The study of translational biomarkers in brain disorders is a very challenging and fruitful approach, which will empower a better understanding of healthy and diseased brains. This project will promote the translation of advanced engineering solutions and mathematic tools to novel neuroimaging applications in psychiatric disorders including major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), allowing sophisticated and powerful analyses on highly complex datasets. To date, the unifying syndrome classification (ICD-9/10;DSM-IV/5) for these mental disorders obscures our knowledge of underlying pathophysiology and cannot guide optimal treatments. For example, there is no biomarker that is able to precisely predict response of MDD to some treatments. One reason for this is that most neuroimaging prediction studies to date have used a single imaging measure or reported simple correlation relationships, without considering multimodal cross- information, nonlinear relationships, or multi-site cross-validation. Hence, developing novel data mining techniques such as deep learning, fusion with reference, and sparse regression can complement and exploit the richness of neuroimaging data, providing promising avenues to identify objective biomarkers and going beyond a descriptive use of brain imaging as traditionally used in studies of brain disease to individualized prediction. We will facilitate the translational biomarker identification by developing 3 novel data-driven methods: 1) A supervised fusion model that can provide insight on how cognitive impairment may affect covarying brain function and structure in mental disorder, by using different clinical measures as a reference to guide multimodal MRI fusion; 2) A cutting-edge prediction framework with aggregated feature selection techniques that is able to estimate clinical outcome more precisely, e.g., remission/relapse status of individual MDD patient after electroconvulsive treatment(ECT) using baseline brain imaging and demographic measures of 3) We will draw on advances and ideas from deep learning combined with layer-wise relevance propagation (LRP) or attention modules, to classify multiple groups of psychiatric disorders by incorporating dynamic functional measures. The proposed (Deep/Recurrent/Convolutional Neural Network, DNN/RNN/CNN) models will have enhanced interpretability that is able to trace back and discover the most predictive functional networks from input. All above proposed methods will be applied to big data containing both multimodal imaging and behavioral information (n~5000) pooled from existing studies, and our developed open-source toolboxes will be shared publicly. This pioneering study may provide an urgently-needed paradigm shift in the treatment and diagnosis of psychiatric disorders, thereby guiding personalized clinical care. Accomplishment of this project has great potential to discover neuroimaging biomarkers that have been missed by existing approaches, leading to earlier and more effective interventions, and laying the groundwork for a significant translational impact.
Psychiatric imaging is struggling with identifying robust biomarkers. Existing approaches do not fully leverage the power of multimodal data, despite evidence that such information is highly informative. We will draw on advances and ideas from fields of deep learning, supervised learning and functional dynamics, to capture rich information from multimodal imaging big data, and to identify precise biomarkers that are able to predict clinical measures for new individuals and help for intervention. We will pool big data from ongoing projects in multiple cohorts, consisting of a big data with imaging and behavioral info to apply clinical applications that will have profound translational medicine impact on schizophrenia, bipolar disorder and major depressive disorders.
