Biology subjects

Meaney, M. J.

Publications and source records attributed to Meaney, M. J..

15 recordsLinked to original sources

bioRxiv2026

From Geometry to Hierarchy: Charting the Dominant Modes of Human Cortical Function Through Development

Cortical function in the human cortex is spatially patterned along a macroscale information processing hierarchy. This hierarchy tracks a sensory-association axis anchored at one end by unimodal sensory regions and at the other by transmodal association areas. Biophysical models of brain dynamics suggest that this axis should align with the dominant, resonant modes of cortical anatomy and geometry, but they do not. Instead, the dominant anatomical modes correspond to simple spatial gradients extending along rostro-caudal, medio-lateral, and dorso-ventral axes. How does this divergence between anatomical and functional modes of the cortex arise? Here, using four independent functional magnetic resonance imaging datasets spanning infancy to young adulthood (n=1,512, aged 0-21 years), we show that this spatial divergence is attributable to the maturation of long-range cortico-cortical inter-regional functional coupling. Specifically, we find that the dominant functional mode of the human cortex during the first 3 to 4 years of life closely resembles the rostro-caudal mode of cortical geometry, then shifts to a rudimentary sensory-association-like mode between 4 and 6 years of age and ultimately matures into a prototypical sensory-association mode by age 13. In infancy and early childhood, a linear model predicting the topography of the dominant functional mode from modes of cortical geometry substantially outperforms a model relying on the adult sensory-association mode, whereas the reverse is true in later stages of development. Furthermore, targeted removal of long-range connections causes the adult functional mode to regress from a sensory-association topography to an infant-like rostro-caudal mode. Our findings indicate that the maturation of long-range cortico-cortical coupling supports the gradual emergence of hierarchical modes of cortical function from an organization initially dominated by geometric constraints.

neuroscience↗

bioRxiv2026

Developing a multi-modal neuroimaging-based BrainAge model across childhood

BrainAge models hold promise as a clinical biomarker for developmental brain health, especially in childhood when there is the potential for early intervention. To distinguish between normative developmental variance and pathological divergence, BrainAge models should reflect the dynamic and diverse neurodevelopmental processes that occur in distinct developmental windows across childhood. We utilized multi-modal neuroimaging data from three pediatric cohorts covering ages 4 to 13 years (n = 1005, 2126 scans), split into Train and Test datasets. Twelve sex-stratified BrainAge models were built stratified by type and different combinations of neuroimaging features. Model types were "Full-Span" models covering the full age range, and "Phase-Specific" models split into early- and late-childhood. We first compared BrainAge estimates in the Test dataset amongst our candidate models, then benchmarked the best-performing model against published pre-trained models and DNA-based biological age measures. Our findings show that a BrainAge model that was phase-specific and consisted of both structural and functional features (cortical thickness, subcortical volumes, and functional network integration measures) showed good prediction of age and best distinguished between healthy and symptomatic subgroups. We present a proof-of-concept for developmental models supporting building BrainAge models of higher temporal resolution that align to different childhood developmental phases.

neuroscience↗

bioRxiv2026

A glucocorticoid-responsive polygenic signature in the anterior cingulate cortex moderates the association of early-life adversity and vulnerability for depression

Stress exposure is a major risk factor for psychopathology, yet how stress mediators shape long-term psychiatric vulnerability in humans remains unclear. Glucocorticoids, central effectors of the stress response, regulate gene expression through tissue-specific transcriptional programs, suggesting that glucocorticoid-responsive networks may shape sensitivity to adversity. Using RNA-sequencing following chronic glucocorticoid exposure in a non-human primate model, we identified a gene co-expression network specific to the anterior cingulate cortex (ACC) that was highly preserved across human post-mortem brain datasets relevant to depression. We derived an expression-based polygenic score (ePGS) reflecting genetic variation in network activity and tested its interaction with adversity in the UK Biobank. The ACC-specific glucocorticoid-responsive ePGS moderated the association between adversity and depressive symptoms in adult females, with the strongest effects for early-life adversity. Network genes were enriched for neurodevelopmental processes and showed stronger co-expression during childhood, highlighting a developmentally sensitive, region-specific mechanism linking stress exposure to depression risk.

neuroscience↗

bioRxiv2024

A meta-analysis of the effects of early life stress on the prefrontal cortex transcriptome suggests long-term effects on myelin

BackgroundEarly life stress (ELS) refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. ELS can permanently alter the brain, leading to cognitive impairment, increased sensitivity to future stressors, and mental health risks. The prefrontal cortex (PFC) is a key brain region implicated in the effects of ELS. MethodsTo better understand the effects of ELS on the PFC, we ran a meta-analysis of publicly available transcriptional profiling datasets. We identified five datasets (GSE89692, GSE116416, GSE14720, GSE153043, GSE124387) that characterized the long-term effects of multi-day postnatal ELS paradigms (maternal separation, limited nesting/bedding) in male and female laboratory rodents (rats, mice). The outcome variable was gene expression in the PFC later in adulthood as measured by microarray or RNA-Seq. To conduct the meta-analysis, preprocessed gene expression data were extracted from the Gemma database. Following quality control, the final sample size was n=89: n=42 controls & n=47 ELS: GSE116416 n=23 (no outliers); GSE116416 n=44 (2 outliers); GSE14720 n=7 (no outliers); GSE153043 n=9 (1 outlier), and GSE124387 n=6 (no outliers). Differential expression was calculated using the limma pipeline followed by an empirical Bayes correction. For each gene, a random effects meta-analysis model was then fit to the ELS vs. Control effect sizes (Log2 Fold Changes) from each study. ResultsOur meta-analysis yielded stable estimates for 11,885 genes, identifying five genes with differential expression following ELS (false discovery rate< 0.05): transforming growth factor alpha (Tgfa), IQ motif containing GTPase activating protein 3 (Iqgap3), collagen, type XI, alpha 1 (Col11a1), claudin 11 (Cldn11) and myelin associated glycoprotein (Mag), all of which were downregulated. Broadly, gene sets associated with oligodendrocyte differentiation, myelination, and brain development were downregulated following ELS. In contrast, genes previously shown to be upregulated in Major Depressive Disorder patients were upregulated following ELS. ConclusionThese findings suggest that ELS during critical periods of development may produce long-term effects on the efficiency of transmission in the PFC and drive changes in gene expression similar to those underlying depression. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/624315v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1619926org.highwire.dtl.DTLVardef@8da510org.highwire.dtl.DTLVardef@14feffcorg.highwire.dtl.DTLVardef@114aae3_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIEarly life stress (ELS) can have long-term effects on the prefrontal cortex (PFC) and its related cognitive and emotional functions. C_LIO_LITo elucidate these long-term effects, we conducted a meta-analysis of five publicly available PFC transcriptional profiling datasets from adult rodents that had previously experienced ELS. C_LIO_LIThis meta-analysis revealed a consistent downregulation of myelin-related genes in the PFC following ELS, and an upregulation of genes related to Major Depressive Disorder. C_LI Plain Language SummaryEarly life stress refers to exposure to negative childhood experiences, such as neglect, disaster, and physical, mental, or emotional abuse. Early life stress can permanently alter the brain, including the prefrontal cortex, which can lead to cognitive and emotional dysfunction that lasts into adulthood. We performed a meta-analysis using five public datasets to identify consistent long-term effects of early life stress on gene expression (mRNA) in the prefrontal cortex of adult rodents. In these studies, rodents that had experienced early life stress consistently showed a decreased amount of mRNA for genes related to myelin. Myelin is the fatty layer that insulates the axons of neurons, allowing them to transmit electrical signals more efficiently. These gene expression changes may suggest long-term effects of early life stress on the efficiency of prefrontal neurotransmission, disrupting cognitive and emotional processing.

neuroscience↗

bioRxiv2024

Resting-state fMRI reveals altered functional connectivity associated with resilience and susceptibility to chronic social defeat stress in mouse brain

Chronic stress is a causal antecedent condition for major depressive disorder and associates with altered patterns of neural connectivity. There are nevertheless important individual differences in susceptibility to chronic stress. How stress-induced alterations in functional connectivity amongst depression-related brain regions associates with resilience and susceptibility to chronic stress is largely unknown. We used resting-state functional magnetic resonance imaging (rs-fMRI) to examine functional connectivity between established depression-related regions in susceptible (SUS) and resilient (RES) adult mice following chronic social defeat stress (CSDS). Seed-seed FC analysis revealed that the ventral dentate gyrus (vDG) exhibited the greatest number of group differences in functional connectivity with targeted brain regions. SUS mice showed greater functional connectivity between the vDG and subcortical regions compared to both control (CON) or RES groups. Whole brain vDG seed-voxel analysis supported seed-seed findings in SUS mice and indicated significantly decreased connectivity between the vDG and anterior cingulate area compared to CON mice. Interestingly, RES mice exhibited enhanced connectivity between the vDG and anterior cingulate area compared to SUS mice. Moreover, RES mice showed greater connectivity between the infralimbic prefrontal cortex and the nucleus accumbens shell. These findings indicate unique differences in functional connectivity patterns in SUS and RES mice that could represent a neurobiological basis for vulnerability for stress-induced depression.

neuroscience↗

bioRxiv2024

Expression based polygenic scores - A gene network perspective to capture individual differences in biological processes.

Incorporating functional aspects into polygenic scores may accelerate early diagnosis and the discovery of therapeutic targets. Yet, existing polygenic scores summarize information from genome wide statistical associations between SNPs and phenotypes. We developed the novel biologically informed, expression-based polygenic scores (ePRS or ePGS). The method characterizes tissue specific gene co-expression networks from genome-wide RNA sequencing data and incorporates this information into polygenic scores. Performance and characteristics of the ePGS were compared to traditional polygenic risk score (PRS). We observed that ePGS differs from PRS for aggregating information on; i. the relation between different genes (co-expression); ii. the levels of tissue-specific gene expression; iii. the genetic variation of the target sample; iv. the tissue-specific effect size of the association between genotyping and gene expression; v. the portability across different ancestries. Variations in the ePGS represent individual variations in the expression of a tissue-specific gene co-expression network, and this methodology may profoundly influence the way we study human disease biology.

bioinformatics↗

bioRxiv2023

Investigating the validity and interpretability of longitudinal brain age underlying cognition in Asian children and older adults

Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55 - 88 years old) and children (4 - 11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.

neuroscience↗

bioRxiv2021

Genomic Modules and Intramodular Network Convergency of Susceptibility and Resilience in Multimodeled Stress in Male Mice

The multifactorial etiology of stress-related disorders is a challenge in developing synchronized medical standards for treatment and diagnosis. It is largely unknown whether there exists molecular convergence in preclinical models of stress generated using disparate construct validity. Using RNA-sequencing (RNA-seq), we investigated the genomic signatures in the ventral hippocampus, which mostly regulates affective behavior, in mouse models that recapitulate the hallmarks of anxiety and depression. Chronic oral corticosterone (CORT), a model that causes a blunted endocrine response to stress, induced anxiety- and depression-like behavior in wildtype mice and mice heterozygous for the gene coding for brain-derived neurotrophic factor (BDNF) Val66Met, a variant associated with genetic susceptibility to stress. In a separate set of mice, chronic social defeat stress led to a susceptible or a resilient population, whose proportion was dependent on housing conditions, standard housing or enriched environment. A rank-rank-hypergeometric (RRHO) analysis of the RNA-seq data set across models demonstrated that in mice treated with CORT and susceptible mice raised in standard housing differentially expressed genes (DEGs) converged toward gene networks involved in similar biological functions. Weighted gene co-expression analysis generated 54 unique modules of interconnected gene hubs, two of which included a combination of all experimental groups and were significantly enriched in DEGs, whose function was consistent with that predicted in the RRHO GO analysis. This multimodel approach showed transcriptional synchrony between models of stress with hormonal, environmental or genetic construct validity shedding light on common genomic drivers that embody the multifaceted nature of stress-related disorders.

neuroscience↗

bioRxiv2019

Internalizing Symptoms Associate with the Pace of Epigenetic Aging in Childhood

Childhood psychiatric symptoms may be associated with advanced biological aging. This study examined whether epigenetic age acceleration (EAA) associates with internalizing and externalizing symptoms across childhood in a longitudinal cohort study. At age 6 buccal epithelial cells from 148 children (69 girls) were collected to survey genome-wide DNA methylation. EAA was estimated using the Horvath clock. Internalizing symptoms at ages 2.5 and 4 years significantly predicted higher EAA at age 6, which in turn was significantly associated with internalizing symptoms from ages 6 to 10 years. Similar trends for externalizing symptoms did not reach significance. These findings indicate advanced biological aging in relation to child mental health and may help better identify those at risk for lasting impairments associated with internalizing disorders.

genetics↗

bioRxiv2019

Epigenetic signatures of chronic social stress in stress-susceptible animals

Exposure of mice to chronic social defeat stress (CSDS) produces depressive- and anxiety-like behaviors and widespread transcriptomic changes in several brain regions in susceptible animals. Here we present the first study of genome-wide cytosine methylation patterns of mice susceptible to CSDS using whole-genome bisulfite sequencing on DNA from the nucleus accumbens, a critical region for CSDS effects on behavior. We found extensive evidence for differential methylation following exposure to CSDS in susceptible animals, with a greater proportion of CG hypermethylation than hypomethylation in CSDS-susceptible mice compared to non-stressed controls; non-CG methylation shows the opposite trend. Several genes previously implicated in the effects of CSDS are among those with the greatest number of differentially methylated sites, including estrogen receptor alpha (Esr1), the deleted in colorectal cancer (Dcc) gene and Cacna1c, which has been associated with a range of psychiatric conditions. Informatic analysis of DM sites revealed a gene network with {beta}-catenin as the hub gene of a network that included the {beta}-catenin-related WNT/frizzled signaling pathway as well as both Esr1 and Dcc. Finally, we found considerable overlap between DM genes associated with CSDS in susceptible animals and those associated with human neuroticism in a genome-wide association study. Analysis of these overlapping genes revealed WNT signaling as the top pathway, which features {beta}-catenin as the primary hub gene. These findings reveal a striking convergence between the molecular pathways identified through either transcriptional or epigenomic analyses of the mouse model of susceptibility to chronic stress and the genomic architecture of increased stress susceptibility reflected in neuroticism in humans.

neuroscience↗

bioRxiv2018

Variably methylated regions in the newborn epigenome: environmental, genetic and combined influences

BackgroundEpigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. We examined the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs), defined as consecutive CpGs showing the highest variability of DNAm in 4 independent cohorts (PREDO, DCHS, UCI, MoBa, N=2,934).\n\nResultsWe used Akaikes information criterion to test which factors best explained variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E) including maternal demographic, psychosocial and metabolism related phenotypes, genotypes in cis (G), or their additive (G+E) or interaction (GxE) effects. G+E and GxE models consistently best explained variability in DNAm of VMRs across the cohorts, with G explaining the remaining sites best. VMRs best explained by G, GxE or G+E, as well as their associated functional genetic variants (predicted using deep learning algorithms), were located in distinct genomic regions, with different enrichments for transcription and enhancer marks. Genetic variants of not only G and G+E models, but also of variants in GxE models were significantly enriched in genome wide association studies (GWAS) for complex disorders.\n\nConclusionGenetic and environmental factors in combination best explain DNAm at VMRs. The CpGs best explained by G, G+E or GxE are functionally distinct. The enrichment of GxE variants in GWAS for complex disorders supports their importance for disease risk.

genetics↗

bioRxiv2018

A novel, biologically-informed polygenic score reveals role of mesocorticolimbic insulin receptor gene network on impulsivity and addiction

ImportanceActivation of brain insulin receptors occurs on mesocorticolimbic regions, modulating reward sensitivity and inhibitory control. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related psychopathologies (attention-deficit hyperactivity disorder, addiction), an idea that agrees with the high comorbidity between insulin resistant states and psychiatric conditions. While genetic studies comprise an interesting tool to explore neurobiological mechanisms in community samples, the conventional genome-wide association studies and polygenic risk score methodologies completely ignore the fact that genes operate in networks, and code for precise biological functions in specific tissues.\n\nObjectiveWe propose a novel, biologically informed genetic score reflecting the mesocorticolimbic insulin receptor-related gene network, and investigate if it predicts dopamine-related psychopathology (impulsivity and addiction) in community samples.\n\nDesignBirth cohort (Maternal Adversity, Vulnerability and Neurodevelopment, MAVAN) and adult cohort (Study of Addiction, Genes and Environment, SAGE).\n\nSettingGeneral community.\n\nParticipants212 4-year-old children (MAVAN), and 1626 adults (SAGE).\n\nExposureThe biologically informed, mesocorticolimbic specific, insulin receptor polygenic score was created based on levels of co-expression with the insulin receptor in striatum and prefrontal cortex, and calculated in the two samples using the genotype data (Psychip/Psycharray).\n\nMain outcomechildhood impulsivity in the Information Sampling task, and risk for early addiction onset.\n\nResultsThe insulin receptor polygenic score showed improved prediction of childhood impulsivity in boys and risk for early addiction onset in males in comparison to conventional polygenic risk scores for attention-deficit hyperactivity disorder or addiction.\n\nConclusions and relevanceThis novel genomic approach reveals insulin action as a relevant biological process involved in the risk for dopamine-related psychopathology.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSConsidering the modulation of mesocorticolimbic dopaminergic pathways by insulin through the action on its receptors (IR), we investigated if a novel, region specific polygenic score on the IR-related gene network (ePRS-IR) is associated with dopamine-related behaviors (impulsivity and addiction).\n\nFindingsThe ePRS-IR showed improved prediction of childhood impulsivity and risk for early addiction onset in comparison to conventional polygenic risk scores for ADHD or addiction.\n\nMeaningThis novel genomic approach reveals insulin action as a biological process involved in the risk for dopamine-related psychopathology.

neuroscience↗

bioRxiv2017

PRS-on-Spark: a novel, efficient and flexible approach for generating polygenic risk scores

MotivationPolygenic risk scores describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of the variance than single nucleotide polymorphisms alone. However, there is little consensus on the optimal data input for generating polygenic risk scores and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs.\n\nResultsWe developed PRS-on-Spark (PRSoS) a polygenic risk score software implemented in Apache Spark and Python that accommodates a variety of data input (e.g., observed genotypes, imputed genotypes, or imputed posterior probabilities) and strand-ambiguous SNPs. We show that PRSoS is flexible and efficient and computes polygenic risk scores at a range of p-value thresholds more quickly than existing software (PRSice). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increases the proportion of variance explained by polygenic risk scores for major depression.\n\nAvailability and ImplementationPRSoS is written in Apache Spark and Python and is freely available (see https://github.com/MeaneyLab/PRSoS).

bioinformatics↗

bioRxiv2017

Gene-expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ

RNA sequencing (RNA-seq) is a sensitive and accurate method for quantifying gene expression. Small samples or those whose RNA is degraded, such as formalin-fixed, paraffin-embedded (FFPE) tissue, remain challenging to study with nonspecialized RNA-seq protocols. Here we present a new method, Smart-3SEQ, that accurately quantifies transcript abundance even with small amounts of total RNA and effectively characterizes small samples extracted by laser-capture microdissection (LCM) from FFPE tissue. We also obtain distinct biological profiles from FFPE single cells, which have been impossible to study with previous RNA-seq protocols, and we use these data to identify possible new macrophage phenotypes associated with the tumor microenvironment. We propose Smart-3SEQ as a highly cost-effective method to enable large gene-expression profiling experiments unconstrained by sample size and tissue availability. In particular, Smart-3SEQs compatibility with FFPE tissue unlocks an enormous number of archived clinical samples, and combined with LCM it allows unprecedented studies of small cell populations and single cells isolated by their in situ context.

genomics↗

bioRxiv2017

BECon: A tool for interpreting DNA methylation findings from blood in the context of brain

Tissue differences are one of the largest contributors to variability in the human DNA methy-lome. Despite the tissue specific nature of DNA methylation, the inaccessibility of human brain samples necessitates the frequent use of surrogate tissues such as blood, in studies of associations between DNA methylation and brain function and health. Results from studies of surrogate tissues in humans are difficult to interpret in this context, as the connection between blood-brain DNA methylation is tenuous and not well documented. Here we aimed to provide a resource to the community to aid interpretation of blood based DNA methylation results in the context of brain tissue. We used paired samples from 16 individuals from three brain regions and whole blood, run on the Illumina 450K Human Methylation Array to quantify the concordance of DNA methylation between tissues. From these data we have made available metrics on: the variability of CpGs in our blood and brain samples, the concordance of CpGs between blood and brain, and estimations of how strongly a CpG is affected by cell composition in both blood and brain through the web application BECon (Blood-Brain Epigenetic Concordance; https://redgar598.shinyapps.io/BECon/). We anticipate that BECon will enable biological interpretation of blood based human DNA methylation results, in the context of brain.

genetics↗