Many Paths to Mapping Out Mental Illness
By Andrew J. Gerber, MD, PhD, Medical Director/CEO, Austen Riggs Center
Psychiatry has long utilized symptom clusters to classify mental illness, and today the Diagnostic and Statistical Manual of Mental Disorders (DSM), which outlines specific conditions as independent entities, remains the standard tool in the field. However, in the past decade or so, there has been growing interest in new ways to characterize mental illness, such as through gene mutations or physiological recordings, which have the potential of adding depth and dimensionality to the understanding of mental illness, and of informing the treatment trajectory. The National Institute of Mental Health (NIMH) is now heavily focused on funding this type of research, using its Research Domain Criteria (RDoC) Matrix as a conceptual framework against which new measures may be proposed and tested. Enticingly, RDoC presents the underpinnings of mental illness as continua or spectra; however, while it has promoted a significant amount of exploratory research to identify new measures, it has also met skepticism regarding how it can add to the existing clinical body of knowledge in psychiatry.
This has been further complicated by the fact that as new RDoC constructs are generated, it has not always been easy to put them in clinical context. In a recent piece for the NIH Record, Dr. Roy Perlis, Director of the Center for Qualitative Health at Massachusetts General Hospital, describes finding himself in the rather dense weeds of looking for genetic markers for depression. He generated lots of data, but no obvious lead candidates for a “depression gene” – nor any clear path forward. Taking a step back, Dr. Perlis realized that there was an opportunity to align these new genetic findings with the vast amount of data collected from patients in the clinical setting, including from electronic health records, surveys, and other materials. Perlis found that notes written by doctors were especially rich in information, as they often capture things that can’t be coded or don’t have a natural home in the medical record. By marrying this trove of information with new research data, Dr. Perlis was able to link specific clinical characteristics of depression to 15 genetic markers that appear to be associated with depression. Dr. Perlis has gone on to review medical records for a history of drug prescription in patients with depression, to help understand why drugs sometimes don’t work and how this experience may have impacted their treatment outcome.
This research highlights the fact that while we have much to learn from new measures of mental illness, the “value added” lies in combining what we learn from the utilization of new technologies, with the rich history of the patient that we gather in the face-to-face clinical setting.