Common genetic variants influence human subcortical brain structures

White matter dysfunction in Alzheimer's disease is associated with disease-related transcriptomic signatures

While anatomical white matter (WM) alterations in Alzheimer's disease (AD) are well-established, functional WM dysregulation remains rarely investigated. The current study examines WM functional connectivity and network properties alterations in AD and mild cognitive impairment (MCI) and further describes their spatially correlated genes. AD and MCI shared decreased functional connectivity, clustering coefficient, and local efficiency within WM regions involved in impaired sensory-motor, visual-spatial, language, or memory functions. AD-specific dysfunction (i.e., AD vs. MCI and cognitively unimpaired participants) was predominantly located in WM, including anterior and posterior limb of internal capsule, corona radiata, and left tapetum. This WM dysfunction spatially correlates with specific genes, which are enriched in multiple biological processes related to synaptic function and development, and are mostly active in neurons and astrocytes. These findings may contribute to understanding molecular, cellular, and functional signatures associated with WM damage in AD.

Alterations in gray matter volume and associated transcriptomics after electroconvulsive therapy in major depressive disorder

BACKGROUND

The antidepressant mechanism of electroconvulsive therapy (ECT) remains not clearly understood. This study aimed to detect the changes in gray matter volume (GMV) in patients with major depressive disorder (MDD) caused by ECT and exploratorily analyzed the potential functional mechanisms.

METHODS

A total of 24 patients with MDD who underwent eight ECT sessions were included in the study. Clinical symptom assessments and MRI scans were conducted and compared. Using whole-brain micro-array measurements provided by the Allen Human Brain Atlas (AHBA), regional gene expression profiles were calculated. The differential gene PLS1 was obtained through Partial Least Squares (PLS) regression analysis, and PLS1 was divided into positive contribution (PLS1+) and negative contribution (PLS1-) genes. Through gene function enrichment analysis, the functional pathways and cell types of PLS1 enrichment were identified.

RESULTS

Gray matter volume (GMV) in the somatosensory and motor cortices, occipital cortex, prefrontal cortex, and insula showed an increasing trend after ECT, while GMV in the temporal cortex, posterior cingulate cortex, and orbitofrontal cortex decreased. PLS1 genes were enriched in synapse- and cell-related biological processes and cellular components (such as 'pre- and post-synapse', 'synapse organization' etc.). A large number of genes in the PLS1+ list were involved in neurons (inhibitory and excitatory), whereas PLS1- genes were significantly involved in Astrocytes (Astro) and Microglia (Micro).

CONCLUSIONS

This study established a link between treatment-induced GMV changes and specific functional pathways and cell types, which suggests that ECT may exert its effects through synapse-associated functional and affect neurons and glial cells.

Associations between morphometric similarity network and brain gene expression in type 2 diabetes mellitus

Abnormal functional and structural connectivity in brain networks is commonly observed in patients with type 2 diabetes mellitus (T2DM) and is often associated with cognitive impairment. In this study, we employed the Morphometric Similarity Network (MSN) method, which is based on seven morphometric features derived from structural and diffusion magnetic resonance imaging, to investigate structural differences in the brains of T2DM patients by quantifying structural similarities between brain regions. Globally, morphometric similarity (MS) was significantly reduced in T2DM patients. Regionally, MS was decreased in the left sensorimotor network and the right salience/ventral attention network, while it was increased in the bilateral visual network. Notably, the increased MS in the bilateral visual network was negatively correlated with memory function in T2DM patients. Furthermore, using the Allen Human Brain Atlas (AHBA; http://human.brain-map.org), which provides transcriptome data from postmortem adult brains, we linked MSN changes to regional gene expression patterns. Transcription-neuroimaging association analyses identified 298 genes whose expression was significantly spatially correlated with T2DM-related MSN abnormalities. Many of these genes are involved in biological processes such as central nervous system development and neurotransmitter transmission, offering valuable molecular and cellular insights into MS abnormalities and cognitive decline in T2DM. These findings shed light on the neural and genetic mechanisms underlying T2DM-related brain changes and cognitive impairment, providing new perspectives for future research and potential therapeutic approaches.

Cross-ancestry and sex-stratified genome-wide association analyses of amygdala and subnucleus volumes

The amygdala is a small but critical multi-nucleus structure for emotion, cognition and neuropsychiatric disorders. Although genetic associations with amygdala volumetric traits have been investigated in sex-combined European populations, cross-ancestry and sex-stratified analyses are lacking. Here we conducted cross-ancestry and sex-stratified genome-wide association analyses for 21 amygdala volumetric traits in 6,923 Chinese and 48,634 European individuals. We identified 191 variant-trait associations (P < 2.38 × 10-9), including 47 new associations (12 new loci) in sex-combined univariate analyses and seven additional new loci in sex-combined and sex-stratified multivariate analyses. We identified 12 ancestry-specific and two sex-specific associations. The identified genetic variants include 16 fine-mapped causal variants and regulate amygdala and fetal brain gene expression. The variants were enriched for brain development and colocalized with mood, cognition and neuropsychiatric disorders. These results indicate that cross-ancestry and sex-stratified genetic association analyses may provide insight into the genetic architectures of amygdala and subnucleus volumes.

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.

Vision Transformer Autoencoders for Unsupervised Representation Learning: Capturing Local and Non-Local Features in Brain Imaging to Reveal Genetic Associations

The discovery of genetic loci associated with brain architecture can provide deeper insights into neuroscience and improved personalized medicine outcomes. Previously, we designed the Unsupervised Deep learning-derived Imaging Phenotypes (UDIPs) approach to extract endophenotypes from brain imaging using a convolutional (CNN) autoencoder, and conducted brain imaging GWAS on UK Biobank (UKBB). In this work, we leverage a vision transformer (ViT) model due to a different inductive bias and its ability to potentially capture unique patterns through its pairwise attention mechanism. Our approach based on 128 endophenotypes derived from average pooling discovered 10 loci previously unreported by CNN-based UDIP model, 3 of which were not found in the GWAS catalog to have had any associations with brain structure. Our interpretation results demonstrate the ViT's capability in capturing non-local patterns such as left-right hemisphere symmetry within brain MRI data, by leveraging its attention mechanism and positional embeddings. Our results highlight the advantages of transformer-based architectures in feature extraction and representation for genetic discovery.

Genetics impact risk of Alzheimer's disease through mechanisms modulating structural brain morphology in late life

BACKGROUND

Alzheimer's disease (AD)-related neuropathological changes can occur decades before clinical symptoms. We aimed to investigate whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or increasing brain atrophy, respectively.

METHODS

We used bidirectional two-sample Mendelian randomisation to estimate the effects between genetic liability to AD and global and regional cortical thickness, estimated total intracranial volume, volume of subcortical structures and total white matter in 37 680 participants aged 8-81 years across 5 independent cohorts (Adolescent Brain Cognitive Development, Generation R, IMAGEN, Avon Longitudinal Study of Parents and Children and UK Biobank). We also examined the effects of global and regional cortical thickness and subcortical volumes from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium on AD risk in up to 37 741 participants.

RESULTS

Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. Some of the identified structures are not typically implicated in AD, such as those in the striatum (eg, thalamus), with consistent effects from childhood to late adulthood. There was little evidence to suggest brain morphology alters AD risk.

CONCLUSIONS

Genetic liability to AD is likely to affect risk of AD primarily through mechanisms affecting indicators of brain morphology in later life, rather than structural brain reserve. Future studies with repeated measures are required for a better understanding and certainty of the mechanisms at play.

Extensive perivascular spaces burden causally affects neurodegenerative diseases and brain structure: A two-sample bidirectional Mendelian randomization study

BACKGROUND

Extensive perivascular spaces (PVS) burden has been reported to be associated with neurodegenerative diseases and brain structure; however, the causal effects has not been determined yet. Therefore, this study aimed to investigate the causal effect of extensive PVS burden on neurodegenerative diseases and brain structure through Mendelian randomization (MR) analysis.

METHODS

Two-sample bidirectional MR was conducted based on publicly available genome-wide association studies (GWAS) summary statistics. Causal estimates of extensive PVS burden on neurodegenerative diseases and brain structure were primarily assessed using the inverse-variance weighted (IVW) method, supplemented by additional methods, including MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses were performed to assess heterogeneity and pleiotropy. In addition, we explored whether brain structure act as a mediating factor in the pathway from extensive PVS burden to neurodegenerative diseases.

RESULTS

Our MR study found that extensive PVS burden in white matter (WM-PVS) burden was associated with lower Alzheimer's disease (AD) risk (IVW OR (95 % CI) = 0.963(0.929 to 0.999), P = 0.0428), with no heterogeneity and pleiotropy detected. In addition, following FDR correction, we found bidirectional causal relationships between extensive PVS burden and brain structure. Moreover, our results of the mediated analysis showed that the surface area of parahippocampal, as a mediating variable, plays an important role in the causal relationship between WM-PVS and AD. The mediation effect is 18 %.

CONCLUSIONS

Our study provides evidence for the causal associations of different extensive PVS burden phenotypes with neurodegenerative diseases and brain structures, improving our understanding of the complex relationships between different brain injuries.

Over-integration of visual network in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21-23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS

Large-scale GWAS studies have uncovered hundreds of genomic loci linked to facial and brain shape variation, but only tens associated with cranial vault shape, a largely overlooked aspect of the craniofacial complex. Surrounding the neocortex, the cranial vault plays a central role during craniofacial development and understanding its genetics are pivotal for understanding craniofacial conditions. Experimental biology and prior genetic studies have generated a wealth of knowledge that presents opportunities to aid further genetic discovery efforts. Here, we use the conditional FDR method to leverage GWAS data of facial shape, brain shape, and bone mineral density to enhance SNP discovery for cranial vault shape. This approach identified 120 independent genomic loci at 1% FDR, nearly tripling the number discovered through unconditioned analysis and implicating crucial craniofacial transcription factors and signaling pathways. These results significantly advance our genetic understanding of cranial vault shape and craniofacial development more broadly.

Modifiable lifestyle factors influencing neurological and psychiatric disorders mediated by structural brain reserve: An observational and Mendelian randomization study

BACKGROUND

Modifiable lifestyle factors are implicated as risk factors for neurological and psychiatric disorders, but whether these associations are causal remains uncertain. We aimed to evaluate associations and ascertain causal relationships between modifiable lifestyle factors, neurological and psychiatric disorder risk, and brain structural magnetic resonance imaging (MRI) markers.

METHODS

We analyzed data from over 50,000 UK Biobank participants with self-reported lifestyle factors, including alcohol consumption, smoking, physical activity, diet, sleep, electronic device use, and sexual factors. Primary outcomes were stroke, all-cause dementia, Parkinson's disease (PD), Major depression disorder (MDD), Anxiety Disorders (ANX), and Bipolar Disorder (BIP), alongside MRI markers. Summary statistics were obtained from genome-wide association studies and Mendelian randomization (MR) analyses investigated bidirectional associations between lifestyle factors, neurological/psychiatric disorders, and MRI markers, with mediation assessed using multivariable Mendelian randomization (MVMR).

RESULTS

Cross-sectional analyses identified lifestyle factors were associated with neurological and psychiatric disorders and brain morphology. MR confirmed causal relationships, including lifetime smoking index on Stroke, PD, MDD, ANX and BIP; play computer games on BIP; leisure screen time on Stroke and MDD; automobile speeding propensity on MDD; sexual factors on MDD and BIP; sleep characteristics on BIP and MDD. Brain structure mediated several lifestyle-disorder associations, such as daytime dozing and dementia, lifetime smoking and PD and age first had sexual intercourse and PD.

CONCLUSION

Our results provide support for a causal effect of multiple lifestyle measures on the risk of neurological and psychiatric disorders, with brain structural morphology serving as a potential biological mediator in their associations.

Multi Layered Omics Approaches Reveal Glia Specific Alterations in Alzheimer's Disease: A Systematic Review and Future Prospects

Alzheimer's disease (AD) is the most common neurodegenerative dementia with multi-layered complexity in its molecular etiology. Multiple omics-based approaches, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics are enabling researchers to dissect this molecular complexity, and to uncover a plethora of alterations yielding insights into the pathophysiology of this disease. These approaches reveal multi-omics alterations essentially in all cell types of the brain, including glia. In this systematic review, we screen the literature for human studies implementing any omics approach within the last 10 years, to discover AD-associated molecular perturbations in brain glial cells. The findings from over 200 AD-related studies are reviewed under four different glial cell categories: microglia, oligodendrocytes, astrocytes and brain vascular cells. Under each category, we summarize the shared and unique molecular alterations identified in glial cells through complementary omics approaches. We discuss the implications of these findings for the development, progression and ultimately treatment of this complex disease as well as directions for future omics studies in glia cells.

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.

Associations of Child Amygdala Development With Borderline Personality Symptoms During Adolescence

BACKGROUND

The current understanding of the neural correlates of borderline personality disorder (BPD) is limited, but some evidence suggests that alterations in limbic structures play a role in adult BPD. The developmental course of structural neural differences in BPD is unknown. Whether there is specificity for structural alterations in BPD compared to other psychiatric presentations, such as major depressive disorder (MDD), remains unexplored. In the current study, we examined childhood trajectories of 2 limbic regions that have been implicated in BPD, hippocampal and amygdala volume, as they relate to adolescent BPD symptoms compared to MDD symptoms.

METHODS

Participants (n = 175; 85 [48.6%] female) were from a 17-year longitudinal study of preschool depression. Participants completed up to 5 magnetic resonance imaging scans from late childhood through adolescence. General linear models were used to examine the relationship between gray matter volume intercepts/slopes and BPD symptoms to understand the influence of the developmental trajectory of brain regions on BPD. Separate models were used to examine the relationship between MDD symptoms and volume intercepts to assess diagnostic specificity.

RESULTS

Lower childhood amygdala volume (intercept; age 13 centered) across scans was associated with higher adolescent BPD symptoms (β = -0.25, adjusted p = .015). There was no relationship between the slope of amygdala volume and BPD symptoms. There was no relationship between hippocampal volume and BPD or any relationship between amygdala or hippocampal volume and MDD symptoms during adolescence.

CONCLUSIONS

Our findings add evidence that supports the role of alterations in amygdala structure in BPD development. Decreased amygdala volume as early as age 13 may be an early indicator of the development of BPD during adolescence.

Neuroimaging stratification reveals the striatal vulnerability to stress as a risk for schizophrenia

The striatum, a core brain structure relevant for schizophrenia, exhibits heterogeneous volumetric changes in this illness. Due to this heterogeneity, its role in the risk of developing schizophrenia following exposure to environmental stress remains poorly understood. Using the putamen (a subnucleus of the striatum) as an indicator for convergent genetic risk of schizophrenia, 63 unaffected first-degree relatives of patients (22.08 ± 4.80 years) with schizophrenia (UFR-SZ) were stratified into two groups. Compared with healthy controls (HC; n = 59), voxel-based and brain-wide volumetric changes and their associations with stressful life events (SLE) were tested. These stratified associations were validated using two large population-based cohorts (the ABCD study; n = 1680, 11.92 ± 0.62 years; and UK Biobank, n = 20547, 55.38 ± 7.43 years). Transcriptomic analysis of brain tissues was used to identify the biological processes associated with the brain mediation effects on the SLE-psychosis relationship. The stratified UFR-SZ subgroup with smaller right putamen had a smaller volume in the left caudate when compared to HC; this caudate volume was associated with both a higher level of SLE and more psychotic symptoms. This caudate-SLE association was replicated in two independent large-scale cohorts, when individuals were stratified by both a higher polygenic burden for schizophrenia and smaller right putamen. In UFR-SZ, the caudate cluster mediated the relationship between SLE and more psychotic symptoms. This mediation was associated with the genes enriched in both glutamatergic synapses and response to oxidative stress. The stratified association between the striatum and stress highlights the differential vulnerability to stress, contributing to the complexity of the gene-by-environment etiology of schizophrenia.

Revealing potential drug targets in schizophrenia through proteome-wide Mendelian randomization genetic insights

BACKGROUND

Schizophrenia (SCZ) is a severe, chronic mental disorder with no current cure. Identifying novel pharmacological targets is crucial for developing more effective treatments.

METHODS

We performed two-sample Mendelian randomization (MR) analyses to estimate the associations between cerebrospinal fluid (CSF) containing 154 proteins and plasma containing 734 proteins and risk of SCZ. Bidirectional MR analysis, steiger filtering, bayesian colocalization, phenotypic scanning, and validation analysis were examined to validate the assumptions of MR. For proteins significantly associated with SCZ identified by MR, we explored their potential impact on brain structures, including cortical surface area (SA), thickness (TH), and the volume of subcortical structures.

RESULTS

MR analysis identified 13 protein-SCZ pairs at Bonferroni significance (P < 5.63 × 10-5). Notably, the genetically proxied protein level of neuromedin B (NMB) was associated with an increased risk for SCZ (odds ratio [OR] = 1.41; 95 % CI, 1.27 to 1.58; P = 6.68 × 10-10). Bayesian colocalization suggested that NMB shares genetic variations with SCZ. Further, NMB interacts with target proteins of current SCZ drugs and was validated in the UK Biobank. The genetically proxied NMB was positively associated with an increase in the surface area (SA) of the parahippocampal gyrus (β = 8.93 mm2, 95 % CI, 1.58 to 16.3, P = .02). Additionally, an increase in the genetically proxied SA of the parahippocampal gyrus was inversely associated with the risk of SCZ (OR = 0.996, 95 % CI, 0.993 to 0.999, P = .04).

CONCLUSIONS

The findings suggest that NMB may represent a promising target for pharmacological intervention in SCZ. This warrants further investigation into the specific constituents involved, which could have potential for follow-up studies.

Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.

Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia

INTRODUCTION

Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aims to explore the role of subcortex and insula and their relationship with symptom changes in first-episode schizophrenia (FES) patients by utilizing machine learning algorithms and functional connectivity (FC).

METHODS

The study encompasses 261 participants, sourced from two independent samples of FES patients and their matched healthy controls (HC). The discovery dataset includes 77 FES patients at baseline (FES0W) and 77 matched HCs, with the patients undergoing a follow-up scan after eight weeks of antipsychotic treatment (FES8W, N = 34). A validation dataset from another region comprises 47 FES patients and 47 matched HCs.

RESULTS

Significant differences in subcortical FCs were observed between FES and controls, correlating with symptom severity and symptom changes. Machine learning models were developed to diagnose schizophrenia on an individual basis, achieving a balanced accuracy of 79.55 % across diverse centers.

CONCLUSIONS

These findings suggest that subcortical connectivity patterns offer potential as biomarkers for schizophrenia, enabling personalized treatment strategies and improving prognosis by facilitating early diagnosis.

Thyroid Function and Brain Structure: Insight from a Mendelian Randomization Study

INTRODUCTION

Thyroid hormones play a critical role in brain development. However, the precise causal associations between thyroid function and structural changes in specific brain regions remain uncertain.

METHODS

We applied the univariate Mendelian randomization (UVMR) method to assess the causal effects of thyroid function on brain structure. Genome-wide association study (GWAS) data on thyroid-related traits from the ThyroidOmics Consortium including free thyroxine (FT4), free tri-iodothyronine (FT3), thyroid-stimulating hormone (TSH), FT3/FT4 ratio, as well as dichotomized high and low TSH levels were used as exposures. GWAS data on cortical thickness, surface area, and volume of subcortical structures served as outcomes. Inverse variance weighted was the main estimate method. Subsequently, multivariable MR (MVMR) was conducted to validate significant causal associations identified in UVMR.

RESULTS

UVMR analysis demonstrated a statistically significant inverse association between genetically predicted FT4 and putamen volume (β = -71.91 mm3, 95% confidence interval: -112.11 mm3 to -31.71 mm3, p = 4.54 × 10-4). The findings were robust in sensitivity analysis. MVMR analysis further confirmed a persistent causal relationship between FT4 and putamen volume after adjusting for FT3, TSH, and neuropsychiatric disorders. Functional enrichment analyses indicated the pathways by which FT4 influences putamen volume may be related to the thyroid hormone signaling pathway, sodium-independent organic anion transport, and Rap1 signaling pathway.

CONCLUSION

MR analysis provides evidence for causal relationships between thyroid function and brain structural alterations, particularly highlighting the impact of FT4 on putamen volume. Further research is warranted to elucidate the underlying mechanisms by which thyroid hormones modulate brain structure.

Deciphering the genetic basis of behavioral traits in dogs: Observed-trait GWAS and latent-trait GWAS analysis reveal key genes and variants

Dogs exhibit remarkable phenotypic diversity, particularly in behavioral traits, making them an excellent model for studying the genetic basis of complex behaviors. Behavioral traits such as aggression and fear are highly heritable among different dog breeds, but their genetic basis is largely unknown. We used the genome-wide association study (GWAS) to identify candidate genes associated with nine behavioral traits including; stranger-directed aggression (SDA), owner-directed aggression (ODA), dog-directed aggression (DDA), stranger-directed fear (SDF), nonsocial fear (NF), dog-directed fear (DDF), touch sensitivity (TS), separation-related behavior (SRB) and attachment attention-seeking (AAS). The observed behavioral traits were collected from 38,714 to 40,460 individuals across 108 modern dog breeds. We performed a GWAS based on a latent trait extracted using the confirmatory factor analysis (CFA) method with nine observable behavioral traits and compared the results with those from the GWAS of the observed traits. Using both observed-trait and latent-trait GWAS, we identified 41 significant SNPs that were common between both GWAS methods, of which 26 were pleiotropic, as well as 10 SNPs unique to the latent-trait GWAS, and 5 SNPs unique to the observed-trait GWAS discovered. These SNPs were associated with 21 genes in latent-trait GWAS and 22 genes in the observed-trait GWAS, with 19 genes shared by both. According to previous studies, some of the genes from this study have been reported to be related to behavioral and neurological functions in dogs. In the human population, these identified genes play a role in either the formation of the nervous system or are linked to various mental health conditions. Taken together, our findings suggest that latent-trait GWAS for behavioral traits in dogs identifies significant latent genes that are neurologically prioritized.

Mendelian randomization in Alzheimer's disease and mild cognitive impairment: Hippocampal volume associations

This study investigates the association between cognitive dysfunction and hippocampal volumes in Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using Mendelian randomization. A meta-analysis of 503 healthy controls, 562 MCI patients, and 389 CE patients revealed significant reductions in hippocampal and subregion volumes in MCI and AD compared to controls. While various subregions showed volume reductions, no causal relationship between hippocampal volume and AD was established through Mendelian randomization analysis. In conclusion, significant volume reductions were observed in MCI and AD patients, highlighting the complexity of the relationship between hippocampal volume and cognitive impairment.

Gene-environment interactions in the influence of maternal education on adolescent neurodevelopment using ABCD study

Maternal education was strongly correlated with adolescent brain morphology, cognitive performances, and mental health. However, the molecular basis for the effects of maternal education on the structural neurodevelopment remains unknown. Here, we conducted gene-environment-wide interaction study using the Adolescent Brain Cognitive Development cohort. Seven genomic loci with significant gene-environment interactions (G×E) on regional gray matter volumes were identified, with enriched biological functions related to metabolic process, inflammatory process, and synaptic plasticity. Additionally, genetic overlapping results with behavioral and disease-related phenotypes indicated shared biological mechanism between maternal education modified neurodevelopment and related behavioral traits. Finally, by decomposing the multidimensional components of maternal education, we found that socioeconomic status, rather than family environment, played a more important role in modifying the genetic effects on neurodevelopment. In summary, our study provided analytical evidence for G×E effects regarding adolescent neurodevelopment and explored potential biological mechanisms as well as social mechanisms through which maternal education could modify the genetic effects on regional brain development.

Genetic Vulnerability to Social Anxiety Disorder

Most anxiety disorders 'run within families': people suffering from an anxiety disorder often have family members who are highly anxious as well. In this chapter, we explore recent work devoted to unraveling the complex interplay between genes and environment in the development of anxiety. We review studies focusing on the genetic vulnerability to develop social anxiety disorder (SAD), as SAD is one of the most prevalent anxiety disorders, with an early onset, a chronic course, and associated with significant life-long impairments. More insight into the development of SAD is thus of uttermost importance.First, we will discuss family studies, twin studies, and large-sized population-based registry studies and explain what these studies can reveal about the genetic vulnerability to develop anxiety. Next, we describe the endophenotype approach; in this context, we will summarize results from the Leiden Family Lab study on Social Anxiety Disorder. Subsequently, we review the relationship between the heritable trait 'behavioral inhibition' and the development of SAD, and highlight the relevance of this work for the development and improvement of preventative and therapeutic interventions for socially anxious youth.

A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics

Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many efforts have been made for this task via building linear models between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). To the best of our knowledge, linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse influences on imaging QTs. In this paper, we propose a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. And then designs a multi-task one-to-one layer and imposes a combined penalty to identify SNPs that make significant contributions. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with feature selection. We compared MTDFS to multi-task linear regression (MTLR) and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than MTLR and DFS on the QT-SNP relationship identification and feature selection. Thus, MTDFS is powerful for identifying risk loci and could be a great supplement to brain imaging genetics.

Macrostructural Brain Morphology as Moderator of the Relationship Between Pandemic-Related Stress and Internalizing Symptomology During COVID-19 in High-Risk Adolescents

BACKGROUND

According to person-by-environment models, individual differences in traits may moderate the association between stressors and the development of psychopathology; however, findings in the literature have been inconsistent and little literature has examined adolescent brain structure as a moderator of the effects of stress on adolescent internalizing symptoms. The COVID-19 pandemic presented a unique opportunity to examine the associations between stress, brain structure, and psychopathology. Given links of cortical morphology with adolescent depression and anxiety, the current study investigated whether cortical morphology moderated the relationship between stress from the COVID-19 pandemic and the development of internalizing symptoms in familial high-risk adolescents.

METHODS

Prior to the COVID-19 pandemic, 72 adolescents (27 male) completed a measure of depressive and anxiety symptoms and underwent magnetic resonance imaging. T1-weighted images were acquired to assess cortical thickness and surface area. Approximately 6 to 8 months after COVID-19 was declared a global pandemic, adolescents reported their depressive and anxiety symptoms and pandemic-related stress.

RESULTS

Adjusting for pre-pandemic depressive and anxiety symptoms and stress, increased pandemic-related stress was associated with increased depressive but not anxiety symptoms. This relationship was moderated by cortical thickness and surface area in the anterior cingulate and cortical thickness in the medial orbitofrontal cortex such that increased stress was only associated with increased depressive and anxiety symptoms among adolescents with lower cortical surface area and higher cortical thickness in these regions.

CONCLUSIONS

Results further our understanding of neural vulnerabilities to the associations between stress and internalizing symptoms in general and during the COVID-19 pandemic in particular.

False recall is associated with larger caudate in males but not in females

After learning semantically related words, some people are more likely than others to incorrectly recall unstudied but semantically related lures (i.e., Deese-Roediger-McDermott [DRM] false recall). Previous studies have suggested that neural activity in subcortical regions (e.g., the caudate) is involved in false memory, and that there may be sex differences in the neural basis of false memory. However, sex-specific associations between subcortical volumes and false memory are not well understood. This study investigated whether sex modulates the associations between subcortical volumes and DRM false recall in 400 healthy college students. Volumes of subcortical regions including the caudate, accumbens, amygdala, hippocampus, pallidum, putamen and thalamus were obtained from structural magnetic resonance images and measured using FreeSurfer. The results showed that males had lower true and false recall but larger subcortical volumes than females. Interestingly, higher false recall was associated with a larger caudate in males, but not in females. This association was significant after controlling for age and intracranial volume. This study provides new evidence on the neural basis of false recall. It suggests that the caudate plays a role in false recall in young men, and that future studies of the neural correlates of false memory should consider sex differences.

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.

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.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits

BACKGROUND

The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus's high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential.

METHODS

We analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits.

RESULTS

Our analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10-9 within the 24 HASV traits, located across 93 chromosomal regions. Notably, the regions 12q14.3, 17q21.31, 12q24.22, 6q21, 9q33.1, 6q25.1, and 2q24.2 were found to influence multiple HASVs. Gene set analysis revealed enrichment of neural differentiation and signaling pathways, as well as protein binding and degradation. Of 240 HASV-neuropsychiatric trait pairs, 75 demonstrated significant genetic correlations (P < 0.05/240), revealing 433 pleiotropic loci. Particularly, genes like ACBD4, ARHGAP27, KANSL1, MAPT, ARL17A, and ARL17B were involved in over 50 HASV-neuropsychiatric pairs. Leveraging Mendelian randomization analysis, we further confirmed that atrophy in the left hippocampus, right hippocampus, right hippocampal body, and right CA1-3 region were associated with an increased risk of developing Parkinson's disease (PD). Furthermore, PRS for all four HASVs were significantly linked to a higher risk of Parkinson's disease (PD), with the highest hazard ratio (HR) of 1.30 (95% CI 1.18-1.43, P = 6.15 × 10⁻⁸) for right hippocampal volume.

CONCLUSIONS

These findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages.

NeuroimaGene: an R package for assessing the neurological correlates of genetically regulated gene expression

BACKGROUND

We present the NeuroimaGene resource as an R package designed to assist researchers in identifying genes and neurologic features relevant to psychiatric and neurological health. While recent studies have identified hundreds of genes as potential components of pathophysiology in neurologic and psychiatric disease, interpreting the physiological consequences of this variation is challenging. The integration of neuroimaging data with molecular findings is a step toward addressing this challenge. In addition to sharing associations with both molecular variation and clinical phenotypes, neuroimaging features are intrinsically informative of cognitive processes. NeuroimaGene provides a tool to understand how disease-associated genes relate to the intermediate structure of the brain.

RESULTS

We created NeuroimaGene, a user-friendly, open access R package now available for public use. Its primary function is to identify neuroimaging derived brain features that are impacted by genetically regulated expression of user-provided genes or gene sets. This resource can be used to (1) characterize individual genes or gene sets as relevant to the structure and function of the brain, (2) identify the region(s) of the brain or body in which expression of target gene(s) is neurologically relevant, (3) impute the brain features most impacted by user-defined gene sets such as those produced by cohort level gene association studies, and (4) generate publication level, modifiable visual plots of significant findings. We demonstrate the utility of the resource by identifying neurologic correlates of stroke-associated genes derived from pre-existing analyses.

CONCLUSIONS

Integrating neurologic data as an intermediate phenotype in the pathway from genes to brain-based diagnostic phenotypes increases the interpretability of molecular studies and enriches our understanding of disease pathophysiology. The NeuroimaGene R package is designed to assist in this process and is publicly available for use.

Brain Iron in signature regions relating to cognitive aging in older adults: the Taizhou Imaging Study

BACKGROUND

Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer's disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults.

METHODS

In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 2 signature regions where atrophy affected by aging and AD only: Aging (AG) -specific and AD signature meta-ROIs. The QSM and cortical morphometry means of the above ROIs were also computed.

RESULTS

Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse ZMMSE than did those in the lower tertile [β = -0.104, p = 0.026;β = -0.118, p = 0.021, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict decline in ZMoCA and key domains such as attention and visuospatial function (all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific signature and AD signature regions (all p < 0.05).

CONCLUSION

AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.

The genetic landscape of basal ganglia and implications for common brain disorders

The basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted univariate and multivariate genome-wide association analyses (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 Europeans with replication in 4,808 white and generalization in 5,220 non-white Europeans. Our multivariate GWAS identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 55.6% at P < 0.05 and 87.5% showed the same direction, revealing a distributed genetic architecture across basal ganglia structures. Of these, 50 loci were novel, including exonic regions of APOE, NBR1 and HLAA. We examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The strongest genetic overlap was between basal ganglia and Parkinson's disease, as supported by robust LD-score regression-based genetic correlations. Mendelian randomization indicated genetic liability to larger striatal volume as potentially causal for Parkinson's disease, in addition to a suggestive causal effect of greater genetic liability to Alzheimer's disease on smaller accumbens. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders.

The Causal Relationships Between Gut Microbiota, Brain Volume, and Intelligence: A Two-Step Mendelian Randomization Analysis

BACKGROUND

Growing evidence indicates that dynamic changes in gut microbiome can affect intelligence; however, whether these relationships are causal remains elusive. We aimed to disentangle the poorly understood causal relationship between gut microbiota and intelligence.

METHODS

We performed a 2-sample Mendelian randomization (MR) analysis using genetic variants from the largest available genome-wide association studies of gut microbiota (N = 18,340) and intelligence (N = 269,867). The inverse-variance weighted method was used to conduct the MR analyses complemented by a range of sensitivity analyses to validate the robustness of the results. Considering the close relationship between brain volume and intelligence, we applied 2-step MR to evaluate whether the identified effect was mediated by regulating brain volume (N = 47,316).

RESULTS

We found a risk effect of the genus Oxalobacter on intelligence (odds ratio = 0.968 change in intelligence per standard deviation increase in taxa; 95% CI, 0.952-0.985; p = 1.88 × 10-4) and a protective effect of the genus Fusicatenibacter on intelligence (odds ratio = 1.053; 95% CI, 1.024-1.082; p = 3.03 × 10-4). The 2-step MR analysis further showed that the effect of genus Fusicatenibacter on intelligence was partially mediated by regulating brain volume, with a mediated proportion of 33.6% (95% CI, 6.8%-60.4%; p = .014).

CONCLUSIONS

Our results provide causal evidence indicating the role of the microbiome in intelligence. Our findings may help reshape our understanding of the microbiota-gut-brain axis and development of novel intervention approaches for preventing cognitive impairment.

A sex-stratified analysis of the genetic architecture of human brain anatomy

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects. Notable exceptions include higher mean heritability in the female group for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and a common variant with stronger effect in male participants mapping to RBFOX1 - a gene linked to multiple neuropsychiatric disorders more common in men. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research in growing datasets.

Multitrait Analysis to Decipher the Intertwined Genetic Architecture of Neuroanatomical Phenotypes and Psychiatric Disorders

BACKGROUND

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven to be challenging, and new approaches are needed to infer the genetic structures that may underlie those phenotypes. Multivariate analyses are a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

METHODS

First, we conducted univariate and multivariate genome-wide association studies for 9 MRI-derived brain volume phenotypes in 20,000 UK Biobank participants. Next, we performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches could distinguish disorder-associated and non-disorder-associated variants from 6 psychiatric disorders: bipolar disorder, attention-deficit/hyperactivity disorder, autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach.

RESULTS

A univariate MRI genome-wide association study revealed only negligible genetic correlations with psychiatric disorders, while a multitrait genome-wide association study identified multiple new associations and showed significant enrichment for variants related to both attention-deficit/hyperactivity disorder and schizophrenia. Clustering analyses also detected 2 clusters that showed not only enrichment for association with attention-deficit/hyperactivity disorder and schizophrenia but also a consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways in both MRI phenotypes and schizophrenia.

CONCLUSIONS

Our results show that multitrait association signature can be used to infer genetically driven latent MRI variables associated with psychiatric disorders, thereby opening paths for future biomarker development.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

Causal effects of sepsis on structural changes in cerebral cortex: A Mendelian randomization investigation

Previous research has shown a strong correlation between sepsis and brain structure. However, whether this relationship represents a causality remains elusive. In this study, we employed Mendelian randomization (MR) to probe the associations of genetically predicted sepsis and sepsis-related death with structural changes in specific brain regions. Genome-wide association study (GWAS) data for sepsis phenotypes (sepsis and sepsis-related death) were obtained from the IEU OpenGWAS. Correspondingly, GWAS data for brain structural traits (volume of the subcortical structure, cortical thickness, and surface area) were derived from the ENIGMA consortium. Inverse variance weighted was mainly utilized to assess the causal effects, while weighted median and MR-Egger regression served as complementary methods. Sensitivity analyses were implemented with Cochran Q test, MR-Egger regression, and MR-PRESSO. In addition, a reverse MR analysis was carried out to assess the possibility of reverse causation. We identified that genetic liability to sepsis was normally significantly associated with a reduced surface area of the postcentral gyrus (β = -35.5280, SE = 13.7465, P = .0096). The genetic liability to sepsis-related death showed a suggestive positive correlation with the surface area of fusiform gyrus (β = 11.0920, SE = 3.6412, P = .0023) and posterior cingulate gyrus (β = 3.6530, SE = 1.6684, P = .0286), While it presented a suggestive negative correlation with surface area of the caudal middle frontal gyrus (β = -11.4586, SE = 5.1501, P = .0261) and frontal pole (β = -1.0024, SE = 0.4329, P = .0206). We also indicated a possible bidirectional causal association between genetic liability to sepsis-related death and the thickness of the transverse temporal gyrus. Sensitivity analyses verified the robustness of the above associations. These findings suggested that genetically determined liability to sepsis might influence the specific brain structure in a causal way, offering new perspectives to investigate the mechanism of sepsis-related neuropsychiatric disorders.

Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

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. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, 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 signalling and brain ageing-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 ADHD. 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.

Transcriptomic decoding of regional cortical vulnerability to major depressive disorder

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia

Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.

Exploring Neuroimaging Association Scores in adulthood ADHD and middle-age trajectories

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder associated with brain differences in children, but not in adults. A combined evaluation of the regional brain differences could improve statistical power and, consequently, allow the detection of possible effects in adults. Thus, our aim is to verify whether Neuroimaging Association Scores (NAS) are associated with adulthood ADHD and clinical trajectories of the disorder in midlife. Clinical and neuroimaging data were collected for 121 subjects with ADHD (mean age: 47.1 ± 10.5; 43% male) and 82 controls (mean age: 38.2 ± 9.0; 54.9% male). Cases were assessed seven and thirteen years after baseline diagnosis, and their clinical trajectories were classified as stable if they fulfilled ADHD diagnosis in all assessments or unstable if they presented remission and recurrence of symptoms. Neuroimaging data were acquired in the last clinical assessment (thirteen years after baseline) and NAS were calculated as a weighted sum of the associations previously reported by meta-analyses for three types of structural brain modalities: cortical thickness, cortical surface area, and subcortical volume. The NAS for cortical surface area was higher in cases compared to controls. No association was found for NAS and number of symptoms of ADHD or clinical trajectories. The fact that differences were restricted to ADHD diagnostic status suggests a susceptibility effect that is not extended to subtle aspects of the disorder. Our results also suggest that evaluating overall effects may have advantages especially when applied to adult ADHD samples.

Identifying the genetic association between the cerebral cortex and fibromyalgia

Fibromyalgia (FM) is a central sensitization syndrome that is strongly associated with the cerebral cortex. This study used bidirectional two-sample Mendelian randomization (MR) analysis to investigate the bidirectional causality between FM and the cortical surface area and cortical thickness of 34 brain regions. Inverse variance weighted (IVW) was used as the primary method for this study, and sensitivity analyses further supported the results. The forward MR analysis revealed that genetically determined thinner cortical thickness in the parstriangularis (OR = 0.0567 mm, PIVW = 0.0463), caudal middle frontal (OR = 0.0346 mm, PIVW = 0.0433), and rostral middle frontal (OR = 0.0285 mm, PIVW = 0.0463) was associated with FM. Additionally, a reduced genetically determined cortical surface area in the pericalcarine (OR = 0.9988 mm2, PIVW = 0.0085) was associated with an increased risk of FM. Conversely, reverse MR indicated that FM was associated with cortical thickness in the caudal middle frontal region (β = -0.0035 mm, PIVW = 0.0265), fusiform region (β = 0.0024 mm, SE = 0.0012, PIVW = 0.0440), the cortical surface area in the supramarginal (β = -9.3938 mm2, PIVW = 0.0132), and postcentral regions (β = -6.3137 mm2, PIVW = 0.0360). Reduced cortical thickness in the caudal middle frontal gyrus is shown to have a significant relationship with FM prevalence in a bidirectional causal analysis.

Transcriptional expression patterns of the cortical morphometric similarity network in progressive supranuclear palsy

BACKGROUND

It has been demonstrated that progressive supranuclear palsy (PSP) correlates with structural abnormalities in several distinct regions of the brain. However, whether there are changes in the morphological similarity network (MSN) and the relationship between changes in brain structure and gene expression remain largely unknown.

METHODS

We used two independent cohorts (discovery dataset: PSP: 51, healthy controls (HC): 82; replication dataset: PSP: 53, HC: 55) for MSN analysis and comparing the longitudinal changes in the MSN of PSP. Then, we applied partial least squares regression to determine the relationships between changes in MSN and spatial transcriptional features and identified specific genes associated with MSN differences in PSP. We further investigated the biological processes enriched in PSP-associated genes and the cellular characteristics of these genes, and finally, we performed an exploratory analysis of the relationship between MSN changes and neurotransmitter receptors.

RESULTS

We found that the MSN in PSP patients was mainly decreased in the frontal and temporal cortex but increased in the occipital cortical region. This difference is replicable. In longitudinal studies, MSN differences are mainly manifested in the frontal and parietal regions. Furthermore, the expression pattern associated with MSN changes in PSP involves genes implicated in astrocytes and excitatory and inhibitory neurons and is functionally enriched in neuron-specific biological processes related to synaptic signaling. Finally, we found that the changes in MSN were mainly negatively correlated with the levels of serotonin, norepinephrine, and opioid receptors.

CONCLUSIONS

These results have enhanced our understanding of the microscale genetic and cellular mechanisms responsible for large-scale morphological abnormalities in PSP patients, suggesting potential targets for future therapeutic trials.

Causal effects of hypertensive disorders of pregnancy on structural changes in specific brain regions: a Mendelian randomization study

This study utilized Mendelian randomization to explore the impact of hypertensive disorders of pregnancy and their subtypes on brain structures, using genome-wide association study data from the FinnGen consortium for hypertensive disorders of pregnancy exposure and brain structure data from the ENIGMA consortium as outcomes. The inverse-variance weighted method, along with Cochran's Q test, Mendelian randomization-Egger regression, Mendelian randomization-PRESSO global test, and the leave-one-out approach, were applied to infer causality and assess heterogeneity and pleiotropy. Findings indicate hypertensive disorders of pregnancy are associated with structural brain alterations, including reduced cortical thickness in areas like the insula, isthmus cingulate gyrus, superior temporal gyrus, temporal pole, and transverse temporal gyrus, and an increased surface area in the superior frontal gyrus. Specific associations were found for hypertensive disorders of pregnancy subtypes: chronic hypertension with superimposed preeclampsia increased cortical thickness in the supramarginal gyrus; preeclampsia/eclampsia led to thinner cortex in the lingual gyrus and larger hippocampal volume and superior parietal lobule surface area. Chronic hypertension was associated with reduced cortical thickness in the caudal and rostral anterior cingulate and increased surface area of the cuneus and thickness of the pars orbitalis cortex. Gestational hypertension showed no significant brain region changes. These insights clarify hypertensive disorders of pregnancies' neurological and cognitive effects by identifying affected brain regions.

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning

OBJECTIVE

Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques.

METHODS

Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated.

RESULTS

All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks.

CONCLUSIONS

These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

Neuroimaging and epigenetic analysis reveal novel epigenetic loci in major depressive disorder

BACKGROUND

Epigenetic modifications, such as DNA methylation, contribute to the pathophysiology of major depressive disorder (MDD). This study aimed to identify novel MDD-associated epigenetic loci using DNA methylation profiles and explore the correlations between epigenetic loci and cortical thickness changes in patients with MDD.

METHODS

A total of 350 patients with MDD and 161 healthy controls (HCs) were included in the epigenome-wide association studies (EWAS). We analyzed methylation, copy number alteration (CNA), and gene network profiles in the MDD group. A total of 234 patients with MDD and 135 HCs were included in neuroimaging methylation analysis. Pearson's partial correlation analysis was used to estimate the correlation between cortical thickness of brain regions and DNA methylation levels of the loci.

RESULTS

In total, 2018 differentially methylated probes (DMPs) and 351 differentially methylated regions (DMRs) were identified. DMP-related genes were enriched in two networks involved in the central nervous system. In neuroimaging analysis, patients with MDD showed cortical thinning in the prefrontal regions and cortical thickening in several occipital regions. Cortical thickness of the left ventrolateral prefrontal cortex (VLPFC, i.e. pars triangularis) was negatively correlated with eight DMPs associated with six genes (EML6, ZFP64, CLSTN3, KCNMA1, TAOK2, and NT5E).

CONCLUSION

Through combining DNA methylation and neuroimaging analyses, negative correlations were identified between the cortical thickness of the left VLPFC and DNA methylation levels of eight DMPs. Our findings could improve our understanding of the pathophysiology of MDD.