Only time will tell: a computational psychiatry approach to model temporal transitions in brain activity as a lens towards developing better diagnostic nosology for psychiatric illness

Stanford University, Stanford CA

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Abstract

Project Summary/Abstract Given the widespread prevalence of mental illness, there is a crucial need for finding ways to prevent and treat mental illness. Despite the accelerated pace of discovery in neuroscience, the pace of development for treatments of mental illness has not only been slow but almost stagnated. This slow pace could be mainly attributed to the lack of accurate and neurobiologically grounded diagnostic nosology (or a disease classification system). Unfortunately, the diagnostic nosology as currently used in psychiatry (i.e., Diagnostic and Statistical Manual of Mental Disorders; DSM-V) is built entirely upon symptoms and not biology. Thus, although the diagnosis made using DSM-V is highly reliable, it is not necessarily (biologically) valid. The grounding of a diagnosis in biological features can not only provide a reliable and accurate diagnosis but can also provide specific biomarkers to track the illness and test the efficacy of new treatments. With the advent of modern noninvasive neuroimaging modalities, sophisticated methods have been developed to examine both structural and functional activity/connectivity of the brain for characterizing different psychiatric disorders. Nevertheless, several issues remain in developing neuroimaging based diagnostic nosology for mental illness. First, translational applications of current findings are limited due to the nonspecific relationship between the markers and disorders, with a considerable overlap between seemingly dissimilar disorders. Second, given that neuroimaging research almost always requires group-based analysis (due to low signal-to-noise ratio), it is unclear how neuroimaging-based diagnostic nosology could be translated into clinical environments for diagnosing and treating patients at an individual level. Third, most current neuroimaging methods are limited to providing a statistical characterization of the observed data and cannot provide mechanistic explanations regarding how the underlying neural populations interact and process information differently in patients. Addressing these issues, here, we propose to take a bold step towards modeling the entire landscape of brain's dynamical organization at an individual level. This modeled landscape could then be used as a ?lens? towards (a) characterizing (and stratifying) psychiatric illness and (b) generating biologically grounded mechanistic insights regarding how neural processes interact during ongoing cognition to give rise to different dynamical landscapes in patient populations. We discuss several challenges associated with our approach, propose solutions, and provide initial proof-of-concept results. To test the efficacy of our approach in stratifying clinical populations, we aim to examine and stratify already collected data from neuro-dynamically opposite populations ? Major Depressive Disorder (MDD) and Attention Deficiency and Hyperactivity Disorder (ADHD). Altogether, in this high-risk high-impact proposal, we challenge several existing paradigms to develop a computational psychiatry framework that will provide a much-needed platform for stratifying psychiatric illnesses and developing novel treatments to provide person-centric diagnosis and care.