ORCID Profile
0000-0001-7578-4469
Current Organisations
University of Western Ontario
,
St. Joseph's Health Care London
,
Lawson Health Research Institute
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Publisher: American Medical Association (AMA)
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 17-02-2023
DOI: 10.1038/S41380-023-01991-7
Abstract: Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-in idual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR) (2) standardised mean differences (SMD) using Hedges g (3) modal distribution of in idual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p 0.001 glutamine: CVR = 0.15, p = 0.003 Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05 Glx: CVR = 0.25, p 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008 Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = −0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = −0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For in idual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate ( g = −0.15, p = 0.03), higher thalamic glutamine ( g = 0.53, p 0.001) and higher BG Glx in patients relative to controls ( g = 0.28, p 0.001). Proportion of males was negatively associated with MFC glutamate ( z = −0.02, p 0.001) and frontal white matter Glx ( z = −0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG ( z = 0.01, p = 0.01) and temporal lobe ( z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.
Publisher: Wiley
Date: 14-12-2021
DOI: 10.1002/BRB3.2413
Abstract: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Adult subjects ( N = 2229 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA‐PGC PTSD sites underwent T1‐weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel‐wise (brainageR) and region‐of‐interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects ( n = 386). Linear mixed effects models were conducted in the full s le (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD brain age − chronological age) controlling for chronological age, sex, and scan site. BrainageR most accurately predicted brain age in a subset ( n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68 PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37 BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three‐way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age‐related brain changes that are consonant with in iduals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.
Publisher: Cold Spring Harbor Laboratory
Date: 13-12-2022
DOI: 10.1101/2022.12.12.519838
Abstract: Current clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify in iduals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. We analyzed brain MRI data from 3,527 structural-MRI 2,502 resting state-fMRI and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish in iduals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Our findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. ⍰ Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC). ⍰ Using deep learning for feature reduction (denoising variational auto-encoder DVAE) dramatically reduced the number of features with no concomitant performance degradation. ⍰ Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks
Publisher: Elsevier BV
Date: 12-2023
Publisher: Springer Science and Business Media LLC
Date: 19-12-2019
Location: United States of America
No related grants have been discovered for Jean Theberge.