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

Effects of mulch location on banana weevil, nutrients, soil water, and biomass in banana

Major constraints to banana (Musa spp., genome group AAA) production, a dietary staple for over 70 million people in sub-Saharan Africa, are pest infestations, poor nutrition and inadequate water. Although mulch can improve soil water and nutrient status, many farmers believe it also promotes the proliferation of banana weevil (Cosmopolites sordidus), one of the most serious banana pests. In this study, we evaluated the effects of mulch location (mulch to base of banana pseudostem; mulch recessed 1 m from the pseudostem) on banana weevil, soil and plant nutrient status, soil water, and banana growth and development. After 3 years, the fully mulched plots had significantly more soil Ca and Mg than plots that did not receive mulch. Banana foliar K concentration was significantly higher in both mulch treatments (full and recessed) than in the control (no mulch) plots. The mulched plots had greater recharge after rainfall events and higher soil water contents during dry periods due to increased infiltration in the mulched plots. However, the mulched plots also exhibited significantly higher banana weevil densities and greater plant damage than the control plots. There was no difference in weevil damage with mulch location, although weevil density was higher in the fully mulched plots throughout most of the trial. Despite greater weevil damage, the treatments that were mulched yielded significantly heavier bunches. Hence the effects of the mulch on soil water infiltration and banana foliar nutrient status outweighed the detrimental effects of banana weevil damage