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 Xiaosi Gu, 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. Dr. Gu completed her postdoctoral training in computational psychiatry at the Wellcome Trust Centre for Neuroimaging at University College London. During her time there, she not only conducted some of the earliest studies on addiction and mental health using computational approaches, but also set up and has since been directing the world’s first computational psychiatry course. Dr. Gu brought her computational psychiatry lab to Mount Sinai in 2018 to further develop the emerging subspecialty, which will accelerate with the Center’s launch and rapid expansion.
Leveraging the rich clinical resources and computational expertise across departments, the new 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, autism, 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.