Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls 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. Results: 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 in each modality (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. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Functional and metabolic remodelling in GLUT4-deficient hearts confers hyper-responsiveness to substrate intervention

Impaired glucose uptake is associated with both cardiac hypertrophy and contractile dysfunction, but whether there are common underlying&nbsp; mechanisms linking these conditions is yet to be determined. Using a &lsquo;gene dose&rsquo; Cre-Lox GLUT4-deficient murine model, we examined the effect of suppressed glucose availability on global myocardial gene expression and glycolysis substrate bypass on the function of isolated perfused hearts. Performance of hearts from 22- to 60-week-old male GLUT4 knockout (KO, &gt; 95% reduction in GLUT4), GLUT4 knockdown (KD, 85% reduction in cardiac GLUT4) and C57Bl/6 wild-type (WT) controls was measured ex vivo in Langendorff mode perfusion. DNA microarray was used to profile mRNA expression differences between GLUT4-KO and GLUT4-KD hearts. At 22 weeks, GLUT4-KO hearts exhibited cardiac hypertrophy and impaired contractile function ex vivo, characterized by a 40% decrease in developed pressure. At 60 weeks, dysfunction was accentuated in GLUT4-KO hearts and evident in GLUT4-KD hearts. Exogenous pyruvate (5 mM) restored systolic pressure to a level equivalent to WT (GLUT4-KO, 176.8 &plusmn; 13.2 mmHg vs. WT, 146.4 &plusmn; 9.56 mmHg) in 22-week-old GLUT4-KO hearts but not in 60-week-old GLUT4-KO hearts. In GLUT4-KO, DNA microarray analysis detected downregulation of a number of genes centrally involved in mitochondrial oxidation and upregulation of other genes indicative of a shift to cytosolic &beta;-oxidation of long chain fatty acids. A direct link between cardiomyocyte GLUT4 deficiency, hypertrophy and contractile dysfunction is demonstrated. These data provide mechanistic insight into the myocardial metabolic adaptations associated with short and long-term insulin resistance and indicate a window of opportunity for substrate intervention and functional &lsquo;rescue&rsquo;.<br /