Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies... Show moreBackground: 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. Show less
Monitoring the illegal trade of wool fibres of wild vicun~a (Vicugna vicugna) and guanaco (Lama guanicoe) is highly desirable. The high market value of fleece from these camelid species poses a... Show moreMonitoring the illegal trade of wool fibres of wild vicun~a (Vicugna vicugna) and guanaco (Lama guanicoe) is highly desirable. The high market value of fleece from these camelid species poses a threat to their wild populations. A previous study showed that direct analysis in real time time-of-flight mass spectrometry (DART-TOFMS) effectively identifies wool fibres to species. Producing high-resolution data in a short period of time makes DART-TOFMS a reliable identification tool, even though data analysis can still be improved. The present study proposes a novel data analysing pipeline based on Convolutional Neural Networks (CNN), applicable to any kind of DART-TOF MS data. We tested our proposed method on keratin fibres of four camelid species (Vicugna vicugna: n 1⁄4 19; Vicugna pacos: n 1⁄4 20; Lama guanicoe: n 1⁄4 20, and Lama glama: n 1⁄4 20). Analyses showed that selecting 512 ions with the highest relative intensity provides the best resolution and yields 100% accuracy for species identification. Show less
