Seminar Series

Towards Computational Psychiatry: Combining Neuroimaging, Pharmacology and Modeling to Understand Schizophrenia Neurobiology

Neuropsychiatric disorders alter the structure and function of neural circuits and distributed neural networks, which leads to profound behavioral disability. Non-invasive neuroimaging tools have matured to reliably detect neural systems-level disturbances in neuropsychiatric disorders. However, mechanistic mapping from neural circuit pathology to abnormal behavior remains out of reach for most psychiatric conditions. This seminar focuses on emerging the emerging field of 'computational psychiatry' from the perspective of findings in schizophrenia. This presentation will first discuss the application of computational microcircuit models to understand cognitive disturbances in schizophrenia. In turn, the focus is placed on pharmacological neuroimaging as a powerful causal tool to probe neural circuit perturbations. Specific recent neuroimaging studies are discussed that use the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Next, the presentation will discuss resting-state neuroimaging advances, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. Emerging findings in schizophrenia suggest that disruptions in sensory-thalamic-prefrontal networks may hold promise as a marker for treatment effects in future clinical studies and might constitute a final common pathway of neural system disturbances in schizophrenia. However, such neuroimaging markers do not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle to the effort to translate findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. Here the use of biophysical computational modeling, extended to large-scale neural system simulations, has proven particularly powerful to draw inferences about neural circuit alterations that may be 'driving' the resting-state dysconnectivity profiles in schizophrenia. In summary, the argument is presented that linking experimental studies in humans with computational models and pharmacological probes will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.