Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders.

Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.

A systematic review of focused ultrasound for psychiatric disorders: current applications, opportunities, and challenges.

OBJECTIVE: Advancements in MRI-guided focused ultrasound (MRgFUS) technology have led to the successful treatment of select movement disorders. Based on the comparative success between ablation and deep brain stimulation, interest arises in focused ultrasound (FUS) as a promising treatment modality for psychiatric illnesses. In this systematic review, the authors examined current applications of FUS for psychiatric conditions and explored its potential opportunities and challenges. METHODS: The authors performed a comprehensive review using the PRISMA guidelines of studies investigating psychiatric applications for FUS. Articles indexed on PubMed between 2014 to 2024 were included. The authors synthesized the psychiatric conditions treated, neural targets, outcomes, study design, and sonication parameters, and they reviewed important considerations for the treatment of psychiatric disorders with FUS. They also discussed active clinical trials in this research domain. RESULTS: Of 250 articles, 10 met the inclusion criteria. Eight articles investigated the clinical, safety, and imaging correlates of MRgFUS in obsessive-compulsive disorder (OCD), whereas 3 examined treatment-resistant depression. Bilateral anterior capsulotomy resulted in a full responder rate of 67% (≥ 35% reduction in the Yale-Brown Obsessive-Compulsive Scale score) and 33% (≥ 50% reduction in the score on the Hamilton Rating Scale for Depression) in OCD and treatment-resistant depression, respectively. Sonications ranged from 8 to 36 with targeted lesional temperatures of 51°C-56°C. Lesions in the anterodorsal aspect of the anterior limb of the internal capsule (ALIC) and increased functional connectivity to the left dorsolateral prefrontal cortex and dorsal anterior cingulate cortex significantly predicted reduction in symptoms among patients with OCD, with decreases in beta-band activity in the frontocentral and temporal regions associated with reductions in depression and anxiety. Treatment of the nucleus accumbens with low-intensity FUS (LIFU) in patients with opioid-use disorders resulted in significant reductions in cue-reactive cravings, lasting up to 90 days. No serious adverse events were reported, including cognitive decline. Side effects were generally mild and transient, consisting of headaches, pin-site swelling, and nausea. Fourteen active clinical trials were identified, primarily targeting depression with LIFU. CONCLUSIONS: Currently, FUS for psychiatric conditions is centered on OCD, with early pilot studies demonstrating promising safety and efficacy. Further research expanding on defining optimal patient selection, study design, intensity, and sonication parameters is warranted, particularly as FUS expands to other psychiatric illnesses and incorporates LIFU paradigms. Ethical considerations such as patient consent and equitable access also remain paramount.

The role of functional emotion circuits in distinct dimensions of psychopathology in youth.

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome.

The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.

Potential dopaminergic deficit in patients with geriatric psychiatric disorders as revealed by DAT-SPECT: a cross-sectional study.

BACKGROUND: It has been reported that patients with geriatric psychiatric disorders include many cases of the prodromal stages of neurodegenerative diseases. Abnormal 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl) nortropane dopamine transporter single-photon emission computed tomography (DAT-SPECT) reveals a nigrostriatal dopaminergic deficit and is considered useful to detect dementia with Lewy bodies and Parkinson's disease as well as progressive supranuclear palsy and corticobasal degeneration. We aimed to determine the proportion of cases that are abnormal on DAT-SPECT in patients with geriatric psychiatric disorders and to identify their clinical profile. METHODS: The design is a cross-sectional study. Clinical findings of 61 inpatients aged 60 years or older who underwent DAT-SPECT and had been diagnosed with psychiatric disorders, but not neurodegenerative disease or dementia were analysed. RESULTS: 36 of 61 (59%) had abnormal results on DAT-SPECT. 54 of 61 patients who had DAT-SPECT (89%) had undergone 123I-metaiodobenzylguanidine myocardial scintigraphy (123I-MIBG scintigraphy); 12 of the 54 patients (22.2%) had abnormal findings on 123I-MIBG scintigraphy. There were no cases that were normal on DAT-SPECT and abnormal on 123I-MIBG scintigraphy. DAT-SPECT abnormalities were more frequent in patients with late-onset (55 years and older) psychiatric disorders (69.0%) and depressive disorder (75.7%), especially late-onset depressive disorder (79.3%). CONCLUSION: Patients with geriatric psychiatric disorders include many cases showing abnormalities on DAT-SPECT. It is suggested that these cases are at high risk of developing neurodegenerative diseases characterised by a dopaminergic deficit. It is possible that patients with geriatric psychiatric disorders with abnormal findings on DAT-SPECT tend to show abnormalities on DAT-SPECT first rather than on 123I-MIBG scintigraphy.

Generative AI for precision neuroimaging biomarker development in psychiatry.

The explosion of generative AI offers promise for neuroimaging biomarker development in psychiatry, but effective adoption of AI methods requires clarity with respect to specific applications and challenges. These center on dataset sizes required to robustly train AI models along with feature selection that capture neural signals relevant to symptom and treatment targets. Here we discuss areas where generative AI could improve quantification of robust and reproducible brain-to-symptom associations to inform precision psychiatry applications, especially in the context of drug discovery. Finally, this communication discusses some challenges that need solutions for generative AI models to advance neuroimaging biomarkers in psychiatry.

Cerebral glucose metabolic correlates of cognitive and behavioural impairments in amyotrophic lateral sclerosis.

OBJECTIVE: Half of ALS patients are cognitively and/or behaviourally impaired. As cognition/behaviour and cerebral glucose metabolism can be correlated by means of 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET), we aimed to utilise FDG-PET, first, to replicate group-level differences in glucose metabolism between non-demented ALS patients separated into non-impaired (ALSni), cognitively impaired (ALSci), behaviourally impaired (ALSbi), and cognitively and behaviourally impaired (ALScbi) groups; second, to investigate glucose metabolism and performance in various cognitive domains; and third, to examine the impact of partial volume effects correction (PVEC) of the FDG-PET data on the results. METHODS: We analysed neuropsychological, clinical, and imaging data from 67 ALS patients (30 ALSni, 21 ALSci, 5 ALSbi, and 11 ALScbi). Cognition was assessed with the Edinburgh Cognitive and Behavioural ALS Screen, and two social cognition tests. FDG-PET and structural MRI scans were acquired for each patient. Voxel-based statistical analyses were undertaken on grey matter volume (GMV) and non-corrected vs. PVE-corrected FDG-PET scans. RESULTS: ALSci and ALScbi had lower cognitive scores than ALSni. In contrast to both ALSni and ALSci, ALScbi showed widespread hypometabolism in the superior- and middle-frontal gyri in addition to the right temporal pole. Correlations were observed between the GMV, the FDG-PET signal, and various cognitive scores. The FDG-PET results were largely unaffected by PVEC. INTERPRETATION: Our study identified widespread differences in hypometabolism in the ALScbi-ni but not in the ALSci-ni group comparison, raising the possibility that cerebral metabolism may be more closely related to the presence of behavioural changes than to mild cognitive deficits.

Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients.

Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases (Part 2)].

In this study, based on the neuroimaging literature Meta analysis retrieved from Neurosynth platform, the scalp stimulation targets for common psychiatric diseases are developed, which provided the stimulation target protocols of scalp acupuncture for attention deficit hyperactivity disorder, autism spectrum disorder, obsessive-compulsive disorder and schizophrenia. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture/scalp acupuncture and common neuromodulation methods for each disease so as to provide the references for clinical practice. Based on the study results above, the paper further summarizes the overlapped stimulation targets undergoing the intervention with scalp acupuncture for common psychiatric diseases, and the potential relationship between these stimulation targets and treatments with acupuncture and moxibustion.

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classification of NDs has been thoroughly explored. However, accurately classifying multiple classes of NDs remains a challenge due to the complexity of disease subclass. In our study, we develop a heterogeneous neural network (H-Net) that integrates sMRI and fMRI modes for classifying multi-class NDs. To account for the differences between the two modes, H-Net adopts a heterogeneous neural network strategy to extract information from each mode. Specifically, H-Net includes an multi-layer perceptron based (MLP-based) encoder, a graph attention network based (GAT-based) encoder, and a cross-modality transformer block. The MLP-based and GAT-based encoders extract semantic features from sMRI and features from fMRI, respectively, while the cross-modality transformer block models the attention of two types of features. In H-Net, the proposed MLP-mixer block and cross-modality alignment are powerful tools for improving the multi-classification performance of NDs. H-Net is validate on the public dataset (CNP), where H-Net achieves 90% classification accuracy in diagnosing multi-class NDs. Furthermore, we demonstrate the complementarity of the two MRI modalities in improving the identification of multi-class NDs. Both visual and statistical analyses show the differences between ND subclasses.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Psychiatric disorders have different prevalence between sexes, with women being twice as likely to develop depression and anxiety across the lifespan. Previous studies have investigated sex differences in brain structure that might contribute to this prevalence but have mostly focused on a single-structure level, potentially overlooking the interplay between brain regions. Sex differences in structures responsible for emotional regulation (limbic system), affected in many psychiatric disorders, have been previously reported. Here, we apply a machine learning model to obtain an estimate of brain sex for each participant based on the volumes of multiple brain regions. Particularly, we compared the estimates obtained with a model based solely on limbic structures with those obtained with a non-limbic model (entire brain except limbic structures) and a whole brain model. To investigate the genetic determinants of the models, we assessed the heritability of the estimates between identical twins and fraternal twins. The estimates of all our models were heritable, suggesting a genetic component contributing to brain sex. Finally, to investigate the association with mental health, we compared brain sex estimates in healthy subjects and in a depressed population. We found an association between depression and brain sex in females for the limbic model, but not for the non-limbic model. No effect was found in males. Overall, our results highlight the potential utility of machine learning models of brain sex based on relevant structures to better understand the link between sex differences in the brain and mental disorders.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders.

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Building diagnostic neuroimaging biomarkers for psychiatric disorders using reverse inference approaches: A viable route?

The advent of structural magnetic resonance imaging (sMRI) at the end of the 20th century opened the way toward a deeper understanding of the neurophysiology of psychiatric disorders, substantiating regional structural abnormalities underlying this group of clinical conditions. However, despite abundant and flourishing scientific research, sMRI methodologies are not currently integrated into daily diagnostic practice. One reason behind this failed translation may be the prevailing approach to logical reasoning in neuroimaging: The forward inference via frequentist-based statistics. This reasoning prevents clinicians from obtaining information about the selectivity of results, which are therefore of limited use regarding the definition of biomarkers and refinement of diagnostic processes. Recently, another type of inferential approach has started to emerge in the neuroimaging field: The reverse inference via Bayesian statistics. Here, we introduce the key concepts of this approach, with a particular emphasis on the clinical sMRI environment. We survey recent findings showing significant potential for clinical translation. Clinical opportunities and challenges for developing reverse inference-based neural markers for psychiatry are also discussed. We propose that a systematic sharing of imaging data across the human brain mapping community is an essential first step toward a paradigmatic clinical shift. We conclude that a defined synergy between forward-based and reverse-based sMRI research can illuminate current discussions on diagnostic brain markers, offering clarity on key issues and fostering new tailored diagnostic avenues.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases].

Scalp acupuncture is a unique acupuncture method, developed based on the cerebral cortex localization. Neuroimaging technology enables the combination of contemporary brain science findings with the studies of scalp stimulation sites. In this study, based on the neuroimaging literature retrieved from Neurosynth platform, the scalp stimulation targets of common psychiatric diseases are developed, which provides the stimulation target protocol of scalp acupuncture for anxiety, bipolar disorder, major depressive disorder and post-traumatic stress disorder. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture and scalp acupuncture for each disease so as to provide the references for clinical practice. These targets can be used not only for the stimulation of scalp acupuncture, but also for the different neuromodulation techniques to treat related diseases.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology.

BACKGROUND: Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. METHODS: We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. RESULTS: We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations. CONCLUSIONS: Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

Functional neuroimaging in patients with catatonia: A systematic review.

BACKGROUND: Catatonia is a challenging and heterogeneous neuropsychiatric syndrome of motor, affective and behavioral dysregulation which has been associated with multiple disorders such as structural brain lesions, systemic diseases, and psychiatric disorders. This systematic review summarized and compared functional neuroimaging abnormalities in catatonia associated with psychiatric and medical conditions. METHODS: Using PRISMA methods, we completed a systematic review of 6 databases from inception to February 7th, 2024 of patients with catatonia that had functional neuroimaging performed. RESULTS: A total of 309 studies were identified through the systematic search and 62 met the criteria for full-text review. A total of 15 studies reported patients with catatonia associated with a psychiatric disorder (n = 241) and one study reported catatonia associated with another medical condition, involving patients with N-methyl-d-aspartate receptor antibody encephalitis (n = 23). Findings varied across disorders, with hyperactivity observed in areas like the prefrontal cortex (PFC), the supplementary motor area (SMA) and the ventral pre-motor cortex in acute catatonia associated to a psychiatric disorder, hypoactivity in PFC, the parietal cortex, and the SMA in catatonia associated to a medical condition, and mixed metabolic activity in the study on catatonia linked to a medical condition. CONCLUSION: Findings support the theory of dysfunction in cortico-striatal-thalamic, cortico-cerebellar, anterior cingulate-medial orbitofrontal, and lateral orbitofrontal networks in catatonia. However, the majority of the literature focuses on schizophrenia spectrum disorders, leaving the pathophysiologic characteristics of catatonia in other disorders less understood. This review highlights the need for further research to elucidate the pathophysiology of catatonia across various disorders.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration.

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.