 22q11.2 CNV, as a driver gene for social interaction and communication, spatial and working memory, and cognitive flexibility. However, it remains unclear how TBX1 impacts the volumes of various brain regions and their functionally linked behavioral dimensions. In this study, we used volumetric magnetic resonance imaging analysis to comprehensively evaluate brain region volumes and behavioral alterations relevant to affected structures in congenic Tbx1 heterozygous mice. Our data showed that the volumes of the anterior and posterior portions of the amygdaloid complex and its surrounding cortical regions were most robustly reduced in Tbx1 heterozygous mice. In an amygdala-dependent task, Tbx1 heterozygous mice were impaired in their ability to learn the incentive value of a social partner. The volumes of the primary and secondary auditory cortexes were increased, and acoustic, but not non-acoustic, sensorimotor gating was impaired in Tbx1 heterozygous mice. Our findings identify the brain's regional volume alterations and their relevant behavioral dimensions associated with Tbx1 heterozygosity.

Deciphering the Heterogeneity of Schizophrenia: A Multimodal and Multivariate Neuroimaging Framework for Unveiling Brain-Symptom Relationships and Underlying Subtypes

BACKGROUND AND HYPOTHESIS

Schizophrenia manifests large heterogeneities in either symptoms or brain abnormalities. However, the neurobiological basis of symptomatic diversity remains poorly understood. We hypothesized that schizophrenia's diverse symptoms arise from the interplay of structural and functional alterations across multiple brain regions, rather than isolated abnormalities in a single area.

STUDY DESIGN

A total of 495 schizophrenia patients and 507 healthy controls from 8 sites were recruited. Five symptomatic dimensions of schizophrenia patients were derived from the Positive and Negative Syndrome Scale. Multivariate canonical correlation analysis was introduced to identify symptom-related multimodal magnetic resonance imaging composite indicators (MRICIs) derived from gray matter volume, functional connectivity strength, and white matter fractional anisotropy. The intergroup differences in MRICIs were compared, and the paired-wise correlations between symptom dimensions and MRICIs were resolved. Finally, K-means clustering was used to identify the underlying biological subtypes of schizophrenia based on MRICIs.

STUDY RESULTS

Canonical correlation analysis identified 15 MRICIs in schizophrenia that were specifically contributed by the neuroimaging measures of multiple regions, respectively. These MRICIs can effectively characterize the complexity of symptoms, showing correlations within and across symptom dimensions, and were consistent across both first-episode and chronic patients. Additionally, some of these indicators could moderately differentiate schizophrenia patients from healthy controls. K-means clustering identified 2 schizophrenia subtypes with distinct MRICI profiles and symptom severity.

CONCLUSIONS

Symptom-guided multimodal and multivariate MRICIs could decode the symptom heterogeneity of schizophrenia patients and might be considered as potential biomarkers for schizophrenia.

Heterogeneity of morphometric similarity networks in health and schizophrenia

Reduced structural network connectivity is proposed as a biomarker for chronic schizophrenia. This study assessed regional morphometric similarity as an indicator of cortical inter-regional connectivity, employing longitudinal normative modeling to evaluate whether decreases are consistent across individuals with schizophrenia. Normative models were trained and validated using data from healthy controls (n = 4310). Individual deviations from these norms were measured at baseline and follow-up, and categorized as infra-normal, normal, or supra-normal. Additionally, we assessed the change over time in the total number of infra- or supra-normal regions for each individual. At baseline, patients exhibited reduced morphometric similarity within the default mode network compared to healthy controls. The proportion of patients with infra- or supra-normal values in any region at both baseline and follow-up was low (<6%) and similar to that of healthy controls. Mean intra-group changes in the number of infra- or supra-normal regions over time were minimal (<1) for both the schizophrenia and control groups, with no significant differences observed between them. Normative modeling with multiple timepoints enables the identification of patients with significant static decreases and dynamic changes of morphometric similarity over time and provides further insight into the pervasiveness of morphometric similarity abnormalities across individuals with chronic schizophrenia.

Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles

Clinical high-risk (CHR) is a prodromal period before psychosis characterized by attenuated, transient, or intermittent psychotic symptoms and declining functioning. They exhibit eye movement abnormalities and brain functional damage compared with schizophrenia, potentially increasing vulnerability to psychosis. This study investigates eye movement dysfunction and brain activity alterations in CHR and first-episode schizophrenia (FSZ) individuals to identify early biomarkers for psychosis progression. Twenty-seven drug-naïve FSZ, 25 CHR, and 28 healthy controls (HCs) were recruited for eye-tracking tasks and resting-state functional magnetic resonance imaging to evaluate eye movement and regional homogeneity (ReHo) differences. Machine-learning algorithms were used to differentiate FSZ from CHR. In combination with the Allen Human Brain Atlas (AHBA), transcriptome-neuroimaging analysis was applied to identify ReHo-related gene expression profiles. FSZ exhibited a wide range of eye movement abnormalities across multiple tasks, while certain abnormalities were already present in CHR. Abnormal ReHo alterations were found in orbitofrontal gyrus, temporal gyrus, and cingulum among three groups, associated with specific eye movement parameters. These differences in eye movement and ReHo allowed for high-accuracy discrimination between them. Genetic analysis identified significant genes in FSZ and CHR, enriched in various biological functions and pathways (all corrected p < 0.05). FSZ and CHR exhibited different eye movement and ReHo patterns, indicating potential as early biomarkers. Our findings reveal correlations between these ReHo patterns and gene expression profiles using AHBA database, shedding light on possible genetic mechanisms underlying brain function in FSZ and CHR.

Molecular, physiological and functional features underlying antipsychotic medication use related cortical thinning

Use of antipsychotic medication is related to thinning of the cerebral cortex, but the underlying mechanisms of this effect remain largely unknown. Here, we investigated potential mechanisms across multiple levels of description by comparing antipsychotic medication related cortical thinning to atlases of normative neurotransmitter distributions, structural and functional organization of the brain, and meta-analyses of functional activation from the Neurosynth database. We first analyzed a single-site discovery sample of patients (N = 131) with early psychosis for whom antipsychotic related cortical thinning was estimated based on lifetime exposure to antipsychotics. Findings were replicated using data from a large (N ≥ 2168) ENIGMA meta-analysis on schizophrenia patients. We discovered that antipsychotic related cortical thinning is associated with a number of neurotransmitter systems, most notably the serotonin system, as well as physiological measures, functional networks and neural oscillatory power distributions typical for regions subserving higher cognition. At the functional level, antipsychotic related cortical thinning affects regions involved in executive function and motivation, but not perception. These results show how molecular, physiological, and large-scale functional patterns may underlie antipsychotic related cortical thinning.

The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia Program (AMP® SCZ), including the rationale for various choices. This protocol includes T1- and T2-weighted structural scans, resting-state fMRI, and diffusion-weighted imaging collected at two time points, approximately 2 months apart. We also present preliminary variance component analyses of several measures, such as signal- and contrast-to-noise ratio (SNR/CNR) and spatial smoothness, to provide quantitative data on the relative percentages of participant, site, and platform (i.e., scanner model) variance. Site-related variance is generally small (typically <10%). For the SNR/CNR measures from the structural and fMRI scans, participant variance is the largest component (as desired; 40-76%). However, for SNR/CNR in the diffusion scans, there is substantial platform-related variance (>55%) due to differences in the diffusion imaging hardware capabilities of the different scanners. Also, spatial smoothness generally has a large platform-related variance due to inherent, difficult to control, differences between vendors in their acquisitions and reconstructions. These results illustrate some of the factors that will need to be considered in analyses of the AMP SCZ neuroimaging data, which will be the largest CHR cohort to date.Watch Dr. Harms discuss this article at https://vimeo.com/1059777228?share=copy#t=0 .

Functional vs Structural Cortical Deficit Pattern Biomarkers for Major Depressive Disorder

Importance

Major depressive disorder (MDD) is a severe mental illness characterized more by functional rather than structural brain abnormalities. The pattern of regional homogeneity (ReHo) deficits in MDD may relate to underlying regional hypoperfusion. Capturing this functional deficit pattern provides a brain pattern-based biomarker for MDD that is linked to the underlying pathophysiology.

Objective

To examine whether cortical ReHo patterns provide a replicable biomarker for MDD that is more sensitive than reduced cortical thickness and evaluate whether the ReHo MDD deficit pattern reflects regional cerebral blood flow (RCBF) deficit patterns in MDD and whether a regional vulnerability index (RVI) thus constructed may provide a concise brain pattern-based biomarker for MDD.

Design, Settings, and Participants

The UK Biobank (UKBB) participants had ReHo and structural measurements. Participants from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) Consortium were included for measuring the MDD structural cortical deficit pattern. The UKBB ReHo and ENIGMA cortical thickness effect sizes for MDD were used to test the deficit patterns in the Amish Connectome Project (ACP) with ReHo, structural, and RCBF data. Finally, the Ament Clinic Inc (ACI) sample had RCBF data measured using single-photon emission computed tomography. Data were analyzed from August 2021 to September 2024.

Exposures

ReHo and structural measurements.

Results

Included in this analysis were 4 datasets: (1) UKBB (N = 4810 participants; 2220 with recurrent MDD and 2590 controls; mean [SD] age, 63.0 [7.5] years; 1121 female [50%]), (2) ENIGMA (N = 10 115 participants; 2148 with MDD and 7957 healthy controls; mean [SD] age, 39.9 [10.0] years; 5927 female [59%]), (3) ACP (N = 204 participants; 68 with a lifetime diagnosis of MDD and 136 controls; mean [SD] age, 41.0 [14.5] years; 104 female [51%]), and (4) ACI (N = 372 participants; 296 with recurrent MDD and 76 controls; mean [SD] age, 45.3 [17.2] years; 189 female [51%]). MDD participants had lower cortical ReHo in the cingulum, superior temporal lobe, frontal lobe, and several other areas, with no significant differences in cortical thickness. The regional pattern of ReHo MDD effect sizes was significantly correlated with that of RCBF obtained from 2 independent datasets (Pearson r = 0.52 and Pearson r = 0.46; P < 10-4). ReHo and RCBF functional RVIs showed numerically stronger effect sizes (Cohen d = 0.33-0.90) compared with structural RVIs (Cohen d = 0.09-0.20). Elevated ReHo-based RVI-MDD values in individuals with MDD were associated with higher depression symptom severity across cohorts.

Conclusions and Relevance

Results of this case-control study suggest that the ReHo MDD deficit pattern reflected cortical hypoperfusion and was regionally specific in MDD. ReHo-based RVI may serve as a sensitive functional biomarker for MDD.

Spatial patterns of individual morphological deformation in schizophrenia: Putative cortical compensatory of unaffected sibling

BACKGROUND

Neuroimaging advancements have revealed morphological deformation across various indicators, illuminating the neuropathological origins of schizophrenia. However, consolidating the findings across indicators and assessing regional global deformation at individual-level poses a significant challenge.

METHODS

We propose individual morphological deformation index (IMDI) as potential biomarker for schizophrenia leveraging a distance algorithm that incorporates three key indicators (cortical thickness, gyrification, and volume), and applied it for 199 schizophrenia patients, 218 healthy controls, and 47 unaffected siblings. Additionally, we studied the relationships between polygenic risks, symptomology, cognition, social functioning and regional IMDI.

RESULTS

Our findings reveal significantly higher IMDI in specific brain regions (bilateral pars opercularis, lateral orbitofrontal, left superior parietal, right pars orbitalis, and superior temporal) in patients, demonstrating two distinct spatial patterns linked to either isolated indicator reduction or concurrent declines across multiple indicators. Notably, unaffected siblings exhibited higher IMDI than controls, primarily due to cortical volume expansion in the right pars opercularis and superior temporal regions. Patients with higher IMDI had more severe positive symptoms, impaired cognition, reduced social functioning and selfcare ability. Participants with higher polygenic scores showed higher IMDI specifically in left caudal middle frontal regions.

CONCLUSIONS

The proposed IMDI biomarker offers an objective, interpretable way to quantify global regional deformation and integrate disparate neuroimaging indicators. Our results indicate that schizophrenia-related cortical deformations encompass sensorimotor, attention, default mode, and frontoparietal networks, exhibiting at least two spatial patterns. Moreover, siblings may exhibit compensation in cortical volume. These insights offer a novel perspective on the neuroanatomical underpinnings of schizophrenia.

Resting-state functional connectivity correlates of brain structural aging in schizophrenia

A large body of research has shown that schizophrenia patients demonstrate increased brain structural aging. Although this process may be coupled with aberrant changes in intrinsic functional architecture of the brain, they remain understudied. We hypothesized that there are brain regions whose whole-brain functional connectivity at rest is differently associated with brain structural aging in schizophrenia patients compared to healthy controls. Eighty-four male schizophrenia patients and eighty-six male healthy controls underwent structural MRI and resting-state fMRI. The brain-predicted age difference (b-PAD) was a measure of brain structural aging. Resting-state fMRI was applied to obtain global correlation (GCOR) maps comprising voxelwise values of the strength and sign of functional connectivity of a given voxel with the rest of the brain. Schizophrenia patients had higher b-PAD compared to controls (mean between-group difference + 2.9 years). Greater b-PAD in schizophrenia patients, compared to controls, was associated with lower whole-brain functional connectivity of a region in frontal orbital cortex, inferior frontal gyrus, Heschl's Gyrus, plana temporale and polare, insula, and opercular cortices of the right hemisphere (rFTI). According to post hoc seed-based correlation analysis, decrease of functional connectivity with the posterior cingulate gyrus, left superior temporal cortices, as well as right angular gyrus/superior lateral occipital cortex has mainly driven the results. Lower functional connectivity of the rFTI was related to worse verbal working memory and language production. Our findings demonstrate that well-established frontotemporal functional abnormalities in schizophrenia are related to increased brain structural aging.

Estimating Multimodal Structural Brain Variability in Schizophrenia Spectrum Disorders: A Worldwide ENIGMA Study

OBJECTIVE

The clinical diversity of schizophrenia is reflected by structural brain variability. It remains unclear how this variability manifests across different gray and white matter features. In this meta- and mega-analysis, the authors investigated how brain heterogeneity in schizophrenia is distributed across multimodal structural indicators.

METHODS

The authors used the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6,037 individuals for a given brain measure. Variability and mean values of cortical thickness, cortical surface area, cortical folding index, subcortical volume, and fractional anisotropy were examined in individuals with schizophrenia and healthy control subjects.

RESULTS

Individuals with schizophrenia showed greater variability in cortical thickness, cortical surface area, subcortical volume, and fractional anisotropy within the frontotemporal and subcortical network. This increased structural variability was mainly associated with psychopathological symptom domains, and the schizophrenia group frequently displayed lower mean values in the respective structural measures. Unexpectedly, folding patterns were more uniform in individuals with schizophrenia, particularly in the right caudal anterior cingulate region. The mean folding values of the right caudal anterior cingulate region did not differ between the schizophrenia and healthy control groups, and folding patterns in this region were not associated with disease-related parameters.

CONCLUSIONS

In patients with schizophrenia, uniform folding patterns in the right caudal anterior cingulate region contrasted with the multimodal variability in the frontotemporal and subcortical network. While variability in the frontotemporal and subcortical network was associated with disease-related diversity, uniform folding may indicate a less flexible interplay between genetic and environmental factors during neurodevelopment.

Drug-Induced Cognitive Impairment

Drug-induced cognitive impairment (DICI) is a well-established, yet under-recognised, complication of many types of pharmacological treatment. While there is a large body of scientific literature on DICI, most papers are about drug-induced dementia in the elderly and one specific drug class. However, DICI also comprises subclinical symptoms, domain-specific forms of cognitive impairment as well as mild cognitive impairment (MCI), and delirium. Even mild forms of DICI, if not recognised as such, can have deleterious and life-long consequences. In addition, DICI also occurs in younger adults and in children, and has been reported with many different drug classes. The aim of this review is to raise awareness of DICI by providing an overview on the type(s) and symptoms of observed DICI and the suspected underlying mechanism(s) for various drug classes: antiseizure medications, antidepressants, antiparkinsonian drugs, antipsychotics, lithium, benzodiazepines/Z-drugs, opioids, first-generation antihistamines, drugs for urinary incontinence, proton pump inhibitors, glucocorticoids, NSAIDs, statins, antihypertensives, and chemotherapeutic agents.

Network-based Molecular Constraints on in vivo Synaptic Density Alterations in Schizophrenia

Converging neuroimaging, genetic, and post-mortem evidence show a fundamental role of synaptic deficits in schizophrenia pathogenesis. However, the underlying molecular and cellular mechanisms that drive the onset and progression of synaptic pathology remain to be established. Here, we used synaptic density positron emission tomography (PET) imaging using the [11C]UCB-J radiotracer to reveal a prominent widespread pattern (p FWE < 0.05) of lower synaptic density in individuals with schizophrenia (n=29), compared to a large sample of healthy controls (n=93). We found that the spatial pattern of lower synaptic density in schizophrenia is spatially aligned (r cca = 0.67; p < 0.001) with higher normative distributions of GABAA/BZ, 5HT1B, 5HT2A, and 5HT6, and lower levels of CB1 and 5HT1A. Competing neighborhood deformation network models revealed that regional synaptic pathology strongly correlated with estimates predicted using a model constrained by both interregional structural connectivity and molecular similarity (.42 < r < .61; p FWE < 0.05). These data suggest that synaptic pathology in schizophrenia is jointly constrained by both global axonal connectivity and local molecular vulnerability. Simulation-based network diffusion models were used to identify regions that may represent the initial sources of pathology, nominating left prefrontal areas (p FWE < 0.05) as potential foci from which synaptic pathology initiates and propagates to molecularly similar areas. Overall, our findings provide in vivo evidence for widespread deficit in synaptic density in schizophrenia that is jointly constrained by axonal connectivity and molecular similarity between regions, and that synaptic deficits spread from initial source regions to axonally connected and molecularly similar territories.

Local cortical structure pattern and genetic links in schizophrenia: An MRI and CRISPR/Cas9 study

While the etiology of schizophrenia (SZ) remains elusive, its diverse phenotypes suggest the involvement of distinct functional cortical areas, and the heritability of SZ implies the underlying genetic factors. This study aimed to integrate imaging and molecular analyses to elucidate the genetic underpinnings of SZ. We investigated the local cortical structural pattern changes in Brodmann areas (BAs) by calculating the cortical structural pattern index (SPI) using magnetic resonance imaging analysis from 194 individuals with SZ and 330 controls. Significant local structural changes were detected in certain Brodmann areas in symmetric or asymmetric patterns, such as symmetric changes in the BA4 primary motor area and BA23 part of posterior cingulate cortex, and asymmetric changes in the BA13 insula, BA11 inferior orbitofrontal area, and BA 24, and BA 31 cingulate cortex. Following genome-wide association tests, we found genetic variants and SNP-mapped genes and verified the areal preferential expression profiles in the developing human and mouse neocortex. Finally, we performed a loss-of-function analysis using the CRISPR/Cas9 system to investigate the effects of disrupting the SZ-related SNP-mapped Morf4l1, Reep3, or Tmed3 gene on cortical cell fate to understand their roles in generating appropriate composition of cortical neurons. This study outlines a pipeline for identifying local structural changes, associated genetic causes, and potential molecular mechanisms underlying mental disorders. Additionally, these data shed light on establishing a structurally integral cerebral cortex for higher cognitive functions.

Brain Structural Alterations Underlying Mood-Related Deficits in Schizophrenia

Background/Objectives: Schizophrenia (SZ) is a complex psychiatric disorder characterized by neurodegenerative processes, but the structural brain alterations associated with its progression remain poorly understood. This study investigated structural brain changes in SZ, particularly in the fronto-temporal and limbic regions, and explored their relationship with symptom severity, with a focus on mood- and emotion-related symptoms. Methods: We analyzed structural MRI data from 74 SZ patients and 91 healthy controls (HCs) using voxel-based morphometry (VBM) to compare whole-brain grey matter volumes (GMVs). The analysis focused on the fronto-temporal and limbic regions, and correlations between GMV and symptom severity were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Generalized Psychopathology (GP) scale. Results: SZ patients exhibited significant reductions in GMV in the fronto-temporal and limbic regions, including the dorsolateral prefrontal cortex (dlPFC) and the temporal pole, compared to HCs. Notably, a significant positive association was found between GMV in the right inferior temporal gyrus (ITG) and the severity of generalized psychopathology, as well as with anxiety, depression, mannerisms, and unusual thought content. Further post hoc analysis identified a specific cluster of mood-related symptoms contributing to the GP scale, which correlated with GMV changes in the right ITG. Conclusions: Our findings provide new evidence of structural brain alterations in SZ, particularly in the fronto-temporal and limbic regions, suggesting a progressive neurodegenerative pattern. The role of the right ITG in mood- and emotion-related symptoms requires further exploration, as it could offer insights into SZ pathophysiology and aid in distinguishing SZ from other mood-related disorders.

Measuring Cognitive Impairments Associated With Schizophrenia in Clinical Practice: Overview of Current Challenges and Future Opportunities

BACKGROUND

Cognitive impairment associated with schizophrenia (CIAS) negatively impacts daily functioning, quality of life, and recovery, yet effective pharmacotherapies and practical assessments for clinical practice are lacking. Despite the pivotal progress made with establishment of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) for clinical research, implementation of the full MCCB is too time-consuming and cost-ineffective for most clinicians in clinical practice.

STUDY DESIGN

Here we discuss current assessments in relation to delivery format (interview-based and performance-based), validity, ease of use for clinicians and patients, reliability/reproducibility, cost-effectiveness, and suitability for clinical implementation. Key challenges and future opportunities for improving cognitive assessments are also presented.

STUDY RESULTS

Current assessments that require 30 min to complete would have value in clinical settings, but the associated staff training and time required might preclude their application in most clinical settings. Initial profiling of cognitive deficits may require about 30 min to assist in the selection of evidence-based treatments; follow-up monitoring with brief assessments (10-15 min in duration) to detect treatment-related effects on global cognition may complement this approach. Guidance on validated brief cognitive tests for the strategic monitoring of treatment effects on CIAS is necessary.

CONCLUSIONS

With increased advancements in technology-based and remote assessments, development of validated formats of remote and in-person assessment, and the necessary training models and infrastructure required for implementation, are likely to be of increasing clinical relevance for future clinical practice.

Schizophrenia and Neurodevelopment: Insights From Connectome Perspective

BACKGROUND

Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS).

STUDY DESIGN

We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field.

STUDY RESULTS

Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients.

CONCLUSIONS

Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population

BACKGROUND

Patients with schizophrenia (SCZ) experience constipation at significantly higher rates compared with the general population. This relationship suggests a potential genetic overlap between these two conditions.

METHODS

We analyzed genome-wide association study (GWAS) data for both SCZ and constipation using a five-part approach. The first and second parts assessed the overall and local genetic correlations using methods such as linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS). The third part investigated the causal association between the two traits using Mendelian randomization (MR). The fourth part employed conditional/conjunctional false discovery rate (cond/conjFDR) to analyze the genetic overlap with different traits based on the statistical theory. Finally, an LDSC-specifically expressed gene (LDSC-SEG) analysis was conducted to explore the tissue-level associations.

RESULTS

Our analyses revealed both overall and specific genetic correlations between SCZ and constipation at the genomic level. The MR analysis suggests a positive causal relationship between SCZ and constipation. The ConjFDR analysis confirms the genetic overlap between the two conditions and identifies two genetic risk loci (rs7583622 and rs842766) and seven mapped genes (GPR75-ASB3, ASB3, CHAC2, ERLEC1, GPR75, PSME4, and ACYP2). Further investigation into the functions of these genes could provide valuable insights. Interestingly, disease-related tissue analysis revealed associations between SCZ and constipation in eight brain regions (substantia nigra, anterior cingulate cortex, hypothalamus, cortex, hippocampus, cortex, amygdala, and spinal cord).

CONCLUSION

This study provides the first genetic evidence for the comorbidity of SCZ and constipation, enhancing our understanding of the pathophysiology of both conditions.

Associations of polygenic risk scores differentiating attention-deficit hyperactivity disorder from autism spectrum disorder with cognitive and cortical alterations in Schizophrenia patients

Schizophrenia (SCZ) is a clinically and genetically heterogeneous disorder that shares genetic factors with autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). A genome-wide association study (GWAS) differentiating ADHD from ASD was performed recently. In this study, we investigated whether polygenic risk scores (PRSs) differentiating ASD from ADHD are associated with cognitive impairments and alterations in cortical structures in SCZ patients. Based on the GWAS data (9,315 ASD and 11,964 ADHD patients), PRSs differentiating ADHD from ASD (indicating a greater risk of ADHD and a lower risk of ASD) were calculated for SCZ patients (n = 168). Cognitive performance, including verbal comprehension (VC), perceptual organization (PO), working memory (WM), and processing speed (PS), was assessed using the WAIS-III (n = 145). The surface areas and cortical thicknesses of 34 bilateral brain regions were extracted using FreeSurfer (n = 126). We examined the associations of these PRSs with cognitive performance and cortical structures in SCZ patients. Among the four cognitive domains, a higher PRS, indicating a greater risk of ADHD, was associated with impaired WM in SCZ patients (beta=-0.21, p = 0.012). A lower PRS, indicating a greater risk of ASD, was associated with decreased surface areas of the left medial orbitofrontal (beta = 0.21, p = 8.29 × 10- 4), left entorhinal (beta = 0.21, p = 0.025), left postcentral (beta = 0.18, p = 7.52 × 10- 3), right fusiform (beta = 0.17, p = 6.64 × 10- 3), and left fusiform cortices (beta = 0.17, p = 7.77 × 10- 3) in SCZ patients. A higher PRS, indicating a greater risk of ADHD, was associated with decreased cortical thickness in the bilateral transverse temporal regions (left, beta=-0.17, p = 0.039; right, beta=-0.17, p = 0.045). Our study revealed a relationship between genetic factors that differentiate ADHD patients from ASD patients and both cortical structure and cognitive performance in SCZ patients. These findings suggest that the heterogeneity of SCZ might be partly derived from genetic factors related to neurodevelopmental and psychiatric disorders other than SCZ.

Changes in degree centrality and its associated genes: A longitudinal study of patients with schizophrenia undergoing pharmacological treatment

BACKGROUND

The role of degree centrality (DC) in schizophrenia (SCZ), its trajectory following pharmacological treatment, and its potential as a prognostic biomarker and genetic mechanism remain unclear.

METHODS

We recruited 51 healthy controls (HC) and 56 patients with SCZ. Additionally, the SCZ patients underwent three months of antipsychotic medication treatment. We collected resting-state functional magnetic resonance imaging data, clinical variables, and conducted analyses using support vector machines, support vector regression, and gene expression correlation analysis.

RESULTS

Our study revealed that SCZ patients had generally reduced DC values in the cerebral cortex compared to HC at baseline, with increased DC values observed in the left occipital gyrus. After three months of treatment, SCZ patients exhibited a significant decrease in DC values in the left fusiform gyrus and an increase in the left inferior parietal gyrus. Variations in DC values in SCZ patients were associated with multiple genes, primarily enriched in molecular functions.

CONCLUSION

Changes in DC values in the right inferior occipital/fusiform gyrus and right calcarine/middle occipital gyrus may serve as neuroimaging markers to differentiate between HC and SCZ patients. Additionally, DC values in the left middle/postcentral gyrus could be used to predict treatment outcomes. Transcriptome-neuroimaging spatial correlation analysis provides valuable insights into the neurobiological mechanisms underlying SCZ pathology.

Depressive and Negative Symptoms in the Early and Established Stages of Schizophrenia: Integrating Structural Brain Alterations, Cognitive Performance, and Plasma Interleukin 6 Levels

Background

Depressive and negative symptoms are related to poor functional outcomes in schizophrenia. Cognitive deficits, reduced brain cortical thickness and volumes, and inflammation may contribute to depressive and negative symptoms, but pharmacological treatment and disease progression may confound the associations.

Methods

We evaluated whether higher plasma interleukin 6 (IL-6) levels would be associated with more severe negative or depressive symptoms in schizophrenia and explored illness stage utilizing early (BeneMin [Benefit of Minocycline on Negative Symptoms of Psychosis: Extent and Mechanism], n = 201, 72.8% male) and established (iRELATE [Immune Response & Social Cognition in Schizophrenia], n = 94, 67.3% male) schizophrenia cohorts. Using structural equation modeling in a subsample (iRELATE: n = 42, 69.0% male; BeneMin: n = 102, 76.5% male) with data on structural brain metrics (cortical thickness and volume), general cognitive performance, and plasma IL-6 levels, we assessed the interrelationships between these variables on depressive and negative symptom severity in early and established schizophrenia samples combined and in early schizophrenia only. All analyses were adjusted for sex, age, and chlorpromazine equivalent dose.

Results

Higher plasma IL-6 levels were related to more severe depressive symptoms in early schizophrenia (p < .05) and negative symptoms in established schizophrenia (p < .05). Structural equation modeling findings in early and established schizophrenia samples combined and early schizophrenia only showed that the interrelationship between higher plasma IL-6 levels, structural brain metrics, and general cognitive performance did not predict the severity of depressive and negative symptoms (p > .05). Higher plasma IL-6 levels and lower general cognitive performance were associated with reduced brain metrics (p < .05).

Conclusions

Our results indicate that higher plasma IL-6 levels may be differently associated with the severity of depressive and negative symptoms dependent on the illness stage. Future work identifying elevated levels of inflammation in larger samples may allow stratification and personalized intervention by subgroups who are at risk of poor outcomes.

Associations between structural brain changes and blood neurofilament light chain protein in treatment-resistant schizophrenia

OBJECTIVE

Around 30% of people with schizophrenia are refractory to antipsychotic treatment (treatment-resistant schizophrenia). Abnormal structural neuroimaging findings, in particular volume and thickness reductions, are often described in schizophrenia. Novel biomarkers of active brain pathology such as neurofilament light chain protein are now expected to improve current understanding of psychiatric disorders, including schizophrenia. This study explored whether treatment-resistant schizophrenia individuals exhibit different associations between plasma neurofilament light chain protein levels and regional cortical thickness reductions compared with controls.

METHODS

Plasma neurofilament light chain protein levels were measured, and T1-weighted magnetic resonance imaging sequences were obtained and processed via FreeSurfer for each participant. General linear models adjusting for age and body mass index were estimated to determine whether the interaction between diagnostic group and plasma neurofilament light chain protein level predicted lower cortical thickness across frontotemporal structures and the insula.

RESULTS

A total of 79 participants were included: 37 treatment-resistant schizophrenia and 42 healthy controls. Significant (false discovery rate-corrected) cortical thinning of the left (p = 0.005, η2p = 0.100) and right (p = 0.002, η2p = 0.149) insula, and left inferior temporal gyrus (p < 0.001, η2p = 0.143) was associated with higher levels of plasma neurofilament light chain protein in treatment-resistant schizophrenia, but not in healthy controls.

CONCLUSIONS

The association between regional thickness reduction of the bilateral insula and left inferior temporal gyrus with plasma neurofilament light chain protein may reflect a neuroprogressive component to schizophrenia, which is not observed in the normal population.

Polygenic Resilience Scores are Associated With Lower Penetrance of Schizophrenia Risk Genes, Protection Against Psychiatric and Medical Disorders, and Enhanced Mental Well-Being and Cognition

BACKGROUND AND HYPOTHESIS

In the past decade, significant advances have been made in finding genomic risk loci for schizophrenia (SCZ). This, in turn, has enabled the search for SCZ resilience loci that mitigate the impact of SCZ risk genes. We identified the first genomic resilience profile for SCZ, completely independent from known risk loci for SCZ, though it remains unclear whether resilience loci foster protection against adverse states associated with SCZ involving clinical, cognitive, and brain-structural phenotypes.

STUDY DESIGN

We analyzed genomic and phenotypic data from 459 784 participants from the UK Biobank, using regression models to estimate interaction effects of resilience and SCZ risk scores on phenotypes spanning multiple scales.

STUDY RESULTS

We found that resilience loci for SCZ afforded protection against lifetime psychiatric (schizophrenia, bipolar disorder, anxiety, and depression) and medical disorders (such as type 2 diabetes, cardiovascular, and digestive and metabolic disorders). Resilience loci also moderated the impact of SCZ loci, associated with protection against self-harm behavior and greater fluid intelligence scores. The main effects of resilience are also observed in the absence of a moderating effect in some instances, such as associations with larger brain structures.

CONCLUSIONS

Overall, this study highlights a wide range of phenotypes that are significantly associated with resilience loci within the general population, revealing distinct patterns separate from those associated with SCZ risk loci. Resilience loci may protect against serious psychiatric and medical outcomes, comorbidities, and cognitive impairment. Therefore, it is conceivable that resilience loci facilitate adaptive processes linked to improved health and life expectancy.

Shared genetic architecture and causal relationship between frailty and schizophrenia

The complex relationship between frailty and schizophrenia has yet to be fully understood. This study aims to clarify their relationship by investigating their genetic links. We hypothesize a shared genetic architecture and a bidirectional causal relationship between the two conditions. Utilizing summary genetic data from European genome-wide association studies, we analyzed genetic associations through global and local correlations, shared genomic loci, tissue enrichments, and functional genes. Bidirectional Mendelian Randomization (MR) was employed to infer causality. Our findings show a positive genetic correlation between frailty and schizophrenia (LDSC: rg = 0.117, p = 6.686 × 10-7; HDL: rg = 0.101, p = 5.63 × 10-13) and local correlations in three genomic regions (chr9: 94167203-96671698, p = 2.21 × 10-6; chr11: 112459488-114257728, p = 1.01 × 10-5; and chr18: 77149991-78017158, p = 9.57 × 10-6). We identified 111 genomic loci associated with both conditions and demonstrated that genetic variants for frailty and schizophrenia share tissue enrichments and functional genes in brain. MR analysis suggests that frailty increases the likelihood of schizophrenia (OR: 1.763, 95% CI: 1.259-2.468, p = 0.001) and vice versa (β: 0.012, 95% CI: 0.006-0.018, p < 0.001). Our research supports the presence of a shared genetic basis and bidirectional causality between frailty and schizophrenia. These findings necessitate further investigation in diverse populations to confirm and expand on this genetic understanding.

Neurobiological Perturbations in Bipolar Disorder Compared With Schizophrenia: Evidence From Cell Cultures and Brain Organoids

Bipolar disorder (BD) and schizophrenia (SCZ) are uniquely human disorders with a complex pathophysiology that involves adverse neuropathological events in brain development. High disease polygenicity and limited access to live human brain tissue make these disorders exceedingly challenging to study mechanistically. Cellular cultures and brain organoids generated from human-derived pluripotent stem cells preserve the genetic background of the donor cells and recapitulate some of the defining characteristics of human brain architecture and early spatiotemporal development. These model systems have already proven successful in deciphering some of the neuropathological perturbations in BD and SCZ, and methodological advancements, such as the functional integration of 2 or more region-specific organoids and organoid transplantation in animals, promise to deliver increasingly refined insights. Here, we review a selection of recent discoveries achieved by stem cell-based models, with a particular focus on patterns of cellular and molecular convergence and divergence between BD and SCZ. First, we provide a brief overview of the evidence from glial and neuronal cell cultures and brain organoids, centering our discussion on several key functional domains, including neuroinflammation, neuronal excitability, and mitochondrial function. Then, we review recent findings demonstrating the power of integrating stem cell-based systems with gene editing technologies to elucidate the functional consequences of risk variants identified through genetic association studies. We end with a discussion of current challenges and some promising avenues for future research.

The Psychosis MRI Shared Data Resource (Psy-ShareD)

Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.

Neuroanatomical Deficits in Visual Cortex Subregions of Individuals with Psychosis Spectrum Disorders linked to Symptoms, Cognition, and Childhood Trauma

Objective

The visual system is a significant site of pathology in psychosis spectrum disorders. However, there is limited research investigating human visual cortex (VC) subregions in this population. Using data from the Bipolar-Schizophrenia Network on Intermediate Phenotypes Consortium (BSNIP-1, BSNIP-2, PARDIP), this study examined structural measures in VC subregions in individuals with psychosis spectrum disorders.

Methods

Cortical surface area and thickness in five VC subregions (hOc1, hOc2, hOc3v, hOc4v, MT) were quantified using FreeSurfer v7.1.0 and compared between individuals with psychosis ( n =1211) and healthy controls ( n =734). Regional specificity was examined by controlling for total surface area or mean cortical thickness. ComBat was used to harmonize scanner effects. Associations between VC measures and symptom severity, cognition, and childhood trauma scores were assessed.

Results

Individuals with psychosis demonstrated smaller surface area in hOc1, hOc2, and hOc3v, and lower cortical thickness in all five VC subregions compared to healthy controls. Thickness reductions in hOc1, hOc4v, and MT were regionally specific. hOc4v and MT were among the top three regions exhibiting the most robust cortical thickness deficits ( d = -0.38 to -0.40) across all VC and Desikan-Killiany brain regions. Lower thickness in mid-level visual subregions were associated with greater positive symptoms, poorer cognition, and higher childhood trauma scores.

Conclusions

This study demonstrates that the visual cortex is among the most profoundly affected brain regions in psychotic disorders. Different patterns of area and thickness changes across early and mid-level visual subregions, along with their varying associations with clinical measures, suggest distinct developmental and disease-related influences.

Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

BACKGROUND

The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear.

METHODS

Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers.

RESULTS

We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D1, D2, dopamine transporter), acetylcholine (VAChT, M1), gamma-aminobutyric acid A (GABAA), and glutamate (NMDA) receptors as key predictors for SSD (R2adjusted = 0.58, false discovery rate [FDR]-corrected p < .05) and CHR-P (R2adjusted = 0.6, pFDR < .05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis.

CONCLUSIONS

Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.

Psychosis-linked Symptoms and Structural Brain Patterns in Cognitive Subgroups among Familial High-Risk Children in the ABCD Study

Objective

Children at familial high risk for psychosis (FHR) are at substantially increased risk for psychotic disorders and other serious mental illnesses. Identifying risk subgroups within FHR youth may enhance prediction models to identify children at greatest risk for potential intervention. This study investigated psychosis-linked symptoms and structural brain patterns in neurocognitive subgroups among FHR children in the Adolescent Brain Cognitive Development (ABCD) Study using baseline, 2-year, and 4-year follow-up data.

Methods

Among children with first- and second-degree family history of psychosis, neurocognitive subgroups were defined using NIH Toolbox Cognitive Battery baseline age-corrected total scores: children with low (FHR-LC, 0-33%, n=234), moderate (FHR-MC, 33-66%, n=261), and high (FHR-HC, 66-100%, n=277) cognitive performance. Psychiatric symptoms were assessed using Prodromal Questionnaire-Brief Child Version (PQ-BC) and Childhood Behavior Checklist (CBCL). Regional vulnerability indices (SSD-RVIs), which quantify the similarity of participants' structural brain patterns to the patterns found in adults with schizophrenia spectrum disorders, were calculated using cortical thickness measures following rigorous quality control.

Results

At baseline, FHR-LC had significantly higher PQ-BC and CBCL scores, and trend-level higher SSD-RVIs compared to FHR-HC. Longitudinally, PQ-BC and CBCL scores decreased with age across all FHR participants, while SSD-RVIs remained stable. No longitudinal cognitive subgroup-by-age interactions were observed, indicating that subgroup differences persisted over time.

Conclusion

Children at FHR who have concurrent poor cognitive performance exhibit elevated and stable clinical and imaging psychosis risk markers. This suggests that they may represent a risk subgroup with elevated vulnerability, presenting an opportunity for early identification and intervention.

Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility

Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

The study on agitation and structure of orbitofrontal cortex subregion in first-episode drug-naïve patients with schizophrenia

Agitation is one of the core symptoms of schizophrenia. The occurrence of agitation may be related to orbitofrontal cortex dysfunction. However, due to methodological heterogeneity, the relationship between agitation and orbitofrontal cortex subregions remains unclear. Based on the multi-dimensional structure of the orbitofrontal cortex subregion, this study aims to explore the relationship between orbitofrontal cortex structure and agitation in first-episode drug-naïve patients with schizophrenia. The study subjects included 50 first-episode drug-naïve patients with schizophrenia and 29 healthy controls. All participants underwent structure magnetic resonance imaging scanning. The patients' clinical symptoms were assessed using the Positive and Negative Syndrome Scale, and the agitation were evaluated using the Brief Agitation Rating Scale. SPSS 26.0 was used to compare the differences in the orbitofrontal cortex subregion between the two groups in different structure dimensions and then conduct a Pearson's partial correlations analysis to observe the relationship between orbitofrontal cortex subregion structure and agitation. There were no significant differences in demographic factors between the two groups. Our results show the folding index of the orbitofrontal cortex subregion in patients with schizophrenia were significantly smaller compared to the healthy controls. The surface area in the orbitofrontal cortex subregion is significantly negatively correlated with agitation in first-episode drug-naïve schizophrenia patients. These results suggest that structure alterations in the orbitofrontal cortex subregion may be involved in schizophrenia agitation.

Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections, and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an increasing flatter axis slope. We also observed that the slope of connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition.

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts

BACKGROUND

Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

METHODS

A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

RESULTS

In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

CONCLUSIONS

This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

The Contribution of Mosaic Chromosomal Alterations to Schizophrenia

BACKGROUND

Mosaic chromosomal alterations are implicated in neuropsychiatric disorders, but the contribution to schizophrenia (SCZ) risk for somatic copy number variations (sCNVs) emerging in early developmental stages has not been fully established.

METHODS

We analyzed blood-derived genotype arrays from 9715 patients with SCZ and 28,822 control participants of Chinese descent using a computational tool (MoChA) based on long-range chromosomal information to detect mosaic chromosomal alterations. We focused on probable early developmental sCNVs through stringent filtering. We assessed the burden of sCNVs across varying cell fraction cutoffs, as well as the frequency with which genes were involved in sCNVs. We integrated this data with the PGC (Psychiatric Genomics Consortium) dataset, which comprises 12,834 SCZ cases and 11,648 controls of European descent, and complemented it with genotyping data from postmortem brain tissue of 936 participants (449 cases and 487 controls).

RESULTS

Patients with SCZ had a significantly higher somatic losses detection rate than control participants (1.00% vs. 0.52%; odds ratio = 1.91; 95% CI, 1.47-2.49; two-sided Fisher's exact test, p = 1.49 × 10-6). Further analysis indicated that the odds ratios escalated proportionately (from 1.91 to 2.78) with the increment in cell fraction cutoffs. Recurrent sCNVs associated with SCZ (odds ratio > 8; Fisher's exact test, p < .05) were identified, including notable regions at 10q21.1 (ZWINT), 3q26.1 (SLITRK3), 1q31.1 (BRINP3) and 12q21.31-21.32 (MGAT4C and NTS) in the Chinese cohort, and some regions were validated with PGC data. Cross-tissue validation pinpointed somatic losses at loci like 1p35.3-35.2 and 19p13.3-13.2.

CONCLUSIONS

The study highlights the significant impact of mosaic chromosomal alterations on SCZ, suggesting their pivotal role in the disorder's genetic etiology.

Progressive structural alterations associated with negative symptoms in schizophrenia: A causal structural covariance network analysis

BACKGROUNDS

Aberrant brain structures in schizophrenia have been widely explored. However, the causal effects of negative symptoms on brain structural alterations are still unclear. This study aims to explore the synchronous and progressive alterations in gray matter volume (GMV) associated with negative symptoms.

METHODS

81 Deficit schizophrenia (DS) patients, 101 non-deficit schizophrenia (NDS) patients, and 177 healthy controls (HCs) were enrolled in this study. T1-weighted images were collected, and the severity of clinical symptoms in patients was evaluated. Then voxel-based morphometry and source-based morphometry were used for gray matter segmentation and structural covariance network construction. Finally, DS patients were ranked based on the severity of negative symptoms, and a causal structural covariance network (CaSCN) was constructed using Granger causality analysis.

RESULTS

Twenty-four independent components were identified. Among them, 20 components showed smaller GMV in patients with schizophrenia compared to HCs. Furthermore, DS exhibited decreased GMV in right inferior frontal gyrus triangular part, bilateral para-hippocampal gyrus, and bilateral anterior cerebellum compared to NDS. Both patient groups showed increased structural covariance across various brain regions compared to HCs. Additionally, DS exhibited decreased structural covariance in left middle frontal gyrus, bilateral inferior frontal gyrus and right superior frontal gyrus compared to NDS. In CaSCN, as negative symptoms worsened, the volume of bilateral caudate decreased along with the atrophy of bilateral para-hippocampal gyrus, the volume of bilateral thalamus increased along with the decline in multiple brain regions, and the decreased volume of bilateral posterior cingulate cortex resulted in increased volume of bilateral lingual gyrus and other brain regions.

CONCLUSIONS

The present study demonstrated the specific brain structural covariance patterns in DS, providing new evidence for the causal effects of negative symptoms on progressive structural abnormalities in schizophrenia.

Subgroups of cognitive impairments in schizophrenia characterized by executive function and their morphological features: a latent profile analysis study

BACKGROUND

The heterogeneity of cognitive impairments in schizophrenia has been widely observed. However, reliable cognitive boundaries to differentiate the subgroups remain elusive. The key challenge for cognitive subtyping is applying an integrated and standardized cognitive assessment and understanding the subgroup-specific neurobiological mechanisms. The present study endeavors to explore cognitive subgroups and identify their morphological features.

METHODS

A total of 920 schizophrenia patients and 169 healthy controls were recruited. MATRICS Consensus Cognitive Battery was applied to assess cognitive performance and recognize cognitive subgroups through latent profile and latent transition analysis. Cortical thickness and gray matter volume were employed for the morphological features across subgroups.

RESULTS

Four reproducible cognitive subgroups were identified, including multidomain-intact, executive-preserved, executive-deteriorated, and multidomain-deteriorated subgroup. After 12 weeks of follow-up, the cognitive characteristics of three out of the four subgroups kept stability, except for multidomain-deteriorated subgroup in which 48.8% of patients with improved cognition transited into the executive-deteriorated subgroup. Across subgroups, significant gradient features of brain structure were exhibited in fronto-temporal regions, hippocampus, and insula. Compared to healthy controls, multidomain-intact subgroup showed the most intact cognition and morphology, and multidomain-deteriorated subgroup with youngest age showed morphological decline in extensive regions. The remaining two subgroups showed intermediate cognitive performance, but could be distinguished by executive function and morphological differences in posterior cingulate cortex.

CONCLUSIONS

Our study provides novel insights into the heterogeneity of cognitive impairments in schizophrenia and the morphological features from cross-sectional and longitudinal levels, which could advance our understanding of complex cognition-morphology relationships and guide personalized interventions.

Advancement in utilization of magnetic resonance imaging and biomarkers in the understanding of schizophrenia

Historically, psychiatric diagnoses have been made based on patient’s reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders. The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal. There have been several studies that examine brain imaging in psychiatric disorders, but more work is needed to elucidate the complexities of the human brain. In this editorial, we examine two articles by Xu et al and Stoyanov et al, that show developments in the direction of using neuroimaging to examine the brains of people with schizophrenia and depression. Xu et al used magnetic resonance imaging to examine the brain structure of patients with schizophrenia, in addition to examining neurotransmitter levels as biomarkers. Stoyanov et al used functional magnetic resonance imaging to look at modulation of different neural circuits by diagnostic-specific scales in patients with schizophrenia and depression. These two studies provide crucial evidence in advancing our understanding of the brain in prevalent psychiatric disorders.

Choroid plexus morphology in schizophrenia and early-stage psychosis: A cross-sectional study

BACKGROUND

The choroid plexus is an important structure within the ventricular system. Schizophrenia has been associated with morphological changes to the choroid plexus but the presence and extent of alterations at different illness stages is unclear.

METHODS

We examined choroid plexus volumes in participants at clinical high-risk for psychosis (N = 110), participants with first-episode psychosis (N = 37), participants with schizophrenia (N = 28), clinical (N = 38) and non-clinical controls (N = 75). Automated segmentation (Gaussian mixture model) was used to estimate choroid plexus volumes from T1 magnetic resonance (MR) images. We then conducted a linear model and Bayes factor analysis to investigate group differences. In addition, the relationship between choroid plexus volumes and clinical characteristics was assessed.

RESULTS

Schizophrenia patients were characterized by increased choroid plexus and ventricular volume while first-episode psychosis and clinical high-risk for psychosis participants showed no differences in choroid plexus volumes. However, choroid plexus volumes in schizophrenia patients did not significantly differ from controls when controlling for ventricular volume. Finally, choroid plexus volumes were not associated with clinical characteristics in the clinical high-risk group.

CONCLUSION

Our findings suggest that morphological alterations are not specific to the choroid plexus in schizophrenia and early-stage psychosis. Previously reported choroid plexus abnormalities in schizophrenia patients could be explained by changes in ventricular volume.

Mismatch Negativity as an Index of Auditory Short-Term Plasticity: Associations with Cortisol, Inflammation, and Gray Matter Volume in Youth at Clinical High Risk for Psychosis

Mismatch negativity (MMN) event-related potential (ERP) component reduction, indexing N-methyl-D-aspartate receptor (NMDAR)-dependent auditory echoic memory and short-term plasticity, is a well-established biomarker of schizophrenia that is sensitive to psychosis risk among individuals at clinical high-risk (CHR-P). Based on the NMDAR-hypofunction model of schizophrenia, NMDAR-dependent plasticity is predicted to contribute to aberrant neurodevelopmental processes involved in the pathogenesis of schizophrenia during late adolescence or young adulthood, including gray matter loss. Moreover, stress and inflammation disrupt plasticity. Therefore, using data collected during the 8-center North American Prodrome Longitudinal Study (NAPLS-2), we explored relationships between MMN amplitudes and salivary cortisol, gray matter volumes, and inflammatory cytokines. Participants included 303 CHR-P individuals with baseline electroencephalography (EEG) data recorded during an MMN paradigm as well as structural magnetic resonance imaging (MRI) and salivary cortisol, of which a subsample (n = 57) also completed blood draws. More deficient MMN amplitudes were associated with greater salivary cortisol and pro-inflammatory cytokine levels in future CHR-Converters, but not among those who did not convert to psychosis within the next two years. More deficient MMN amplitude was also associated with smaller total gray matter volume across participants regardless of future clinical outcomes, and with subcortical gray matter volumes among future CHR-Converters only. These findings are consistent with the theory that deficient NMDAR-dependent plasticity results in an overabundance of weak synapses that are subject to over-pruning during psychosis onset, contributing to gray matter loss. Further, MMN plasticity mechanisms may interact with stress, cortisol, and neuroinflammatory processes, representing a proximal influence of psychosis.

MRI Study of the Brain Structural Characteristic in Persecutory Delusions

The structural MRI of the cortex, subcortical structures, hippocampal subfields, and brainstem regions in patients with persecutory delusions in schizophrenia (ICD-10 code: F20) and delusional disorder (ICD-10 code: F22.0) were analyzed. Patients demonstrated multiple regions of decreased cortical gray matter thickness, reduced volumes of the left thalamus and some hippocampal sub-fields compared to mentally healthy subjects. No alterations in brainstem structures were found. The results were analyzed within the framework of the transnosological model of delusions.

Embracing variability in the search for biological mechanisms of psychiatric illness

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations. Embracing such variability is necessary to understand pathophysiological mechanisms and develop more targeted and effective treatments.

mFusion: a multiscale fusion method bridging neuroimages to genes through neurotransmissions in mental health disorders

Mental health disorders emerge from complex interactions among neurobiological processes across multiple scales, which poses challenges in uncovering pathological pathways from molecular dysfunction to neuroimaging changes. Here, we proposed a multiscale fusion (mFusion) method to evaluate the relevance of each gene to the neuroimaging traits of mental health disorders. We combined gene-neuroimaging associations with gene-positron emission tomography (PET) and PET-neuroimaging associations using protein-protein interaction networks, where various genes traced by PET maps are involved in neurotransmission. Compared with previous methods, the proposed algorithm identified more disease genes on both simulated and empirical data sets. Applying mFusion to eight mental health disorders, we found that these disorders formed three clusters with distinct associated genes. In summary, mFusion is a promising tool of prioritizing genes for mental health disorders by establishing gene-PET-neuroimaging pathways.

A 10-Year Longitudinal Study of Brain Cortical Thickness in People with First-Episode Psychosis Using Normative Models

BACKGROUND

Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms.

DESIGN

Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms.

RESULTS

LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores.

CONCLUSIONS

This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time.

Genotype-dependent functional role of the anterior and posterior paraventricular thalamus in pavlovian conditioned approach

RATIONALE

The specific location of deviations from normative models of brain function varies considerably across individuals with the same diagnoses. However, as pathological processes are distributed across interconnected systems, this heterogeneity of individual brain deviations may also reveal similarities and differences between disorders. The paraventricular nucleus of the thalamus (PVT) is a potential switcher to various behavioral responses where functionally distinct cell types exist across its antero-posterior axis.

OBJECTIVES

This study aimed to test the hypothesis that genotype-dependent differences in the anterior and posterior PVT subregions (aPVT and pPVT) are involved in the Sign-tracking (ST) behavior expressed by C57BL/6J (C57) and DBA/2J (DBA) inbred mice.

METHODS

Based on previous findings, male mice of the two strains were tested at ten weeks of age. The density of c-Fos immunoreactivity along the antero-posterior axis of PVT was assessed following the expression of ST behavior. Selective excitotoxic lesions of the aPVT or the pPVT by the NMDA infusion were performed prior to development of ST behavior. Finally, the distribution of neuronal populations expressing the Drd2 and Gal genes (D2R + and Gal +) was measured by in situ hybridization (ISH).

RESULTS

The involvement of PVT subregions in ST behavior is strain-specific, as aPVT is crucial for ST acquisition in DBA mice while pPVT is crucial for C57 mice. Despite similar antero-posterior distribution of D2R + and Gal + neurons, density of D2R + neurons differentiate aPVT in C57 and DBA mice.

CONCLUSIONS

These genotype-dependent results offer valuable insights into the nuanced organization of brain networks and individual variability in behavioral responses.

Brain structural damage networks at different stages of schizophrenia

BACKGROUND

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia.

METHODS

We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks.

RESULTS

Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time.

CONCLUSIONS

Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis

BACKGROUND

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective.

METHODS

We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables.

RESULTS

A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance.

CONCLUSIONS

Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia's structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder's heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI's integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.