Pragmatic Computational Psychiatry: Towards Precision in Diagnostic, Prognostic, and Treatment-related Objective Markers in Mood and Anxiety Disorders
Dr. Martin Paulus, MD
Dr. Martin Paulus, MD has been the Scientific Director and President of the Laureate Institute for Brain Research (LIBR) in Tulsa, OK since May 2014. Prior he had been a Professor in the Department of Psychiatry at the University of California San Diego and the Director of Telemental Health at the Veterans Affairs San Diego Health Care System. Dr. Paulus has a Google Scholar h-index of 101 and has published over 400 peer-reviewed manuscripts. Dr. Paulus is the Deputy Editor of JAMA Psychiatry, a Series Editor for Current Topics in Behavioral Neuroscience, and is on several editorial boards of top-tier psychiatric journals. He has served on numerous NIH and International Study Sections and is currently on the National Institute of Mental Health Board of Scientific Councilors. The goal for LIBR is to identify disease-modifying processes (DMP) based on circuits, behavior, or other levels of analysis, which – when modulated – change (1) the risk for, (2) the severity of, or (3) the recurrence of a disease such as mood, anxiety, or substance use disorder. Dr. Paulus’ program of research is to delineate DMPs and provide pathways towards the development of process-specific transdiagnostic interventions that have pragmatic utility, i.e. improve a patient’s condition faster with fewer side effects and fewer recurrences, and explanatory value, i.e. refine our understanding of the causal relationships between specific processes and a mental health condition.
About the presentation
Mood and Anxiety Disorders are the most prevalent and disabling mental health conditions worldwide . There are limited explanatory  and predictive  disease models available for these conditions. Evidence from multiple studies focused on explanatory and predictive disease models in psychiatry support the idea that there is not one overarching process that explains or predicts Mood or Anxiety Disorders . On the other hand, there is also limited evidence for dis-ease process heterogeneity among psychiatric disorders  and meta-analyses of treatment studies do not support the hypothesis that there are heterogeneous treatment effects of existing treatments [6,7]. This leads to a conundrum. One the one hand there is no evidence of a homogenous process that underlies any given psychiatric disorder, on the other hand there is no evidence of interventions working better for some individuals with a given psychiatric disorder than another. One possibility that is emerging is that psychiatric disorders as currently defined consist of a mixture of disordered processes that occur on different levels of analyses and yield disease states that are highly unique for any given individual. Moreover, since current interventions do not specifically target any particular disease process it should not be surprising that these interventions do not give rise to heterogeneous treatment effects.
A panel will follow the presentation to advance precision convergence science in discussing how mathematical algorithmic approaches can develop a quantitative mechanistic understanding of the brain and society multiscale processes that underlie mental health and disease to inform better targeted and more effective practical applications based on model-based analyses. Discussion will address how such knowledge can inform better targeted and more impactful professional practice/innovation/interventions for lifelong socio-emotional wellness and resilience in both health and disease. The webinar is chaired by Prof. Laurette Dubé, Chair and Scientific Director, McGill Centre for the Convergence of Health and Eco-nomic (MCCHE), and co-chaired by Dr. Gillian Bartlett, Associate Dean for Population Health and Outcomes Research at the School of Medicine at the University of Missouri (Mizzou).