Biology subjects

Morey, R. A.

Publications and source records attributed to Morey, R. A..

8 recordsLinked to original sources

bioRxiv2026

Dissociable Neural Responses to Conditioned Social Threats are Modulated by Spatial Proximity in PTSD and MDD

The brains response to potential threats is shaped by the egocentric spatiotemporal distance to the threat. While distal threats recruit evaluative, cognitive-fear networks for strategic planning, proximal threats engage evolutionarily conserved reactive-fear circuitry to mount immediate defensive responses. Understanding this dual neural architecture is clinically relevant, as the spatial proximity to a traumatic event predicts subsequent psychiatric symptom severity and recovery. Despite the well-characterized disruptions to threat circuitry in posttraumatic stress disorder (PTSD) and major depressive disorder (MDD), how these networks respond to threats at distinct spatial distances has yet to be investigated in these disorders. Utilizing 3D virtual reality technology, we implemented a spatially-modulated fear conditioning paradigm by presenting human avatars at proximal (peripersonal) and distal (extrapersonal) distances, paired with shock, to trauma-exposed participants (n = 50) during functional MRI (fMRI). We modeled associations between PTSD and MDD symptom severity and task-evoked hemodynamic responses within cognitive-fear and reactive-fear threat networks. Our results support a transdiagnostic disruption of thalamic responses to proximal threats, driven by heightened activation to the safety stimulus and stronger deactivation to the threat stimulus. MDD showed a disorder-specific effect of disrupted activity across cognitive-fear and social cognition regions in response to proximal social threats, and illness severity-by-proximity effects across motor regions. PTSD symptom severity was uniquely associated with hyperactivity of the amygdala to distal threats. Transdiagnostic disruption of connectivity between the dorsal precuneus and several subcortical structures followed a disorder-specific gradient across the anterior-posterior axis. While thalamic disruptions to threats represent a shared transdiagnostic effect, PTSD and MDD are distinguished by differences in amygdala hyperactivation and cognitive-fear network hypoactivation, respectively.

neuroscience↗

bioRxiv2022

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 anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 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. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p<5x10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks. HIGHLIGHTSO_LIGenomic SEM can examine genetic correlation across cortical regions. C_LIO_LIWe inferred regional genetic networks of cortical thickness and surface area. C_LIO_LINetwork-associated variants have been implicated in multiple traits. C_LIO_LIThese networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence. C_LI

genomics↗

bioRxiv2021

Alcohol Use Disrupts Age-Appropriate Cortical Thinning in Adolescence: A Data Driven Approach

ObjectiveCortical thickness changes dramatically during development and is influenced by adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying heterogeneity from the effects of alcohol. MethodsAdolescents (n=657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) who endorsed little to no alcohol use at baseline were assessed with structural MRI and followed longitudinally at four yearly intervals. Seven unique spatially covarying patterns of cortical thickness were obtained from the baseline scans by applying a novel data-driven method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. ResultsIn most NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts, among moderate drinkers the decline was faster in the younger cohort and slower in the older cohort, among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort (FDR corrected p-values < 0.01). ConclusionsThe NMF method can delineate spatially coordinated patterns of cortical thickness at the vertex level that are unconstrained by anatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers.

neuroscience↗

bioRxiv2021

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized AnaLysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

neuroscience↗

bioRxiv2018

Economic status mediates the relationship between educational attainment and posttraumatic stress disorder: a multivariable Mendelian randomization study

ObjectivesTo investigate the genetic overlap and causal relationship between posttraumatic stress disorder (PTSD) and traits related to educational attainment.\n\nDesignGenetic correlation, polygenic risk scoring, and causal inference via multivariable Mendelian randomization (MR).\n\nSettingPsychiatric Genomics Consortium for PTSD, UK Biobank, 23andMe, and Social Science Genetic Association Consortium.\n\nParticipants23,185 PTSD cases and 151,309 controls; up to 1,131,881 individuals assessed for educational attainment and related traits.\n\nMain outcome measuresGenetic correlation obtained from linkage disequilibrium score regression, phenotypic variance explained by polygenic risk scores, and casual effects (beta values) estimated with MR\n\nResultsPTSD showed strong negative genetic correlations with educational attainment (EdAtt; rg=-0.26, p=4.6x10-8). PRS based on genome-wide significant variants associated with EdAtt significantly predicted PTSD (p=6.16x10-4), but PRS based on variants associated with PTSD did not predict EdAtt (p>0.05). MR analysis indicated that EdAtt has negative causal effects on PTSD (beta=-0.23, p=0.004). Investigating potential mediators of the EdAtt-PTSD relationship, we observed that propensity for trauma exposure and risk-taking behaviors are risk factors for PTSD independently from EdAtt (beta = 0.36, p = 2.57x10-5 and beta = 0.76, p = 6.75x10-4, respectively), while income fully mediates the causal effect of EdAtt on PSTD (MR: Income - beta = -0.18, p =0.001; EdAtt - beta =-0.23, p=0.004; multivariable MR: Income - beta = -0.32, p = 0.017; EdAtt - beta = -0.04, p = 0.786).\n\nConclusionsWe report novel findings based on large-scale datasets regarding the relationship between educational attainment and PTSD, supporting the role of economic status as the key mediator in the causal relationship observed.\n\nWhat is already known on this topicThere is a well-established negative association of educational attainment and other traits related to cognitive ability with posttraumatic stress disorders (PTSD). However, the findings of these previous studies support various possible causal explanations: 1) individuals with high educational attainment are more resilient with respect to developing PTSD, 2) PTSD negatively impacts cognitive ability, or 3) PTSD and educational attainment share some underlying determinants, including relevant molecular mechanisms.\n\nA key obstacle to disentangling the complex association between educational attainment and PTSD is reverse causation, i.e. the situation in which the outcome precedes and causes the exposure instead of the other way around.\n\nWhat this study addsWe conducted a causal-inference investigation based on large-scale information from the investigation of more than one million individuals. Our main assumption is that genetic information can strongly minimize the bias of reverse causation, because genetic variants are determined at conception and do not change throughout life.\n\nOur findings indicate 1) the effect of traits related to educational attainment on PTSD, 2) no reverse effect of PTSD on educational attainment, and 3) economic status mediates the relationship between educational attainment and PTSD, independently from the brain mechanisms related to educational attainment.

genetics↗

bioRxiv2018

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Background and PurposePosttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a wide range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here we used a data-driven method to identify shared and unique neuroimaging markers of ELS and PTSD in 97 military veterans. We hypothesized that clustering standard regions of interest would improve classification accuracy of PTSD and ELS, relative to using individual atlas-defined brain regions, with moderate overlap in the patterns associated with each condition.\n\nMethodsWe used a novel machine learning method - evolving partitions to improve classification (EPIC) - to determine how combinations of cortical thickness, surface area, and subcortical brain volumes, could contribute to classification of veterans with PTSD (n=40) relative to trauma-exposed controls (n=57), and classification of ELS within the PTSD (ELS+ n=16; ELS- n=24) and control groups (ELS+ n=16; ELS- n=41). Additional inputs included intracranial volume, age, sex, and self-reported depression.\n\nResultsEPIC improved classification of PTSD, with 71% accuracy, on average. Within groups, EPIC classified ELS with 70% accuracy in controls on average, and 67% accuracy in PTSD; accuracy ranged from 75-84% for the best run of each analysis. For each binary analysis, EPIC identified unique predictors and combinations of neuroimaging variables that contributed to improve group classification from the classifier that modeled input features individually. Self-reported depression was a stronger predictor of PTSD and ELS than any combination of neuroimaging variables, along with adult trauma exposure in controls. Temporoparietal metrics were the strongest neuroimaging predictors of PTSD after depression, whereas regions of the cingulate cortex were strong markers of ELS in both subgroups.\n\nConclusionsCombinatorial machine learning methods such as EPIC may boost power to delineate underlying structural signatures of stress-related neuropathology.

neuroscience↗

bioRxiv2018

Genome wide association study of hippocampal subfield volume in PTSD cases and trauma-exposed controls

Behavioral, structural, and functional neuroimaging have implicated the hippocampus as a critical brain region in PTSD pathogenesis. We conducted a GWAS of hippocampal subfield volumes in a sample of recent military veteran trauma survivors (n=157), including some with PTSD (n=66). Covariates in our analysis included lifetime PTSD diagnosis, sex, intracranial volume, genomic estimates of ancestry, and childhood trauma. Interactions between genetic variants and lifetime PTSD or childhood trauma were interrogated for SNPs with significant main effects. Several genetic associations surpassed correction for multiple testing for several hippocampal subfields, including fimbria, subiculum, cornu ammonis-1(CA1), and hippocampal amygdala transition area (HATA). One association replicated in an independent cohort of civilians with PTSD (rs12880795 in TUNAR with L-HATA volume, p=3.43 x 10-7 in the discovery and p=0.0004 in the replication cohort). However, the most significant association in the discovery data set was between rs6906714 in LINC02571 and R-fimbria volume (p=5.99 x10-8, q=0.0056). Interestingly, the effect of rs6906714 on R-fimbria volume increased with childhood trauma (G*E interaction p=0.022). In addition to variants in long intergenic non-coding RNAs (lincRNAs), we identified SNPs associated with hippocampal subfield volume, which are also quantitative trait loci (QTLs) for genes involved in RNA editing of glutamate receptor subunits (GluRs), oxidative stress, and autoimmune disorders. Genomic regions, some with putative regulatory roles, influence the volume of hippocampal subfields. Neuroimaging phenotypes may offer important insight into the genetic architecture and neurobiological pathways relevant to PTSD, as well as in the identification of potential biomarkers and drug targets for PTSD.

neuroscience↗

bioRxiv2018

Smaller Hippocampal CA-1 Subfield Volume in Posttraumatic Stress Disorder

BackgroundSmaller hippocampal volume in patients with PTSD represents the most consistently reported structural alteration in the brain. Subfields of the hippocampus play distinct roles in encoding and processing of memories, which are disrupted in PTSD. We examined PTSD-associated alterations in 12 hippocampal subfields in relation to global hippocampal shape, and clinical features.\n\nMethodsCase-control cross-sectional study of US military veterans (n=282) from the Iraq and Afghanistan era were grouped into PTSD (n=142) and trauma-exposed controls (n=140). Participants underwent clinical evaluation for PTSD and associated clinical parameters followed by MRI at 3-Tesla. Segmentation with Free Surfer v6.0 produced hippocampal subfield volumes for the left and right CA1, CA3, CA4, DG, fimbria, fissure, hippocampus-amygdala transition area, molecular layer, parasubiculum, presubiculum, subiculum, and tail, as well as hippocampal meshes. Covariates included age, gender, trauma exposure, alcohol use, depressive symptoms, antidepressant medication use, total hippocampal volume, and MRI scanner model.\n\nResultsSignificantly lower subfield volumes were associated with PTSD in left CA1 (p=.01; d=.21; uncorrected), CA3 (p=.04; d=.08; uncorrected), and right CA3 (p=.02; d=.07; uncorrected) only if ipsilateral whole hippocampal volume was included as a covariate. A trend level association of L-CA1 with PTSD [F4, 221=3.32, p = 0.07] is present and the other subfield findings are non-significant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD associated differences in global hippocampal shape were non-significant.\n\nConclusionsThe present finding of smaller hippocampal CA1 in PTSD is consistent with model systems in rodents that exhibit increased anxiety-like behavior from repeated exposure to acute stress. Behavioral correlations with hippocampal subfield volume differences in PTSD will elucidate their relevance to PTSD, particularly behaviors of associative fear learning, extinction training, and formation of false memories.

neuroscience↗