Here are the slides for the keynote entitled “Symptomics: studying symptoms and their network configurations” I held at SYMPCA 2017 in September 2017.
A growing chorus of voices—including the DSM-5 task force and the NIMH—has raised concerns about the validity and reliability of most mental disorders such as Major Depressive Disorder (MDD). Efforts to develop biological tests have largely failed, and there have been limited advances in treatment. Why is that? To use MDD as an example, most researchers sum disparate symptoms such as sadness, insomnia, and concentration problems to one sumscore representing depression severity, and use thresholds to diagnose MDD. But sumscores discard information about specific symptoms by treating them as equivalent and interchangeable indicators of MDD, and the practice of thresholds leads to highly heterogeneous depressed samples in which patients often have few symptoms in common. Recent work suggests that this unaddressed heterogeneity explains the lack of research progress. A novel research framework—Symptomics—offers a way forward by focusing on the study of individual symptoms and their causal interactions in network models.