Major depressive disorder (MDD) is a highly prevalent psychiatric disorder with complex genetic underpinnings. While genome-wide association studies (GWAS) have identified multiple risk loci, pinpointing causal genes within the human brain remains challenging, particularly given the regulatory complexity across different cell types.

We performed summary data-based MR (SMR) and Bayesian colocalization analyses by integrating bulk-tissue eQTL data from 888 individuals with single-cell eQTL datasets from 192 donors representing major brain cell types (excitatory and inhibitory neurons, astrocytes, microglia, oligodendrocytes, OPCs/COPs, endothelial cells, and pericytes). GWAS summary statistics for MDD (170,756 cases and 329,443 controls) were used to assess the causal impact of gene expression. Sensitivity analyses, including the heterogeneity in dependent instruments (HEIDI) test and Steiger filtering, ensured robust inference.

In bulk tissue analyses, five genes (BTN3A2, SLC12A5, AREL1, GMPPB, and ZNF660) emerged as having robust causal evidence for MDD, displaying consistent MR signals and strong colocalization. Cell type-specific analyses revealed additional candidate genes in excitatory neurons (FLOT1, AL450423.1), astrocytes (AL121821.1), and oligodendrocytes (YLPM1, COP1).

Our integrative approach reveals that causal gene expression profiles differ markedly between bulk-tissue and specific brain cell types, emphasizing cellular heterogeneity in MDD pathogenesis and informing precision therapeutic strategies. These findings underscore the necessity of considering cell type-specific gene regulation when developing therapeutic interventions for MDD.

Many patients with major depressive disorder (MDD) experience limited treatment effectiveness due to an incomplete understanding of its neurobiological underpinnings. This review integrates neuroimaging and genetic data to examine structural and functional brain changes in MDD, alongside their genetic bases. A PRISMA-guided systematic review of imaging transcriptomics over the past decade was conducted using PubMed and Web of Science. Studies included MRI scans of both MDD patients and healthy controls, as well as brain-wide gene expression data, excluding those that were purely meta-analytical, lacked spatial correlations, or involved transdiagnostic analyses. Of the 206 studies reviewed, 20 met the inclusion criteria. Consistent patterns across studies reveal that key biological processes-such as synaptic signaling, calcium ion binding, neurodevelopment, immune regulation, and neurotransmitter transport-play a central role in brain alterations associated with MDD. Additionally, our findings suggest that electroconvulsive therapy (ECT) may alleviate symptoms by modulating these shared pathways. This review underscores the link between brain changes in MDD and specific gene expression profiles, offering insights that could inform more targeted therapeutic approaches.

Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.

We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.

Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.

These include measuring anhedonia using interest-activity score and employing a cross-sectional design.

This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.

Aging is a multilevel process of gradual decline that predicts morbidity and mortality. Independent investigations have implicated senescence of brain and peripheral physiology in psychiatric risk, but it is unclear whether these effects stem from unique or shared mechanisms.

To address this question, we analyzed clinical, blood chemistry, and resting-state functional neuroimaging data in a healthy aging cohort (n = 427; ages 36-100 years) and 2 disorder-specific samples including patients with early psychosis (100 patients, 16-35 years) and major depressive disorder (MDD) (104 patients, 20-76 years).

We identified sex-dependent coupling between blood chemistry markers of metabolic senescence (i.e., homeostatic dysregulation), functional brain network aging, and psychiatric risk. In females, premature aging of frontoparietal and somatomotor networks was linked to greater homeostatic dysregulation. It also predicted the severity and treatment resistance of mood symptoms (depression/anxiety [all 3 samples], anhedonia [MDD]) and social withdrawal/behavioral inhibition (avoidant personality disorder [healthy aging], negative symptoms [early psychosis]). In males, premature aging of the default mode, cingulo-opercular, and visual networks was linked to reduced homeostatic dysregulation and predicted the severity and treatment resistance of symptoms relevant to hostility/aggression (antisocial personality disorder [healthy aging], mania/positive symptoms [early psychosis]), impaired thought processes (early psychosis, MDD), and somatic problems (healthy aging, MDD).

Our findings identify sexually dimorphic relationships between brain dynamics, peripheral physiology, and risk for psychiatric illness, suggesting that the specificity of putative risk biomarkers and precision therapeutics may be improved by considering sex and other relevant personal characteristics.

Temporal lobe epilepsy (TLE) is associated to genetic predisposition, metabolic abnormalities, and disruptions in brain connectivity. However, the relationships between genetic factors, metabolic processes, and brain network dynamics are not yet fully understood. Simultaneous positron emission tomography and function magnetic resonance imaging (PET/fMRI) data were collected from 66 patients with TLE and 38 healthy controls (HCs). We compared differences in brain network dynamics between TLE patients and HCs using the multilayer network model constructed from extensive temporal features extracted from fMRI. Postmortem whole brain gene expression data were then utilized to identify genes associated with alterations in TLE connectome dynamics, with subsequent enrichment analysis for functional annotation, cellular, and disease associations. Mediation analysis further explored the interrelations among gene expression, glucose metabolism as measured by PET, and brain network dynamics as measured by fMRI. Compared with HCs, individuals with TLE exhibited increased module variability primarily in the default mode network and reduced module variability in the attention network. These case-control differences were validated through split-half analyses and remained unaffected by medication or lateralization. These aberrant module variability patterns were associated with gene expression profiles predominantly related to inhibitory neurons, postsynaptic cell components, MAPK signaling pathway, and these genes were significantly enriched relative to established epilepsy-related gene sets. Moreover, we observed that the effect of gene expression profile on the alterations in TLE connectome dynamics was significantly mediated by changes in glucose metabolism. These findings highlight that alterations in brain network dynamics in TLE are associated with transcriptomic signatures, and that glucose metabolic changes partially mediate this relationship, furthering insights into the biological basis of the disorder.

Psychiatric research has shifted from a neuroncentric view to understanding mental disorders as disturbances of heterogeneous brain networks. Oligodendrocyte precursor cells (OPCs)- actively involved in the modulation of neuronal functions - are altered in psychiatric patients, but the extent and related consequences are unclear. This review explores canonical and non-canonical OPC-related pathways in schizophrenia, bipolar disorder, post-traumatic stress disorder, and depression in humans, highlighting potential mechanisms shared across diagnostic entities.

Ex vivo magnetic resonance imaging (MRI) has revolutionized psychoradiological research by enabling detailed structural and pathological assessments of the brain in conditions ranging from psychiatric disorders to neurodegenerative diseases. By providing high-resolution images of postmortem brain tissue, ex vivo MRI overcomes several limitations inherent in in vivo imaging, offering unparalleled insights into the underlying pathophysiology of mental disorders. This review critically summarizes the state-of-the-art ex vivo MRI methodologies for neuroanatomical mapping and pathological characterization in psychoradiology, while also establishing standardized specimen processing protocols. Furthermore, we explore the prospects of application in ex vivo MRI in schizophrenia, major depressive disorder and bipolar disorder, highlighting its role in understanding neuroanatomical alterations, disease progression, and the validation of in vivo neuroimaging biomarkers.

The identification of relationships between individual differences in functional connectivity (FC) and behavior has been the focus of considerable investigation. Although emerging evidence has identified relationships between FC and cognitive performance, relationships between FC and measures of affect, including depressed mood, anhedonia, and anxiety, and decision-making style, including impulsivity and sensation seeking, appear to be more inconsistent across the literature. This may be due to low power, methodological differences across studies, including the use of global signal correction (GSR), or uncontrolled characteristics of the population.

Here, we evaluated measures of FC, regional variance, and global signal (GS) across six functional MRI (fMRI) sequences of different tasks and resting states and their relationship with individual differences in self-reported measures of symptoms of depression, anxiety, impulsivity, reward sensitivity, and sensation seeking, as well as demographic variables and acquisition order, within groups of distressed and healthy young adults (18-25 years old).

Adopting a training/testing sample structure to the analysis, we found no evidence of reproducible brain/behavior relationships despite identifying regions and connections that reflect reliable between-scan individual differences. However, summary measures of the GS were reproducibly associated with sex: The most consistent finding was an increase in low frequency variance of the blood-oxygenation-level-dependent (BOLD) signal from all gray matter regions in males relative to females. Post hoc analysis of GS topography yielded sex differences in a number of regions, including cerebellum and putamen. In addition, effects of paradigm acquisition order were observed on GS measures, including an increase in BOLD signal variance across time. In an exploratory analysis, a specific relationship between sex and relative high-frequency within-scanner motion was observed.

Together, the findings suggest that FC relationships with affective measures may be inconsistent or modest, but that global phenomena related to state and individual differences can be robust and must be evaluated, particularly in studies of psychiatric disorders such as mood disorders or ADHD, which show sex differences.

The heterogeneity of major depressive disorder (MDD) has hindered clinical translation and neuromarker identification. Biotyping facilitates solving the problems of heterogeneity, by dissecting MDD patients into discrete subgroups. However, interindividual variations suggest that depression may be conceptualized as a "continuum," rather than as a "category." We use a Bayesian model to decompose structural MRI features of MDD patients from a multisite cross-sectional cohort into three latent disease factors (spatial pattern) and continuum factor compositions (individual expression). The disease factors are associated with distinct neurotransmitter receptors/transporters obtained from open PET sources. Increases cortical thickness in sensory and decreases in orbitofrontal cortices (Factor 1) associate with norepinephrine and 5-HT2A density, decreases in the cingulo-opercular network and subcortex (Factor 2) associate with norepinephrine and 5-HTT density, and increases in social and affective brain systems (Factor 3) relate to 5-HTT density. Disease factor patterns can also be used to predict depressive symptom improvement in patients from the longitudinal cohort. Moreover, individual factor expressions in MDD are stable over time in a longitudinal cohort, with differentially expressed disease controls from a transdiagnostic cohort. Collectively, our data-driven disease factors reveal that patients with MDD organize along continuous dimensions that affect distinct sets of regions.

The G-protein-coupled receptor (GPR158), an orphan receptor, is highly expressed in the medial prefrontal cortex (mPFC) and identified as a novel therapeutic target for depression. Trilobatin is a naturally occurring food additive with potent neuroprotective properties. However, its pharmacological effects and molecular mechanisms against depression remain unknown. Therefore, we explored whether trilobatin alleviates depression by targeting GPR158. Our results indicated that trilobatin alleviated chronic unpredictable mild stress (CUMS)-induced depressive-like behavior in mice. Mitophagy contributed to the antidepressant-like effect of trilobatin, as evidenced by the qRT-PCR array. Furthermore, trilobatin up-regulated autophagy-associated protein expression, restored mitochondrial dynamic balance, and inhibited oxidative stress of mPFC in mice after CUMS insult and in corticosterone-induced primary neuron injury. Intriguingly, trilobatin directly bound to GPR158 and decreased its level of protein expression. GPR158 deficiency attenuated depressive-like behavior through promoting mitophagy, while the antidepressant effect of trilobatin was strengthened in GPR158-deficient mice. Our findings highlight that GPR158-mediated mitophagy acts as a crucial pharmacological target for depression and reveal a new-found pharmacological property of trilobatin: serving as a novel naturally occurring ligand of GPR158 to safeguard from depression by oxidative stress by promoting mitophagy.

Major depressive disorder (MDD) is a widespread psychiatric condition, recognized as the third leading cause of global disease burden in 2008. In the context of MDD, alterations in synaptic transmission within the prefrontal cortex (PFC) are associated with PFC hypoactivation, a key factor in cognitive function and mood regulation. Given the high energy demands of the central nervous system, these synaptic changes suggest a metabolic imbalance within the PFC of MDD patients. However, the cellular mechanisms underlying this metabolic dysregulation remain not fully elucidated. This study employs single-nucleus RNA sequencing (snRNA-seq) data to predict metabolic alterations in the dorsolateral PFC (DLPFC) of MDD patients. Our analysis revealed cell type-specific metabolic patterns, notably the disruption of oxidative phosphorylation and carbohydrate metabolism in the DLPFC of MDD patients. Gene set enrichment analysis based on human phenotype ontology predicted alterations in serum lactate levels in MDD patients, corroborated by the observed decrease in lactate levels in MDD patients compared to 47 age-matched healthy controls (HCs). This transcriptional analysis offers novel insights into the metabolic disturbances associated with MDD and the energy dynamics underlying DLPFC hypoactivation. These findings are instrumental for comprehending the pathophysiology of MDD and may guide the development of innovative therapeutic strategies.

In patients with major depressive disorder (MDD) and animal models of depression, key pathological hallmarks include activation of microglia as well as atrophy and loss of astrocytes. Under certain pathological conditions, microglia can inflict damage to neurons and astrocytes. However, the precise mechanisms underlying how activated microglia induced astrocyte atrophy and loss remain enigmatic. In this study, a depression model induced by chronic social defeat stress (CSDS) is utilized. The results show that CSDS induces significant anxiety- and depression-like behaviors, along with notable astrocyte atrophy and apoptosis, microglial activation, and elevated levels of microglial interleukin-6 (IL-6). Subsequent studies demonstrate that IL-6 released from activated microglia promotes astrocyte apoptosis. Furthermore, the knockdown of the P2X7 receptor (P2X7R) in microglia, which is implicated in the stress response, reduces stress-induced microglial activation, IL-6 release, and astrocyte apoptosis. Direct inhibition of microglia by minocycline corroborates these effects. The selective knockdown of IL-6 in microglia and IL-6 receptors in astrocytes effectively mitigates depression-like behaviors and reduces astrocyte atrophy. This study identifies microglial IL-6 as a key factor that contributes to astrocyte apoptosis and depressive symptoms. Consequently, the IL-6/IL-6R pathway has emerged as a promising target for the treatment of depression.

Brief sleep loss alters cognition and synaptic structures of principal neurons in hippocampus and neocortex. However, while in vivo recording and bioinformatic data suggest that inhibitory interneurons are more strongly affected by sleep loss, it is unclear how sleep and sleep deprivation affect interneurons' synapses. Disruption of the SST+ interneuron population seems to be a critical early sign of neuropathology in Alzheimer's dementia, schizophrenia, and bipolar disorder - and the risk of developing all three is increased by habitual sleep loss. We aimed to test how the synaptic structures of SST+ interneurons in various brain regions are affected by brief sleep disruption.

We used Brainbow 3.0 to label SST+ interneurons in the dorsal hippocampus, prefrontal cortex, and visual cortex of male SST-CRE transgenic mice, then compared synaptic structures in labeled neurons after a 6-h period of ad lib sleep, or gentle handling sleep deprivation (SD) starting at lights on.

Dendritic spine density among SST+ interneurons in both hippocampus and neocortex was altered in a subregion-specific manner, with increased overall and thin spine density in CA1, dramatic increases in spine volume and surface area in CA3, and small but significant changes (primarily decreases) in spine size in CA1, PFC and V1.

Our suggest that the synaptic connectivity of SST+ interneurons is significantly altered in a brain region-specific manner by a few hours of sleep loss. This suggests a cell type-specific mechanism by which sleep loss disrupts cognition and alters excitatory-inhibitory balance in brain networks.

Major depressive disorder (MDD) is associated with disrupted brain structural integration. Morphometric similarity offers a means to capture the coordinated patterns of various structural features. However, it remains unknown whether MDD-related changes can be detected in cortical morphometric similarity through the Morphometric Inverse Divergence (MIND) network. Additionally, the role of brain connectivity in shaping these alterations, and their links to neuroreceptors and gene expression, have yet to be investigated.

Using the T1-weighted MRI data from 71 patients with first-episode, treatment-naïve MDD and 69 healthy controls, we constructed the MIND network for all participants. We then performed between-group comparisons to investigate abnormalities in the network and spatial relationships between the observed patterns of MIND disruption and the patterns of neuroreceptors were estimated. Network-based spreading was utilized to explore the abnormalities constrained by brain connectivity based on structural, functional, and transcriptional connectome architecture and to further identify potential epicenters of MDD. In addition, partial least squares regression was conducted to examine the associations of gene expression profiles with MIND changes in MDD.

Patients with MDD showed significantly increased MIND in regions associated with sensation and cognition compared with healthy controls, with this altered pattern being influenced by a combination of transcriptional and structural connectivity, and potential epicenters of MDD were identified in the frontal, parietal, and paracentral cortices. Furthermore, the cortical map of case-control differences in MIND was spatially correlated with the cannabinoid CB1 receptor and the brain-wide expression of a weighted combination of genes. These genes were enriched for neurobiologically relevant pathways and preferentially expressed in different cell classes and cortical layers.

These results highlight the abnormal pattern of morphometric similarity observed in MDD, shedding light on the complex interplay between disrupted macroscale coordinated morphology and microscale molecular organization in MDD.

Attention-deficit hyperactivity disorder (ADHD) is a heterogenous behavioral disorder with inattention, hyperactivity and impulsivity symptoms, indicating the important implication of identifying biotypes and its epicenters in understanding disease's pathogenesis. The study investigated the neuromorphic heterogeneity relating to transcriptional similarity architecture in ADHD, and further analyzed the epicenters of network-spreading in each ADHD biotype and their correlations with clinical characteristics. Individuals with ADHD could be identified into two discriminative biotypes that exhibited distinct neuromorphic aberrances. As increased regional cortical thickness deviation in ADHD, the first component of partial least squares (PLS1) positively weighted genes were over-expressed, whereas PLS1 negatively weighted genes were under-expressed as its reduction. Both ADHD biotypes exhibited distinct disease epicenters that distributed in cognitive control and attention networks with significantly heterogeneous characteristics, holding promise for advancing our understanding, and ultimately the treatment, of ADHD. Overall, our findings identified two discriminative biotypes and its epicenters in ADHD, promoting the understanding of underlying transcriptome-neuroimaging relationships.

Previous research has found brain structural and functional abnormalities in patients with insomnia disorder (ID). However, the relationship between brain abnormalities in ID and brain gene expression is unclear. This study explored the relationship between gene expression and brain structural or functional abnormalities in ID, and we validated the reliability of the results with two independent datasets (discover dataset: healthy control (HC) = 129, ID = 264; validation dataset: HC = 160, ID = 115). Brain imaging results show that ID has abnormal resting-state spontaneous activity, regional homogeneity, and widespread gray matter volume reduction compared to HC. The association analysis results with gene expression further revealed that brain function abnormalities in ID were significantly associated with gene expression, but structural abnormalities were not. This study establishes a link between transcriptional changes and brain functional abnormalities in ID, revealing a genetic basis that may involve several biological pathways. Specifically, these pathways include hormonal regulation of the hypothalamic-pituitary-adrenal (HPA) axis, which plays a crucial role in stress response and sleep regulation; ion transport across membranes, vital for neuronal communication; and inhibitory neuronal regulation, essential for maintaining normal brain function. Furthermore, the ID-related genes are enriched for brain tissue and cortical cells, emphasizing their relevance in understanding the biological underpinnings of ID.

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Multiple lines of evidence indicate that mood disorders, such as major depressive and bipolar disorder, are associated with abnormalities in neuroglial cells. This chapter discusses the existing literature investigating the potential role of astrocytes, oligodendrocytes, and microglia in mood pathology. We will describe evidence from in vivo imaging, postmortem, animal models based on (stress) paradigms that mimic depressive-like behavior, and biomarker studies in blood and cerebrospinal fluid in patients with mood disorders. The effect of medication used in the treatment of mood disorders, such as antidepressants and lithium, on glial function is discussed. Lastly, we highlight the most relevant findings about potential deficiencies in glia-glia crosstalk in mood disorders. Overall, decreased astrocyte and oligodendrocyte density and expression and microglial changes in homeostatic functions have frequently been put forward in MDD pathology. Studies of BD report similar findings to some extent; however, the evidence is less well established. Together, these findings are suggestive of reduced glial cell function leading to potential white matter abnormalities, glutamate dysregulation, disrupted neuronal functioning, and neurotransmission. However, more research is required to better understand the exact mechanisms underlying glial cell contributions to mood disorder development.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

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.

Understanding the neurobiological effects of stress is critical for addressing the etiology of major depressive disorder (MDD). Using a dimensional approach involving individuals with differing degree of MDD risk, we investigated 1) the effects of acute stress on cortico-cortical and subcortical-cortical functional connectivity (FC) and 2) how such effects are related to gene expression and receptor maps.

Across 115 participants (37 control, 39 remitted MDD, 39 current MDD), we evaluated the effects of stress on FC during the Montreal Imaging Stress Task. Using partial least squares regression, we investigated genes whose expression in the Allen Human Brain Atlas was associated with anatomical patterns of stress-related FC change. Finally, we correlated stress-related FC change maps with opioid and GABAA (gamma-aminobutyric acid A) receptor distribution maps derived from positron emission tomography.

Results revealed robust effects of stress on global cortical connectivity, with increased global FC in frontoparietal and attentional networks and decreased global FC in the medial default mode network. Moreover, robust increases emerged in FC of the caudate, putamen, and amygdala with regions from the ventral attention/salience network, frontoparietal network, and motor networks. Such regions showed preferential expression of genes involved in cell-to-cell signaling (OPRM1, OPRK1, SST, GABRA3, GABRA5), similar to previous genetic MDD studies.

Acute stress altered global cortical connectivity and increased striatal connectivity with cortical regions that express genes that have previously been associated with imaging abnormalities in MDD and are rich in μ and κ opioid receptors. These findings point to overlapping circuitry underlying stress response, reward, and MDD.

Brain connectivity arises from interactions across biophysical scales, ranging from molecular to cellular to anatomical to network level. To date, there has been little progress toward integrated analysis across these scales. To bridge this gap, from a unique cohort of 98 individuals, we collected antemortem neuroimaging and genetic data, as well as postmortem dendritic spine morphometric, proteomic and gene expression data from the superior frontal and inferior temporal gyri. Through the integration of the molecular and dendritic spine morphology data, we identified hundreds of proteins that explain interindividual differences in functional connectivity and structural covariation. These proteins are enriched for synaptic structures and functions, energy metabolism and RNA processing. By integrating data at the genetic, molecular, subcellular and tissue levels, we link specific biochemical changes at synapses to connectivity between brain regions. These results demonstrate the feasibility of integrating data from vastly different biophysical scales to provide a more comprehensive understanding of brain connectivity.

This study aims to elucidate the neuroimaging changes associated with major depressive disorder (MDD) and their relationship with genetic characteristics. We conducted a global-brain functional connectivity (GFC) and genetic-neuroimaging correlation analysis on 42 MDD patients and 42 healthy controls (HCs), exploring the correlation between GFC abnormalities and clinical variables. Results showed that compared to HCs, MDD patients had significantly decreased GFC values in the bilateral posterior cingulate cortex/precuneus and increased GFC values in the left and right cerebellum Crus I/II. Additionally, a negative correlation was observed between the GFC values of the left cerebellum Crus I/II and subjective support scores, as well as social support revalued scale total scores. We identified genes associated with GFC changes in MDD, which are enriched in biological processes such as synaptic transmission and ion transport. Our findings indicate the presence of abnormal GFC values in severe depression, complementing the pathological research on the condition. Furthermore, this study provides preliminary evidence for the correlation between social support levels and brain functional connectivity, offering insights into the potential association between GFC changes and gene expression in MDD patients.

Previous studies have demonstrated widespread brain neurodegeneration in Alzheimer's disease (AD). However, the neurobiological and pathogenic substrates underlying this structural atrophy across the AD spectrum remain largely understood.

In this study, we obtained structural MRI data from ADNI datasets, including 83 participants with early-stage cognitive impairments (EMCI), 83 with late-stage mild cognitive impairments (LMCI), 83 with AD, and 83 with normal controls (NC). Our goal was to explore structural atrophy across the full clinical AD spectrum and investigate the genetic mechanism using gene expression data from the Allen Human Brain Atlas.

As a result, we identified significant volume atrophy in the left thalamus, left cerebellum, and bilateral middle frontal gyrus across the AD spectrum. These structural changes were positively associated with the expression levels of genes such as ABCA7, SORCS1, SORL1, PILRA, PFDN1, PLXNA4, TRIP4, and CD2AP, while they were negatively associated with the expression levels of genes such as CD33, PLCG2, APOE, and ECHDC3 across the clinical AD spectrum. Further gene enrichment analyses revealed that the positively associated genes were mainly involved in the positive regulation of cellular protein localization and the negative regulation of cellular component organization, whereas the negatively associated genes were mainly involved in the positive regulation of iron transport.

Overall, these results provide a deeper understanding of the biological mechanisms underlying structural changes in prodromal and clinical AD.

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

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.

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.

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.

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.

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.

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.