Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

Psychosis as a disorder of muscarinic signalling: psychopathology and pharmacology

Dopaminergic receptor antagonism is a crucial component of all licensed treatments for psychosis, and dopamine dysfunction has been central to pathophysiological models of psychotic symptoms. Some clinical trials, however, indicate that drugs that act through muscarinic receptor agonism can also be effective in treating psychosis, potentially implicating muscarinic abnormalities in the pathophysiology of psychosis. Here, we discuss understanding of the central muscarinic system, and we examine preclinical, behavioural, post-mortem, and neuroimaging evidence for its involvement in psychosis. We then consider how altered muscarinic signalling could contribute to the genesis and maintenance of psychotic symptoms, and we review the clinical evidence for muscarinic agents as treatments. Finally, we discuss future research that could clarify the relationship between the muscarinic system and psychotic symptoms.

M2 receptors are required for spatiotemporal sequence learning in mouse primary visual cortex

Acetylcholine is a neurotransmitter that plays a variety of roles in the central nervous system. It was previously shown that blocking muscarinic receptors with a nonselective antagonist prevents a form of experience-dependent plasticity termed "spatiotemporal sequence learning" in the mouse primary visual cortex (V1). Muscarinic signaling is a complex process involving the combined activities of five different G protein-coupled receptors, M1-M5, all of which are expressed in the murine brain but differ from each other functionally and in anatomical localization. Here we present electrophysiological evidence that M2, but not M1, receptors are required for spatiotemporal sequence learning in mouse V1. We show in male mice that M2 is highly expressed in the neuropil in V1, especially in thalamorecipient layer 4, and colocalizes with the soma in a subset of somatostatin-expressing neurons in deep layers. We also show that expression of M2 receptors is higher in the monocular region of V1 than it is in the binocular region but that the amount of experience-dependent sequence potentiation is similar in both regions and that blocking muscarinic signaling after visual stimulation does not prevent plasticity. This work establishes a new functional role for M2-type receptors in processing temporal information and demonstrates that monocular circuits are modified by experience in a manner similar to binocular circuits.NEW & NOTEWORTHY Muscarinic acetylcholine receptors are required for multiple forms of plasticity in the brain and support perceptual functions, but the precise role of the five subtypes (M1-M5) are unclear. Here we show that the M2 receptor is specifically required to encode experience-dependent representations of spatiotemporal relationships in both monocular and binocular regions of mouse V1. This work identifies a novel functional role for M2 receptors in coding temporal information into cortical circuits.

Acetylcholine modulates the precision of prediction error in the auditory cortex

A fundamental property of sensory systems is their ability to detect novel stimuli in the ambient environment. The auditory brain contains neurons that decrease their response to repetitive sounds but increase their firing rate to novel or deviant stimuli; the difference between both responses is known as stimulus-specific adaptation or neuronal mismatch (nMM). Here, we tested the effect of microiontophoretic applications of ACh on the neuronal responses in the auditory cortex (AC) of anesthetized rats during an auditory oddball paradigm, including cascade controls. Results indicate that ACh modulates the nMM, affecting prediction error responses but not repetition suppression, and this effect is manifested predominantly in infragranular cortical layers. The differential effect of ACh on responses to standards, relative to deviants (in terms of averages and variances), was consistent with the representational sharpening that accompanies an increase in the precision of prediction errors. These findings suggest that ACh plays an important role in modulating prediction error signaling in the AC and gating the access of these signals to higher cognitive levels.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity

BACKGROUND

Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia.

METHODS

Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time.

RESULTS

Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters.

CONCLUSIONS

Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

Why mismatch negativity continues to hold potential in probing altered brain function in schizophrenia

The brain potential known as mismatch negativity (MMN) is one of the most studied indices of altered brain function in schizophrenia. This review looks at what has been learned about MMN in schizophrenia over the last three decades and why the level of interest and activity in this field of research remains strong. A diligent consideration of available evidence suggests that MMN can serve as a biomarker in schizophrenia, but perhaps not the kind of biomarker that early research supposed. This review concludes that MMN measurement is likely to be most useful as a monitoring and response biomarker enabling tracking of an underlying pathology and efficacy of interventions, respectively. The role of, and challenges presented by, pre-clinical models is discussed as well as the merits of different methodologies that can be brought to bear in pursuing a deeper understanding of pathophysiology that might explain smaller MMN in schizophrenia.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

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.

Markers of muscarinic deficit for individualized treatment in schizophrenia

Recent clinical studies have shown that agonists at muscarinic acetylcholine receptors effectively reduce schizophrenia symptoms. It is thus conceivable that, for the first time, a second substance class of procholinergic antipsychotics could become established alongside the usual antidopaminergic antipsychotics. In addition, various basic science studies suggest that there may be a subgroup of schizophrenia in which hypofunction of muscarinic acetylcholine receptors is of etiological importance. This could represent a major opportunity for individualized treatment of schizophrenia if markers can be identified that predict response to procholinergic vs. antidopaminergic interventions. In this perspective, non-response to antidopaminergic antipsychotics, specific symptom patterns like visual hallucinations and strong disorganization, the presence of antimuscarinic antibodies, ERP markers such as mismatch negativity, and radiotracers are presented as possible in vivo markers of muscarinic deficit and thus potentially of response to procholinergic therapeutics. Finally, open questions and further research steps are outlined.

Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasize speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.