Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function.

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Such dysfunction may be caused by aberrant interactions of different neuromodulators with NMDARs, which could explain clinical heterogeneity among patients. In two studies (N = 81 each), we used a double-blind placebo-controlled between-subject design to systematically test whether auditory mismatch responses under varying levels of environmental stability are sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. We found a significant drug × mismatch interaction: while the muscarinic acetylcholine receptor antagonist biperiden delayed and topographically shifted mismatch responses, particularly during high stability, this effect could not be detected for amisulpride, a dopamine D2/D3 receptor antagonist. Neither galantamine nor levodopa, which elevate acetylcholine and dopamine levels, respectively, exerted significant effects on MMN. This differential MMN sensitivity to muscarinic versus dopaminergic receptor function may prove useful for developing tests that predict individual treatment responses in schizophrenia.

Models of neuromodulation for computational psychiatry.

Psychiatry faces fundamental challenges: based on a syndrome-based nosology, it presently lacks clinical tests to infer on disease processes that cause symptoms of individual patients and must resort to trial-and-error treatment strategies. These challenges have fueled the recent emergence of a novel field-computational psychiatry-that strives for mathematical models of disease processes at physiological and computational (information processing) levels. This review is motivated by one particular goal of computational psychiatry: the development of 'computational assays' that can be applied to behavioral or neuroimaging data from individual patients and support differential diagnosis and guiding patient-specific treatment. Because the majority of available pharmacotherapeutic approaches in psychiatry target neuromodulatory transmitters, models that infer (patho)physiological and (patho)computational actions of different neuromodulatory transmitters are of central interest for computational psychiatry. This article reviews the (many) outstanding questions on the computational roles of neuromodulators (dopamine, acetylcholine, serotonin, and noradrenaline), outlines available evidence, and discusses promises and pitfalls in translating these findings to clinical applications. WIREs Cogn Sci 2017, 8:e1420. doi: 10.1002/wcs.1420 For further resources related to this article, please visit the WIREs website.