Machine learning links major dimensions of mental illness in youth to abnormalities of brain networks
A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients. A team at Penn Medicine led by Theodore D. Satterthwaite, MD, an assistant professor in the department of Psychiatry, mapped abnormalities in brain networks to four dimensions of psychopathology: Mood, psychosis, fear, and disruptive externalizing behavior. The research is published in Nature Communications.
Currently, psychiatry relies on patient reporting and physician observations alone for clinical decision making, while other branches of medicine have incorporated biomarkers to aid in diagnosis, determination of prognosis, and selection of treatment for patients. While previous studies using standard clinical diagnostic categories have found evidence for brain abnormalities, the high level of diversity within disorders and comorbidity between disorders has limited how this kind of research may lead to improvements in clinical care.
To uncover the brain networks associated with psychiatric disorders, the team studied a large sample of adolescents and young adults (999 participants, ages 8 to 22). All participants completed both functional MRI scans and a comprehensive evaluation of psychiatric symptoms as part of the Philadelphia Neurodevelopmental Cohort (PNC), an effort lead by Raquel E. Gur, MD, PhD, professor of Psychiatry, Neurology, and Radiology, that was funded by the National Institute of Mental Health. The brain and symptom data were then jointly analyzed using a machine learning method called sparse canonical correlation analysis.
This analysis revealed patterns of changes in brain networks that were strongly related to psychiatric symptoms. In particular, the findings highlighted four distinct dimensions of psychopathology—mood, psychosis, fear, and disruptive behavior—all of which were associated with a distinct pattern of abnormal connectivity across the brain.
The researchers found that each brain-guided dimension contained symptoms from several different clinical diagnostic categories. For example, the mood dimension was comprised of symptoms from three categories, e.g. depression (feeling sad), mania (irritability), and obsessive-compulsive disorder (recurrent thoughts of self-harm). Similarly, the disruptive externalizing behavior dimension was driven primarily by symptoms of both Attention Deficit Hyperactivity Disorder(ADHD) and Oppositional Defiant Disorder (ODD), but also included the irritability item from the depression domain. These findings suggest that when both brain and symptomatic data are taken into consideration, psychiatric symptoms do not neatly fall into established categories. Instead, groups of symptoms emerge from diverse clinical domains to form dimensions that are linked to specific patterns of abnormal connectivity in the brain.
These two brain networks have long intrigued psychiatrists and neuroscientists because of their crucial role in complex mental processes such as self-control, memory, and social interactions. The findings in this study support the theory that many types of psychiatric illness are related to abnormalities of brain development.
The team also examined how psychopathology differed across age and sex. They found that patterns associated with both mood and psychosis became significantly more prominent with age. Additionally, brain connectivity patterns linked to mood and fear were both stronger in female participants than males.
