Tracking mental health with machine learning and brain imaging

Social cognition

Deficits in cognitive functions that are important for competent interpersonal interactions and what we tend to designate with the unifying term of ”‘social cognition” (eg, empathy) are increasingly perceived as a crucial feature of several mental health disorders, even those that are not associated with readily apparent social dysfunction. Indeed, the National Institute of Mental Health considers social cognition an essential domain to understand the nature of mental health and its disorders.1-5 Mental illness affects a significant portion of the global population. In 2017 in the US alone, an estimated 11.2 million adults (4.5%) had serious mental illness.6 Studies have demonstrated that impairments in social cognition predict disability severity and the increased use of mental health resources, including hospital admissions and emergency department visits.7 Therefore, it is likely that treatments that successfully improve social cognition may significantly impact mental health outcomes. Unfortunately, we not only lack effective treatments targeting social cognition but also the strategies and tools to track changes in this crucial functional domain for mental health outcomes. Without such strategies and tools, it is difficult to figure out which treatments have the most potential to facilitate social behavior and empathy to improve the clinical condition of patients with mental health issues.