Math tools may improve prediction in psychiatry

A special issue of Biological Psychiatry explores the utility of mathematical models in psychiatry and the potential of computational psychiatry to explain mechanisms of disease and improve treatment.

“The state-of-the-art in research in psychiatry consists of a bewildering variety of approaches and findings that, unfortunately, often do not coalesce into a coherent whole,” guest editor Tiago Maia, PhD, of University of Lisbon, Portugal, said in a press release.

Approaches based on mathematical theory have provided more unified explanations with significant prediction ability, which has been a “cornerstone” of impactful achievements in theoretical physics.

“I see theory-based computational psychiatry as a long-overdue effort to finally bring to psychiatry the same rigorous mathematical tools that have so successfully shaped fields such as physics — enriched now with the capacity for computational simulations, which vastly expand the range of problems that can be addressed mathematically,” Maia said in the release.

However, the clinical utility of mathematical models in mental health has not yet been proven.

“What I find really exciting about this special issue is that it demonstrates that this approach is already starting to bear fruit in terms of improved understanding in psychiatry,” Maia said.

The issue includes studies that review model-based control in dimensional psychiatry, computational psychosomatics and computational psychiatry, computational dysfunctions in anxiety and more.

“The studies included in this issue of Biological Psychiatry showcase the utility of this formal approach and that it can enrich understanding and guide principled questions in need of further investigation, spanning a range of issues of central importance,” guest editor Michael Frank, PhD, of Brown University, said in the release.

Quentin Huys , MD, PhD , of University of Zurich, also served as a guest editor of this special issue.

Reference:

Maia TV, et al. Biol Psychiatry. 2017;doi:10.1016/j.biopsych.2017.07.020.