IUPHAR review: Computational Psychiatry 2.0. A new tool for supporting combination therapy of psychopharmacology with neuromodulation in schizophrenia

Recent clinical trial successes in schizophrenia with non-dopaminergic agents have rejuvenated the field after a long period of unsuccesfull attempts. At the same time, non-invasive neurostimulation has been increasingly applied in other mental health disorders while a few studies have been performed in schizophrenia. The time has arrived to consider combining psychotherapy with neuromodulation. However, a systematic approach to optimize trial designs is needed. “Computational Psychiatry” has been defined as computational neuroscience modeling using biophysically and anatomically realistic representations of key brain areas based on neuroimaging data and biological knowledge. In this position paper, we will expand this concept to include modeling drug exposure and pharmacology in combination with non-invasive neuromodulation. This computational approach can be used to optimize the impact of psychotherapy and active neuromodulation. This computational platform generates a new in silico biomarker, the “information bandwidth”, that might be related to clinical outcomes in schizophrenia. This is based on the assumption that the information processing capacity of the human brain can be represented by a measure of the entropy that quantifies the level of uncertainty associated with the brain processes. Previously we have shown that this readout in a computational neuroscience model of the closed cortical-striatal-thalamocortical loop is highly correlated with clinical changes in positive symptoms after antipsychotic treatment. In this paper we will present a strategy on how this expanded Computational Psychiatry approach can support optimization of clinical trial design combining neuromodulation with psychopharmacology, as well as the understanding and mitigating of the placebo response.