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

Supplementary Material for: Human Cardiovascular Disease IBC Chip-Wide Association with Weight Loss and Weight Regain in the Look AHEAD Trial

<b><i>Background/Aims:</i></b> The present study identified genetic predictors of weight change during behavioral weight loss treatment. <b><i>Methods:</i></b> Participants were 3,899 overweight/obese individuals with type 2 diabetes from Look AHEAD, a randomized controlled trial to determine the effects of intensive lifestyle intervention (ILI), including weight loss and physical activity, relative to diabetes support and education, on cardiovascular outcomes. Analyses focused on associations of single nucleotide polymorphisms (SNPs) on the Illumina CARe iSelect (IBC) chip (minor allele frequency >5%; n = 31,959) with weight change at year 1 and year 4, and weight regain at year 4, among individuals who lost ≥3% at year 1. <b><i>Results:</i></b> Two novel regions of significant chip-wide association with year-1 weight loss in ILI were identified (p < 2.96E-06). <i>ABCB11 </i>rs484066 was associated with 1.16 kg higher weight per minor allele at year 1, whereas <i>TNFRSF11A,</i> or<i> RANK,</i> rs17069904 was associated with 1.70 kg lower weight per allele at year 1. <b><i>Conclusions:</i></b> This study, the largest to date on genetic predictors of weight loss and regain, indicates that SNPs within <i>ABCB11</i>, related to bile salt transfer, and <i>TNFRSF11A</i>, implicated in adipose tissue physiology, predict the magnitude of weight loss during behavioral intervention. These results provide new insights into potential biological mechanisms and may ultimately inform weight loss treatment

Genetic predisposition to weight loss and regain with lifestyle intervention: Analyses from the Diabetes Prevention Program and the Look AHEAD randomized controlled trials.

We investigated the genetic overlap between Alzheimer&rsquo;s disease (AD) and Parkinson&rsquo;s disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 &times; 10&minus;7). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer&rsquo;s neurodegeneration