UC San Diego

Neuropsychiatric disorders, specifically mental disorders, generally lack a quantitative and biophysiological basis for diagnostics and treatment due to fundamental limitations in theoretical knowledge of the disorder and brain-mind duality. Due to the complex multiscale nature of the brain, large and multimodal datasets as well as biophysically-based simulations are required to elucidate its functioning. As such, computational methods exist to bridge these scales. Specifically, probabilistic graph models are utilized here to capture inherent uncertainty in the system while being computationally efficient and tractable to allow for scaling and combining biophysically and theoretical-based models with Big Data in order to model and diagnose known mental disorders. First, a theoretically-driven computationally efficient reaction-diffusion model of synaptic transmission using Markov models and eigenmode decomposition is presented to scale molecular simulations to neural networks with applications in pharmacological simulations, artificial neural networks, and neuromorphic engineering. The second part connects the network level to behavior using deep learning, graph models, and manifold learning applied to neuroimaging data in adolescent depression using a combination of theory- and data-driven techniques. In addition to creating scalable models, this work interrogates structural and functional biomarkers and creates a neuroimaging pipeline resulting in automatic disorder detection. Finally, the focus shifts to diagnostics of anxiety and depression using behavioral data in the form of natural language processing, making use of transformer deep learning architectures.