ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

ComBatLS: A Location- and Scale-Preserving Method for Multi-Site Image Harmonization

Recent study has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites." Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Measuring and interpreting individual differences in fetal, infant, and toddler neurodevelopment

As scientists interested in fetal, infant, and toddler (FIT) neurodevelopment, our research questions often focus on how individual children differ in their neurodevelopment and the predictive value of those individual differences for long-term neural and behavioral outcomes. Measuring and interpreting individual differences in neurodevelopment can present challenges: Is there a "standard" way for the human brain to develop? How do the semantic, practical, or theoretical constraints that we place on studying "development" influence how we measure and interpret individual differences? While it is important to consider these questions across the lifespan, they are particularly relevant for conducting and interpreting research on individual differences in fetal, infant, and toddler neurodevelopment due to the rapid, profound, and heterogeneous changes happening during this period, which may be predictive of long-term outcomes. This article, therefore, has three goals: 1) to provide an overview about how individual differences in neurodevelopment are studied in the field of developmental cognitive neuroscience, 2) to identify challenges and considerations when studying individual differences in neurodevelopment, and 3) to discuss potential implications and solutions moving forward.

Cognitive outcomes and performance of patients diagnosed and treated for N-Methyl-D-Aspartate receptor antibody-mediated (NMDAR) encephalitis compared with patients with schizophrenia and healthy controls

BACKGROUND

Cognitive dysfunction may be a sequelae of N-Methyl-D-Aspartate receptor encephalitis (NMDAR encephalitis) with working memory commonly affected. This study examined cognitive outcomes in patients treated for NMDAR encephalitis using a neurocognitive test battery and a working memory paradigm, compared with healthy controls & patients with schizophrenia.

METHODS

Adult patients previously treated for NMDAR encephalitis were assessed using the Cambridge Automated Neuropsychological Test Battery (CANTAB) for working memory & episodic memory. Patients completed the N-back task during functional MRI (fMRI) scanning. Results were compared to patients with schizophrenia and healthy controls from a prior study.

RESULTS

Twelve patients were recruited [11 women; mean (SD) age 37(12) years; Mean (SD) duration until immunotherapy treatment 7.09 (2.43) weeks]. Data were compared to 14 patients with schizophrenia [10 women; mean (SD) age 39 (12) years] and 14 healthy controls [7 women; mean (SD) age 30 (6) years]. Significant differences in letter number sequencing, spatial working memory, logical memory I, 1-back, and 2-back performance were observed (Cohen's d = 0.766 to 1.254, p< 0.05), driven by poorer performance by patients with schizophrenia. While patients with NMDAR encephalitis exhibited slightly lower performance compared with healthy controls, none of these differences were statistically significant. No significant differences in neural activation during 1-back or 2-back performance were observed.

CONCLUSIONS

Study findings suggest cognitive performance in patients treated for NMDAR encephalitis approaches normal over time. Prompt treatment with immunotherapy is associated with improved cognitive outcomes. Psychiatric services should be aware of the clinical features of autoimmune encephalitis.

The role of gut microbiota in modulating brain structure and psychiatric disorders: A Mendelian randomization study

The influence of the gut microbiome on the human brain, especially its associations with psychiatric disorders, has emerged as a focal area in contemporary neuroscience and psychiatry research. In this study, we employed a mediation Mendelian randomization approach to delve into the potential causal relationships between gut microbiota and psychiatric disorders, with a focus on the mediating role of brain structural changes. We harnessed genetic data from large - scale genome - wide association studies to analyze how 196 gut microbiota taxa affect ten psychiatric disorders via alterations in 3143 brain structures. Our key findings revealed significant bidirectional causal relationships. In the gut microbiota - brain structure relationship, certain gut microbiota taxa, such as Bacteroides and Marvinbryantia, were associated with changes in brain activity and white matter integrity respectively. Conversely, brain structures like the right hippocampus and left superior cerebellar peduncle influenced gut microbiota composition. Regarding gut microbiota and psychiatric disorders, we identified numerous associations. For example, the genus Prevotellaceae was significantly associated with an increased risk of Autism Spectrum Disorder, while Ruminococcaceae UCG005 showed a protective effect. In Panic Disorder, Alistipes was positively associated, and for Schizophrenia, both protective (Barnesiella) and risk - associated (Phascolarctobacterium) genera were found. Moreover, through mediation analysis, we found that brain structures mediated the effects of gut microbiota on five psychiatric disorders, including bipolar disorder and anorexia nervosa. In these cases, the influence of gut microbiota on the disorders was fully transmitted through changes in brain structure. Overall, our research clarifies the role of the microbiota - gut - brain axis in mental health. It offers a new perspective on how intestinal microbes impact brain physiology and psychiatric pathology. These findings not only deepen our understanding of the biological interactions between the gut and brain but also suggest that targeted gut microbiota modifications could be novel therapeutic strategies for mental health disorders.

AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders

Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.

Unique opportunities and challenges of longitudinal approaches in studying brain health and mental health

Longitudinal studies offer a unique opportunity to characterize individual human lifespan trajectories. While challenging, their added value can advance our understanding of neurodevelopment and mental health disorders, with the aim to optimize brain health and mental health.

Generalizability of developmental EEG: Demographic reporting, representation, and sample size

Electroencephalography (EEG) is one of the main neuroscientific measures used with infants and children to identify potential biomarkers of cognitive and social developmental processes. Given the implications of developmental EEG research within policy, clinical, and educational domains, it is important to ensure that reported results are generalizable and reproducible. In this review, to provide an initial assessment of previous and current practices regarding participant recruitment (sample size and representation) and demographic reporting, we carried out a systematic review of six notable journals for publishing pediatric EEG studies between 2011 and 2023. We identified 700 articles reporting on pediatric EEG. We found that most studies did not provide complete reporting of basic demographic information (e.g., race, ethnicity, socioeconomic status, geographical location). This trend persisted across years of publication, suggesting continued underreporting. However, the reporting of demographic information differed between journals, suggesting solutions for improving reporting practices. Our review also indicated that samples were of modest sample size (Median = 51) and consisted of mostly White participants (78 %) from North America and Western Europe (85 %). Our discussion emphasizes the need for larger, more diverse samples and greater transparency in developmental EEG studies, while providing recommendations to address barriers to representation and reproducibility.

Neuroimaging pattern interactions for suicide risk in depression captured by ensemble learning over transcriptome-defined parcellation

BACKGROUND

For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined parcellations (TDP) to explore homogeneously parcellated brain patterns and their interactions.

METHODS

96 depressed patients without suicide attempt (SA), 86 with SA and 102 healthy controls were recruited for resting state fMRI scanning. Six genetic dimensions were created by homogenous transcriptomic delineations from Allen Human Brain Atlas. Spatially-continuous TDPs were generated according to expression-levels of each brain region along diverse dimensions. Subsequently, TDPs were integrated with a three-layer ensemble learning scheme, where brain dysfunction of each TDP related to suicide was quantified with a resting-state functional abnormality (RSFA) score. Then, personalized index of brain dysfunction was produced according to the interactive pattern across TDPs.

RESULTS

Ensemble learning over TDPs displayed higher suicide predictive performance, relative to that over the regions level, and over null model (95 % CI of accuracy: 73.23 ± 1.07 %; 64.59 ± 3.00 %; 65.41 ± 3.97 %, respectively). Empowered by specific parieto-occipital TDP (PO-TDP) pattern quantified with RSFA score in suicide risk prediction, its alternations of SA effects were spatially associated with transcriptional profiles of GRIN2A and GABRG2. Moreover, glutamatergic and GABAergic synapse were overrepresented in enrichment analysis.

CONCLUSION

Glutamatergic and GABAergic dysfunction in the visual cortex was suggested via the PO-TDP specific interaction pattern. The inherent excitatory/inhibitory imbalance could contribute to aberrant emotional processing and neurocognitive impairment, ultimately leading to suicide.

White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals

Although specific risk factors for brain alterations in bipolar disorders (BD) are currently unknown, obesity impacts the brain and is highly prevalent in BD. Gray matter correlates of obesity in BD have been well documented, but we know much less about brain white matter abnormalities in people who have both obesity and BD. We obtained body mass index (BMI) and diffusion tensor imaging derived fractional anisotropy (FA) from 22 white matter tracts in 899 individuals with BD, and 1287 control individuals from 20 cohorts in the ENIGMA-BD working group. In a mega-analysis, we investigated the associations between BMI, diagnosis or medication and FA. Lower FA was associated with both BD and BMI in six white matter tracts, including the corpus callosum and thalamic radiation. Higher BMI or BD were uniquely associated with lower FA in three and six white matter tracts, respectively. People not receiving lithium treatment had a greater negative association between FA and BMI than people treated with lithium in the posterior thalamic radiation and sagittal stratum. In three tracts BMI accounted for 10.5 to 17% of the negative association between the number of medication classes other than lithium and FA. Both overweight/obesity and BD demonstrated lower FA in some of the same regions. People prescribed lithium had a weaker association between BMI and FA than people not on lithium. In contrast, greater weight contributed to the negative associations between medications and FA. Obesity may add to brain alterations in BD and may play a role in effects of medications on the brain.

Cortical differences across psychiatric disorders and associated common and rare genetic variants

Genetic studies have identified common and rare variants increasing the risk for neurodevelopmental and psychiatric disorders (NPDs). These risk variants have also been shown to influence the structure of the cerebral cortex. However, it is unknown whether cortical differences associated with genetic variants are linked to the risk they confer for NPDs. To answer this question, we analyzed cortical thickness (CT) and surface area (SA) for common and rare variants associated with NPDs, in ~33000 individuals from the general population and clinical cohorts, as well as ENIGMA summary statistics for 8 NPDs. Rare and common genetic variants increasing risk for NPDs were preferentially associated with total SA, while NPDs were preferentially associated with mean CT. Larger effects on mean CT, but not total SA, were observed in NPD medicated subgroups. At the regional level, genetic variants were preferentially associated with effects in sensorimotor areas, while NPDs showed higher effects in association areas. We show that schizophrenia- and bipolar-disorder-associated SNPs show positive and negative effect sizes on SA suggesting that their aggregated effects cancel out in additive polygenic models. Overall, CT and SA differences associated with NPDs do not relate to those observed across individual genetic variants and may be linked with critical non-genetic factors, such as medication and the lived experience of the disorder.

A genetically informed brain atlas for enhancing brain imaging genomics

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

Individual-level deviations from normative brain morphology in violence, psychosis, and psychopathy

Neuroimaging research has shown brain morphological abnormalities associated with violence and psychosis, but individual differences are substantial and results not consistent across studies. Normative modeling of brain MRI-features facilitates a systematic mapping of individual brain characteristics of complex phenotypes also in small samples but has not yet been applied to forensic psychiatry populations. We explored brain heterogeneity in persons with a history of severe violence with a comorbid schizophrenia spectrum disorder (SSD-V; n = 38), non-violent persons with schizophrenia spectrum disorders (SSD-NV; n = 138), persons with a history of severe violence without comorbid schizophrenia spectrum disorder (nonSSD-V; n = 20), and healthy non-violent participants (HC; n = 196) from lifetime normative trajectories of cortical thickness, surface area, and subcortical volumes. Normative models based on Freesurfer derived regions of interest from 58,836 individuals were used to investigate individual deviances, group differences, and associations to psychopathy traits. We found overall heterogeneous patterns of individual deviations from the norm, which were most prominent for regions within the collateral transverse sulcus, lingual gyrus, and cerebellum among SSD-V, a pattern that differed from SSD-NV (parieto-occipital and suborbital sulci), and nonSSD-V (paracentral and middle frontal regions). We found no significant associations to psychopathy traits. By applying normative modeling, we demonstrate heterogeneous patterns of brain morphometry deviations associated with violence and psychosis. While the results warrant replication, studies addressing individual brain deviations may contribute to improved understanding of the neurobiological underpinnings of comorbid violence and psychosis, which ultimately may have clinical impact on treatment and forensic psychiatric evaluations.

Gaze behaviors during free viewing revealed differences in visual salience processing across four major psychiatric disorders: a mega-analysis study of 1012 individuals

Aberrant salience processing has been proposed as a pathophysiological mechanism underlying psychiatric symptoms in patients with schizophrenia. The gaze trajectories of individuals with schizophrenia have been reported to be abnormal when viewing an image, suggesting anomalous visual salience as one possible pathophysiological mechanism associated with psychiatric diseases. This study was designed to determine whether visual salience is affected in individuals with schizophrenia, and whether this abnormality is unique to patients with schizophrenia. We examined the gaze behaviors of 1012 participants recruited from seven institutes (550 healthy individuals and 238, 41, 50 and 133 individuals with schizophrenia, bipolar disorder, major depressive disorder and autism spectrum disorder, respectively) when they looked at stationary images as they liked, i.e., free-viewing condition. We used an established computational model of salience maps derived from low-level visual features to measure the degree to which the gaze trajectories of individuals were guided by visual salience. The analysis revealed that the saliency at the gaze of individuals with schizophrenia were higher than healthy individuals, suggesting that patients' gazes were guided more by low-level image salience. Among the low-level image features, orientation salience was most affected. Furthermore, a general linear model analysis of the data for the four psychiatric disorders revealed a significant effect of disease. This abnormal salience processing depended on the disease and was strongest in patients with schizophrenia, followed by patients with bipolar disorder, major depressive disorder, and autism spectrum disorder, suggesting a link between abnormalities in salience processing and strength/frequency for psychosis of these disorders.

Site effects in multisite fetal brain MRI: morphological insights into early brain development

OBJECTIVE

To evaluate multisite effects on fetal brain MRI. Specifically, to identify crucial acquisition factors affecting fetal brain structural measurements and developmental patterns, while assessing the effectiveness of existing harmonization methods in mitigating site effects.

MATERIALS AND METHODS

Between May 2017 and March 2022, T2-weighted fast spin-echo sequences in-utero MRI were performed on healthy fetuses from retrospectively recruited pregnant volunteers on four different scanners at four sites. A generalized additive model (GAM) was used to quantitatively assess site effects, including field strength (FS), manufacturer (M), in-plane resolution (R), and slice thickness (ST), on subcortical volume and cortical morphological measurements, including cortical thickness, curvature, and sulcal depth. Growth models were selected to elucidate the developmental trajectories of these morphological measurements. Welch's test was performed to evaluate the influence of site effects on developmental trajectories. The comBat-GAM harmonization method was applied to mitigate site-related biases.

RESULTS

The final analytic sample consisted of 340 MRI scans from 218 fetuses (mean GA, 30.1 weeks ± 4.4 [range, 21.7-40 weeks]). GAM results showed that lower FS and lower spatial resolution led to overestimations in selected brain regions of subcortical volumes and cortical morphological measurements. Only the peak cortical thickness in developmental trajectories was significantly influenced by the effects of FS and R. Notably, ComBat-GAM harmonization effectively removed site effects while preserving developmental patterns.

CONCLUSION

Our findings pinpointed the key acquisition factors in in-utero fetal brain MRI and underscored the necessity of data harmonization when pooling multisite data for fetal brain morphology investigations.

KEY POINTS

Question How do specific site MRI acquisition factors affect fetal brain imaging? Finding Lower FS and spatial resolution overestimated subcortical volumes and cortical measurements. Cortical thickness in developmental trajectories was influenced by FS and in-plane resolution. Clinical relevance This study provides important guidelines for the fetal MRI community when scanning fetal brains and underscores the necessity of data harmonization of cross-center fetal studies.

Beyond case-control study in neuroimaging for psychiatric disorders: Harmonizing and utilizing the brain images from multiple sites

Recent magnetic resonance imaging (MRI) research has advanced our understanding of brain pathophysiology in psychiatric disorders. This progress necessitates re-evaluation of the diagnostic system for psychiatric disorders based on MRI-based biomarkers, with implications for precise clinical diagnosis and optimal therapeutics. To achieve this goal, large-scale multi-site studies are essential to develop a standardized MRI database, with the analysis of several thousands of images and the incorporation of new data. A critical challenge in these studies is to minimize sampling and measurement biases in MRI studies to accurately capture the diversity of disease-derived biomarkers. Various techniques have been employed to consolidate datasets from multiple sites in case-control studies. Traveling subject harmonization stands out as a powerful tool that can differentiate measurement bias from sample variety and sampling bias. A non-linear statistical model for a normative trajectory across the lifespan also strengthens the database to mitigate sampling bias from known factors such as age and sex. These approaches can enhance the alterations between psychiatric disorders and integrate new data and follow-up scans into existing life-course trajectory, enhancing the reliability of machine learning classification and subtyping. Although this approach has been developed using T1-weighted structural image features, future research may extend this framework to other modalities and measures. The required sample size and methodological establishment are needed for future investigations, leading to novel insights into the brain pathophysiology of psychiatric disorders and the development of optimal therapeutics for bedside clinical applications. Sharing big data and their findings also need to be considered.

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.

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the field of meditation and contemplative neuroscience.

Brain Network Resilience and Cognitive Function in Patients with First Episode Schizophrenia Spectrum Disorders

BACKGROUND

First-episode schizophrenia spectrum disorders (FESSDs) are associated with significant cognitive impairment that affects daily functioning. While these deficits are well-documented, the neural mechanisms underlying the cognitive impairment in FESSDs remain limited. As the structure robustness and resilience of the brain network in healthy population are reported to be associated with cognitive function, we examined these associations in FESSDs.

METHODS

This study investigated the relationships of the structure robustness and resilience of the core brain network with cognitive function and psychopathology in patients with FESSDs (n = 340) using data from two independent cohorts in South Korea and China. Age- and sex-matched HC (n = 420) were also recruited. We applied advanced k-shell decomposition techniques to functional brain networks and estimated various measures of structure robustness and resilience.

RESULTS

Patients with FESSDs had brain networks with a less robust structure and resilience than those of HC. Resilience measures were positively correlated with executive function in patients. Core brain network structure and k-core resilience were negatively correlated with the Positive and Negative Syndrome Scale scores. Subgroup analysis showed that structure robustness and resilience at each site matched the main analysis, but correlations with cognitive function and psychopathology were observed only in the South Korea cohort.

CONCLUSIONS

Better preservation of brain network structure and resilience is associated with higher cognitive function and milder clinical symptoms in patients with FESSDs. This highlights potential targets for therapeutic interventions aimed at enhancing cognitive function and improving the symptoms of FESSDs.

Novel Multimodal Precision Medicine Approaches and the Relevance of Developmental Trajectories in Bipolar Disorder

There is a pressing need to establish objective measures to improve diagnosis, prediction, prevention, and treatment of bipolar disorder (BD). Multimodal artificial intelligence (AI) tools could provide these means by incorporating various layers of data orthogonally related to BD, including genomics and other omics, environmental exposures, imaging measures, electronic health records, cognition, sensing devices, and clinical variables. These rapidly evolving AI models hold promise to capture the multidimensional complexity of BD and delineate clinically relevant developmental trajectories that could guide clinical care and therapeutic strategies. In this review, we describe the potential of mapping developmental trajectories that underlie BD, outline how novel multimodal models could improve the prediction of BD and related outcomes, and discuss specific clinical use cases and key ethical and practical challenges regarding the development and potential implementation of these multimodal AI solutions to advance precision medicine approaches in BD.

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis

Introduction

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, brain age research in psychiatry has primarily examined mood and psychotic disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group.

Methods

3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1,134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effect models examined group differences in brain-PAD and moderation by age.

Results

No significant group difference in brain-PAD manifested (β diagnosis (SE)=0.37 years (0.43), p=0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (β diagnosis-by-age=-0.08 (0.03), pFDR=0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post-hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; β diagnosis=1.20 (0.60), p<0.05, mixed-effects d (95% confidence interval)=0.14 (0.00-0.28)), but not older participants (36-75 years; β diagnosis=0.07 (0.65), p=0.91).

Conclusions

Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post-hoc analyses showed a subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

Importance

Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients.

Objective

To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics.

Design setting and participants

A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices).

Main outcome measures

Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC.

Results

Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms.

Conclusions and relevance

Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture.

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.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Proposal for a Novel Classification of Patients With Enlarged Ventricles and Cognitive Impairment Based on Data-Driven Analysis of Neuroimaging Results in Patients With Psychiatric Disorders

One of the challenges in diagnosing psychiatric disorders is that the results of biological and neuroscience research are not reflected in the diagnostic criteria. Thus, data-driven analyses incorporating biological and cross-disease perspectives, regardless of the diagnostic category, have recently been proposed. A data-driven clustering study based on subcortical volumes in 5604 subjects classified into four brain biotypes associated with cognitive/social functioning. Among the four brain biotypes identified in controls and patients with schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and other psychiatric disorders, we further analyzed the brain biotype 1 subjects, those with an extremely small limbic region, for clinical utility. We found that the representative feature of brain biotype 1 is enlarged lateral ventricles. An enlarged ventricle, defined by an average z-score of left and right lateral ventricle volumes > 3, had a sensitivity of 99.1% and a specificity of 98.1% for discriminating brain biotype 1. However, the presence of an enlarged ventricle was not sufficient to classify patient subgroups, as 1% of the controls also had enlarged ventricles. Reclassification of patients with enlarged ventricles according to cognitive impairment resulted in a stratified subgroup that included patients with a high proportion of schizophrenia diagnoses, electroencephalography abnormalities, and rare pathological genetic copy number variations. Data-driven clustering analysis of neuroimaging data revealed subgroups with enlarged ventricles and cognitive impairment. This subgroup could be a new diagnostic candidate for psychiatric disorders. This concept and strategy may be useful for identifying biologically defined psychiatric disorders in the future.

The prevalence and risk factors of sleep disturbances in community-dwelling older adults: a systematic review and meta-analysis

PURPOSE

Sleep disturbance is one of the most prevalent health issues among community-dwelling older adults. This systematic review aims to assess the prevalence of sleep disturbances among these adults living in the community and identify associated risk factors.

METHODS

A comprehensive literature search was performed using PubMed, Web of Science, Embase, and the Cochrane Library databases. We screened studies focusing on the prevalence of sleep disturbances in community-dwelling older adults (≥ 60 years). A random-effects model was used to calculate the pooled prevalence of sleep disturbances. Sensitivity and subgroup analyses were conducted to investigate sources of heterogeneity, and funnel plots were used to assess publication bias.

RESULTS

Our systematic review included 41 articles, encompassing a total sample of 71,607 participants from 13 countries. The pooled prevalence of sleep disturbances, measured by PSQI, was found to be 45% (95% CI: 40-50%). Notably, the prevalence of sleep disturbances was significantly higher among individuals aged 70 years and older (48%) compared to those aged 60 years and older (41%). Common risk factors for sleep disturbances included depression, advanced age, females, chronic diseases (hypertension, coronary heart disease, chronic obstructive pulmonary disease) and poor external support (poor social support and poor family relationships).

CONCLUSION

The findings highlight the necessity for comprehensive assessments and management strategies targeting this population with depression, advanced age, females, hypertension, coronary heart disease, chronic obstructive pulmonary disease, and poor external support while also underscoring the significance of healthcare planners and policymakers in enhancing sleep quality for older adults.

NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics

Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortium's work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.

Unlocking NAC's potential ATF4 and m6A dynamics in rescuing cognitive impairments in PTSD

In this study, we investigated the therapeutic potential of N-acetylcysteine (NAC) in a mouse model of post-traumatic stress disorder (PTSD) induced by a single prolonged stress (SPS) protocol. Our findings demonstrate that NAC treatment significantly improved cognitive function and mitigated hippocampal neuronal apoptosis in PTSD model mice. These positive effects were accompanied by a reduction in m6A methylation levels and activating transcription factor 4 (ATF4) expression. Silencing ATF4 further attenuated hippocampal neuronal apoptosis and cognitive dysfunction, while ATF4 overexpression partially reversed the beneficial effects of NAC. It suggests that NAC's efficacy in PTSD may be mediated by its regulation of ATF4 expression and m6A methylation levels. Overall, our study provides valuable insights into the potential mechanism of action for NAC in PTSD treatment, offering promising avenues for future therapeutic strategies.

A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder

Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for Obsessive Compulsive Disorder (OCD). Our systematic review of structural neuroimaging markers (i.e., morphometry, structural connectivity) of psychotherapy and medication treatment response for OCD identified 26 eligible publications from 20 studies (average study total n=54 ± 41.6 [range: 11-175]; OCD group n=29 ± 19) in child, adolescent, and adult samples evaluating baseline brain structure correlates of treatment response as well as treatment-related changes in brain structure. Findings were inconsistent across studies; significant associations within the anterior cingulate cortex (3/5 regional, 2/8 whole brain studies) and orbitofrontal cortex (5/10 regional, 2/7 whole brain studies) were most common, but laterality and directionality were not always consistent. Structural neuroimaging markers of treatment response do not currently hold clinical utility. Given increasing evidence that associations between complex behavior and brain structure are characterized by small, but potentially meaningful, effects, much larger samples are likely needed. Multivariate approaches (e.g., machine learning) may also improve the clinical predictive utility of neuroimaging data.

Post-traumatic stress disorder: evolving conceptualization and evidence, and future research directions

The understanding of responses to traumatic events has been greatly influenced by the introduction of the diagnosis of post-traumatic stress disorder (PTSD). In this paper we review the initial versions of the diagnostic criteria for this condition and the associated epidemiological findings, including sociocultural differences. We consider evidence for post-traumatic reactions occurring in multiple contexts not previously defined as traumatic, and the implications that these observations have for the diagnosis. More recent developments such as the DSM-5 dissociative subtype and the ICD-11 diagnosis of complex PTSD are reviewed, adding to evidence that there are several distinct PTSD phenotypes. We describe the psychological foundations of PTSD, involving disturbances to memory as well as to identity. A broader focus on identity may be able to accommodate group and communal influences on the experience of trauma and PTSD, as well as the impact of resource loss. We then summarize current evidence concerning the biological foundations of PTSD, with a particular focus on genetic and neuroimaging studies. Whereas progress in prevention has been disappointing, there is now an extensive evidence supporting the efficacy of a variety of psychological treatments for established PTSD, including trauma-focused interventions - such as trauma-focused cognitive behavior therapy (TF-CBT) and eye movement desensitization and reprocessing (EMDR) - and non-trauma-focused therapies, which also include some emerging identity-based approaches such as present-centered and compassion-focused therapies. Additionally, there are promising interventions that are neither psychological nor pharmacological, or that combine a pharmacological and a psychological approach, such as 3,4-methylenedioxymethamphetamine (MDMA)-assisted psychotherapy. We review advances in the priority areas of adapting interventions in resource-limited settings and across cultural contexts, and of community-based approaches. We conclude by identifying future directions for work on trauma and mental health.

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.

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models are available at https://github.com/LisaHemforth/AutomaticIHIRating.

The Neurologist's Role in Promoting Brain Health: Emerging Issues in Neurology

Neurologic conditions are a leading cause of morbidity and mortality within the United States and worldwide. Brain health is a global concern, and the American Academy of Neurology's Brain Health Initiative promises to drive progress in this field over the next decades. Neurologists with detailed training and insight into brain function are uniquely positioned to apply emerging preventive health data to promote healthy brain development and maintain optimal brain function throughout the lifespan. The neurologist's role in promoting brain health is also vital in patients with active neurologic disease, in whom preventive measures may reduce recurrence or slow progression of disease and may enhance quality of life and overall function. In this Emerging Issues in Neurology article, we present the factors that may protect brain function and frame a practical approach to screening assessments and preventive interventions that neurology clinicians may consider to improve the brain health of patients at all life stages.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

Asymmetries in the human brain

The human brain is an intricate network of cortical regions interconnected by white matter pathways, dynamically supporting cognitive functions. While cortical asymmetries have been consistently reported, the asymmetry of white matter connections remains less explored. This chapter provides a brief overview of asymmetries observed at the cortical, subcortical, cytoarchitectural, and receptor levels before exploring the detailed connectional anatomy of the human brain. It thoroughly examines the lateralization and interindividual variability of 56 distinct white matter tracts, offering a comprehensive review of their structural characteristics and interindividual variability. Additionally, we provide an extensive update on the asymmetry of a wide range of white matter tracts using high-resolution data from the Human Connectome Project (7T HCP www.humanconnectome.org). Future research and advanced quantitative analyses are crucial to understanding fully how asymmetry contributes to interindividual variability. This comprehensive exploration enhances our understanding of white matter organization and its potential implications for brain function.

Application of a localized morphometrics approach to imaging-derived brain phenotypes for genotype-phenotype associations in pediatric mental health and neurodevelopmental disorders

Introduction

Quantitative global or regional brain imaging measurements, known as imaging-specific or -derived phenotypes (IDPs), are commonly used in genotype-phenotype association studies to explore the genomic architecture of the brain and how it may be affected by neurological diseases (e.g., Alzheimer's disease), mental health (e.g., depression), and neurodevelopmental disorders (e.g., attention-deficit hyperactivity disorder [ADHD]). For this purpose, medical images have been used as IDPs using a voxel-wise or global approach via principal component analysis. However, these methods have limitations related to multiple testing or the inability to isolate high variation regions, respectively.

Methods

To address these limitations, this study investigates a localized, principal component analysis-like approach for dimensionality reduction of cross-sectional T1-weighted MRI datasets utilizing diffeomorphic morphometry. This approach can reduce the dimensionality of images while preserving spatial information and enables the inclusion of spatial locality in the analysis. In doing so, this method can be used to explore morphometric brain changes across specific components and spatial scales of interest and to identify associations with genome regions in a multivariate genome-wide association study. For a first clinical feasibility study, this method was applied to data from the Adolescent Brain Cognitive Development (ABCD) study, including adolescents with ADHD (n = 1,359), obsessive-compulsive disorder (n = 1,752), and depression (n = 1,766).

Results

Meaningful associations of specific morphometric features with genome regions were identified with the data and corresponded to previous found brain regions in the respective mental health and neurodevelopmental disorder cohorts.

Discussion

In summary, the localized, principal component analysis-like approach can reduce the dimensionality of medical images while still being able to identify meaningful local brain region alterations that are associated with genomic markers across multiple scales. The proposed method can be applied to various image types and can be easily integrated in many genotype-phenotype association study setups.

Normative modeling of brain MRI data identifies small subcortical volumes and associations with cognitive function in youth with fetal alcohol spectrum disorder (FASD)

AIM

To quantify regional subcortical brain volume anomalies in youth with fetal alcohol spectrum disorder (FASD), assess the relative sensitivity and specificity of abnormal volumes in FASD vs. a comparison group, and examine associations with cognitive function.

METHOD

Participants: 47 children with FASD and 39 typically-developing comparison participants, ages 8-17 years, who completed physical evaluations, cognitive and behavioral testing, and an MRI brain scan. A large normative MRI dataset that controlled for sex, age, and intracranial volume was used to quantify the developmental status of 7 bilateral subcortical regional volumes. Z-scores were calculated based on volumetric differences from the normative sample. T-tests compared subcortical volumes across groups. Percentages of atypical volumes are reported as are sensitivity and specificity in discriminating groups. Lastly, Pearson correlations examined the relationships between subcortical volumes and neurocognitive performance.

RESULTS

Participants with FASD demonstrated lower mean volumes across a majority of subcortical regions relative to the comparison group with prominent group differences in the bilateral hippocampi and bilateral caudate. More individuals with FASD (89%) had one or more abnormally small volume compared to 72% of the comparison group. The bilateral hippocampi, bilateral putamen, and right pallidum were most sensitive in discriminating those with FASD from the comparison group. Exploratory analyses revealed associations between subcortical volumes and cognitive functioning that differed across groups.

CONCLUSION

In this sample, youth with FASD had a greater number of atypically small subcortical volumes than individuals without FASD. Findings suggest MRI may have utility in identifying individuals with structural brain anomalies resulting from PAE.

Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

BACKGROUND

It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.

METHODS

Our analysis included 8635 participants (52.5% females) aged 40-70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes.

RESULTS

Individuals in the top 20% of metabolomic state had 2.4-35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresponding number for GRS ranged from 1.5 to 32.8%. The proportion of variance of brain volumes (R [2]) explained by the corresponding metabolomic state ranged from 2.2 to 19.4%, and the corresponding number for GRS ranged from 0.8 to 8.7%. Metabolomic state provided no or minimal additional prediction values of brain volumes to age and sex while GRS provided moderate additional prediction values (ranging from 0.8 to 8.8%). No significant interplay between metabolomic state and GRS was observed, but the association between metabolomic state and some regional brain volumes was stronger in men or younger individuals. Individual metabolomic profiles including lipids and fatty acids were strong predictors of brain volumes.

CONCLUSIONS

In conclusion, metabolomic state is strongly associated with multiple brain volumes but provides minimal additional prediction value of brain volumes to age + sex. Although GRS is a weaker contributor to brain volumes than metabolomic state, it provides moderate additional prediction value of brain volumes to age + sex. Our findings suggest metabolomic state and GRS are important predictors for multiple brain phenotypes.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Individualized gray matter morphological abnormalities uncover two robust transdiagnostic biotypes

Psychiatric disorders exhibit a shared neuropathology, yet the diverse presentations among patients necessitate the identification of transdiagnostic subtypes to enhance diagnostic and treatment strategies. This study aims to unveil potential transdiagnostic subtypes based on personalized gray matter morphological abnormalities. A total of 496 patients with psychiatric disorders and 255 healthy controls (HCs) from three distinct datasets (one for discovery and two for validation) were enrolled. Individualized gray matter morphological abnormalities were determined using normative modeling to identify transdiagnostic subtypes. In the discovery dataset, two transdiagnostic subtypes with contrasting patterns of structural abnormalities compared to HCs were identified. Reproducibility and generalizability analyses demonstrated that these subtypes could be generalized to new patients and even to new disorders in the validation datasets. These subtypes were characterized by distinct disease epicenters. The gray matter abnormal pattern in subtype 1 was mainly linked to excitatory receptors, whereas subtype 2 showed a predominant association with inhibitory receptors. Furthermore, we observed that the gray matter abnormal pattern in subtype 2 was correlated with transcriptional profiles of inflammation-related genes, while subtype 1 did not show this association. Our findings reveal two robust transdiagnostic biotypes, offering novel insights into psychiatric nosology.

Quality over quantity: powering neuroimaging samples in psychiatry

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Principles of intensive human neuroimaging

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Synthetic data in generalizable, learning-based neuroimaging

Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Can personality traits be predicted from resting-state EEG oscillations? A replication study

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 min of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.