Models of neuromodulation for computational psychiatry

Advanced Review

Sandra Iglesias, Sara Tomiello, Maya Schneebeli, Klaas E. Stephan

Published Online: Sep 21 2016

DOI: 10.1002/wcs.1420

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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 This article is categorized under: Psychology > Brain Function and Dysfunction Neuroscience > Computation Neuroscience > Plasticity

Modulatory neurotransmitter systems. An illustrative representation of the (1) dopaminergic, (2) cholinergic, (3) serotonergic, and (4) noradrenergic systems.

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Computational psychiatry. One key goal of computational psychiatry is to develop (1) computational assays, (2) apply them to neuroimaging data from individual patients to infer disease‐relevant mechanisms which allow for (3) detection of subgroups in order to (4) improve individual treatment prediction. (Reprinted with permission from Ref . Copyright 2015 Elsevier; Panel 1: Reprinted with permission from Ref . Copyright 2009 Elsevier)

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Computational quantities of neuromodulation. This figure summarizes results from previous model‐based functional magnetic resonance imaging (fMRI) studies of neuromodulation. The following studies reported correlations between BOLD signal in the human midbrain and (1) reward prediction errors (PEs; Reprinted with permission from Ref . Copyright 2008 Nature Publishing Group), (2) reward PEs (Reprinted with permission from Ref . Copyright 2011 Elsevier), (3) sensory PEs (Reprinted with permission from Ref . Copyright 2013 Elsevier), (4) precision (Reprinted with permission from Ref . Copyright 2015 Oxford University Press). Additionally, (5) expected uncertainty (specifically: precision‐weighted PE about outcome probability) correlated with BOLD signal in the cholinergic septum (Reprinted with permission from Ref . Copyright 2013 Elsevier), (6) whereas unexpected uncertainty showed an inverse correlation with BOLD activity in the noradrenergic locus coeruleus (Reprinted with permission from Ref . Copyright 2013 Elsevier)

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Model‐based functional magnetic resonance imaging (fMRI) analysis. A schematic demonstration of the steps involved in a model‐based fMRI analysis. (1) Fitting computational models to behavioral data provides a trajectory of trial‐wise (k) estimates of computational quantities. Here, we consider the example of a precision‐weighted outcome prediction error (PE; μ₂) from the hierarchical Gaussian filter (HGF). This consists of a precision weight (green) and the PE (purple). (2) Trial‐by‐trial trajectories of these quantities are (3) entered into a general linear model (GLM) as parametric modulators (third and fifth columns), modeling a specific time window in the trial (e.g., the outcome phase). All regressors are convolved with the canonical hemodynamic response function and its temporal derivative. (4) Voxel‐wise contrasts applied to the parametric modulators result in statistical parametric maps, which, following correction for multiple comparisons, reveal significant relations between the blood oxygen level‐dependent (BOLD) signal in a particular region and the computational quantity of interest. SFG: superior frontal gyrus.

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