Computational Psychiatry?

From MIT Technology Review:

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior.

That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.

Computational psychiatry has suddenly made it possible to mine data from long-standing observations and link it to mathematical theories of cognition. It’s also become possible to develop computer-based experiments that carefully control environments so that specific behaviors can be studied in detail.

The article then goes on to discuss research reported in:

Sarah K Fineberg (MD PhD), Dylan Stahl (BA), Philip Corlett (PhD), Computational Psychiatry in Borderline Personality Disorder, Current Behavioral Neuroscience Reports, March 2017, Vol 4, Issue 1, pp31-40: arXiv:1707.03354v1 [q-bio.NC]

Purpose of review: We review the literature on the use and potential use of computational psychiatry methods in Borderline Personality Disorder.

Recent findings: Computational approaches have been used in psychiatry to increase our understanding of the molecular, circuit, and behavioral basis of mental illness. This is of particular interest in BPD, where the collection of ecologically valid data, especially in interpersonal settings, is becoming more common and more often subject to quantification. Methods that test learning and memory in social contexts, collect data from real-world settings, and relate behavior to molecular and circuit networks are yielding data of particular interest.

Summary: Research in BPD should focus on collaborative efforts to design and interpret experiments with direct relevance to core BPD symptoms and potential for translation to the clinic.