Psychiatry is one of the few medical specialties that relies on the patient’s subjective reports and clinician observation alone, with little to no objective data to aid the subjective analysis. Linking brain mechanisms to behavior using algorithms, behavioral testing, and neuroimaging could arm clinicians with vital data to understand causes and to improve diagnosis, resulting in more personalized and effective treatment plans. This approach is known as computational psychiatry, and it could completely transform the field in the near future. Since its inception in the early 2010s computational psychiatry has become one of the fastest growing and most exciting areas in neuropsychiatry. 

Mount Sinai’s Center for Computational Psychiatry, led by Laura Berner, PhD, is among the first integrated centers in the world that studies how quantitative tools and methodologies can be used to improve mental health diagnosis and treatment. 

Leveraging the rich clinical resources and computational expertise across departments, the Center for Computational Psychiatry is dedicated to deepening our understanding of how both algorithms and the brain’s biology can contribute to what we know about mental health issues such as addiction, eating disorders, and personality disorders. The Center is especially interested in a trans-diagnostic approach towards mental health; for example, by examining how aberrant social cognition might manifest itself similarly or differently across a range of distinct diagnostic labels. This research could ultimately lead to paradigm-shifting findings for neuropsychiatry research and life-changing treatments for those with psychiatric disorders.