Biology subjects

Galea, S.

Publications and source records attributed to Galea, S..

7 recordsLinked to original sources

bioRxiv2026

The Signal Generating (SiGn) fMRI Phantom

Functional magnetic resonance imaging (fMRI) quality assurance has traditionally relied on static, geometrically regular phantoms that cannot generate the dynamic signal changes fMRI analysis pipelines are designed to detect. Here we present the Signal Generating (SiGn) anthropomorphic brain phantom, a 3D-printed cortical model derived from an individual participant's structural MRI, filled with tissue contrast-mimicking agar gels and coupled to a hemin-based infusion system that produces controlled, time-varying $T_2^*$-weighted signal changes. We validated the phantom across two scanning sessions on a 3\,T Siemens MAGNETOM Vida scanner, demonstrating that hemin infusion produced spatially localised activation detectable by standard general linear model analyses. Because the phantom's geometry is derived from real participant anatomy, its functional data can be coregistered and spatially normalised to standard brain templates through the same pipeline applied to human data, enabling end-to-end assessment of how each preprocessing step affects a known ground-truth signal. To support adoption and reproducibility, we openly release the full resource, including 3D-printable STL model files, tissue-mimicking gel recipes, the BIDS-formatted dataset, preprocessing and analysis scripts, and a containerised reproducibility workflow; the corresponding archival container image is also deposited on Zenodo. This framework is intended to lower the barrier for other groups to fabricate, scan, and analyse an equivalent device on their own hardware, adapt it to specific research questions, and iteratively improve the design, thereby supporting more rigorous and transparent fMRI quality assurance practices across the neuroimaging community.

neuroscience↗

bioRxiv2020

Neighborhood environment, social cohesion, and epigenetic aging

Living in adverse neighborhood environments have been linked to increased risk of aging-related diseases and mortality; however, the biological mechanisms explaining this observation remain poorly understood. DNA methylation (DNAm), a proposed biomarker of biological aging responsive to environmental stressors, offers promising insight into molecular pathways. We examined associations of three measures of neighborhood conditions (poverty, quality, and social cohesion) with three different epigenetic clocks (Horvath, Hannum, and Levine) using data from the Detroit Neighborhood Health Study (n=158). Using linear regression models, we evaluated associations in the total sample and stratified by gender and social cohesion. Differential effects by gender were found between men and women. Neighborhood poverty was associated with PhenoAge acceleration among women, but not among men (women: {beta} = 1.4; 95% CI: -0.4, 3.3 vs. men: {beta} = -0.3; 95% CI: -2.2, 1.5) in fully adjusted models. In models stratified on social cohesion, association of neighborhood poverty and quality with accelerated DNAm aging remained elevated for residents living in neighborhoods with lower social cohesion, but were null for those living in neighborhoods with higher social cohesion. Our study suggests that living in adverse neighborhood conditions can speed up epigenetic aging, while positive neighborhood characteristics may buffer effects.

genomics↗

bioRxiv2020

Heterogeneous Mediation Analysis for Causal Inference

DNA methylation (DNAm) has been suggested to play a critical role in post-traumatic stress disorder (PTSD), through mediating the relationship between trauma and PTSD. However, this underlying mechanism of PTSD for African Americans still remains unknown. To fill this gap, in this paper, we investigate how DNAm mediates the effects of traumatic experiences on PTSD symptoms in the Detroit Neighborhood Health Study (DNHS) (2008-2013) which involves primarily African Americans adults. To achieve this, we develop a new mediation analysis approach for high-dimensional potential DNAm mediators. A key novelty of our method is that we consider heterogeneity in mediation effects across sub-populations. Specifically, mediators in different sub-populations could have opposite effects on the outcome, and thus could be difficult to identify under a traditional homogeneous model framework. In contrast, the proposed method can estimate heterogeneous mediation effects and identifies sub-populations in which individuals share similar effects. Simulation studies demonstrate that the proposed method outperforms existing methods for both homogeneous and heterogeneous data. We also present our mediation analysis results of a dataset with 125 participants and more than 450, 000 CpG sites from the DNHS study. The proposed method finds three sub-groups of subjects and identifies DNAm mediators corresponding to genes such as HSP90AA1 and NFATC1 which have been linked to PTSD symptoms in literature. Our finding could be useful in future finer-grained investigation of PTSD mechanism and in the development of new treatments for PTSD.

bioinformatics↗

bioRxiv2020

The Impact of Psychopathology, Social Adversity and Stress-relevant DNAm on Prospective Risk for Post-traumatic Stress: A Machine Learning Approach

BackgroundA range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD. MethodsWe used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS). ResultsML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness. ConclusionMultiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

bioinformatics↗

bioRxiv2019

Genomic Influences on Self-Reported Childhood Maltreatment

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to 1) identify genetic variation associated with reported childhood maltreatment, 2) calculate the relevant SNP-based heritability estimates, and 3) quantify the genetic overlap of reported childhood maltreatment with mental and physical health-related phenotypes. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n=124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder working group (PGC-PTSD) (n=26,290). Heritability estimations for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression (LDSR). Two genome-wide significant loci associated with childhood maltreatment, located on chromosomes 3p13 (rs142346759, beta=0.015, p=4.35x10-8, FOXP1) and 7q31.1 (rs10262462, beta=-0.016, p=3.24x10-8, FOXP2), were identified in the discovery dataset but were not replicated in the PGC-PTSD sample. SNP-based heritability for childhood maltreatment was estimated to be [~]6%. Childhood maltreatment was most significantly genetically correlated with depressive symptoms (rg=0.70, p=4.65x10-40). This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. FOXP genes could influence traits such as depression and thereby be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with various psychiatric disorders, may ultimately be useful in in developing targeted treatment and prevention strategies.

genomics↗

bioRxiv2019

Methylomic profiles reveal sex-specific shifts in leukocyte composition associated with post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a debilitating mental disorder precipitated by trauma exposure. However, only some persons exposed to trauma develop PTSD. There are sex differences in risk; twice as many women as men, develop a lifetime diagnosis of PTSD. Methylomic profiles derived from peripheral blood are well-suited for investigating PTSD because DNA methylation (DNAm) encodes individual response to trauma and may play a key role in the immune dysregulation characteristic of PTSD pathophysiology. In the current study, we leveraged recent methodological advances to investigate sex-specific shifts in DNAm-based leukocyte composition that are associated with lifetime PTSD. We estimated leukocyte composition on a combined methylation array dataset (483 participants, [~]450k CpG sites) consisting of two civilian cohorts, the Detroit Neighborhood Health Study and Grady Trauma Project. Sex-stratified Mann-Whitney U test and two-way ANCOVA revealed that lifetime PTSD was associated with a small but significant elevation in monocyte proportions in males, but not in females (Holm-adjusted p-val < 0.05). No difference in monocyte proportions was observed between current and remitted PTSD cases in males, suggesting that this sex-specific peak shift reflects a long-standing trait of lifetime history of PTSD, rather than current state of PTSD. Associations with lifetime PTSD or PTSD status were not observed in any other leukocyte subtype and our finding in monocytes was confirmed using cell estimates based on a different deconvolution algorithm, suggesting that our sex-specific findings are robust across cell estimation approaches. Overall, our main finding of elevated monocyte proportions in males, but not in females with lifetime history of PTSD provides evidence for a sex-specific shift in peripheral blood leukocyte composition that is detectable in methylomic profiles and that reflects long-standing changes associated with PTSD diagnosis.

genomics↗

bioRxiv2019

Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies novel methylation loci

Differences in susceptibility to posttraumatic stress disorder (PTSD) may be related to epigenetic differences between PTSD cases and trauma-exposed controls. Such epigenetic differences may provide insight into the biological processes underlying the disorder. Here we describe the results of the largest DNA methylation meta-analysis of PTSD to date with data from the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. Ten cohorts, military and civilian, contributed blood-derived DNA methylation data (HumanMethylation450 BeadChip) from 1,896 PTSD cases (42%) and trauma-exposed controls (58%). Utilizing a common QC and analysis strategy, we identified ten CpG sites associated with PTSD (9.61E-07<p<4.72E-11) after adjustment for multiple comparisons (FDR<.05). Several CpGs were located in genes previously implicated in PTSD and other psychiatric disorders. The top four CpG sites fell within the aryl-hydrocarbon receptor repressor (AHRR) locus and were associated with lower DNA methylation in PTSD cases relative to controls. Interestingly, this association appeared to uncorrelated with smoking status and was most pronounced in non-smokers with PTSD. Additional evaluation of metabolomics data supported our findings and revealed that AHRR methylation associated with kynurenine levels, which were lower among subjects with PTSD relative to controls. Overall, this study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

genomics↗