Computational dissociation of dopaminergic and cholinergic effects on action selection and inhibitory control

Abstract

BackgroundPatients with schizophrenia make more errors than healthy subjects on the antisaccade task. In this paradigm, participants are required to inhibit a reflexive saccade to a target and to select the correct action (a saccade in the opposite direction). While the precise origin of this deficit is not clear, it has been connected to aberrant dopaminergic and cholinergic neuromodulation.MethodsTo study the impact of dopamine and acetylcholine on inhibitory control and action selection, we administered two selective drugs (levodopa 200mg/galantamine 8mg) to healthy volunteers (N=100) performing the antisaccade task. A computational model (SERIA) was employed to separate the contribution of inhibitory control and action selection to empirical reaction times and error rates.ResultsModeling suggested that levodopa improved action selection (at the cost of increased reaction times) but did not have a significant effect on inhibitory control. By contrast, according to our model, galantamine affected inhibitory control in a dose dependent fashion, reducing inhibition failures at low doses and increasing them at higher levels. These effects were sufficiently specific that the computational analysis allowed for identifying the drug administered to an individual with 70% accuracy.ConclusionsOur results do not support the hypothesis that elevated tonic dopamine strongly impairs inhibitory control. Rather levodopa improved the ability to select correct actions. Instead, inhibitory control was modulated by cholinergic drugs. This approach may provide a starting point for future computational assays that differentiate neuromodulatory abnormalities in heterogeneous diseases like schizophrenia.

GABAergic cortical network physiology in frontotemporal lobar degeneration

“…Such trials are a pressing need for bvFTD, PSP and dementia generally. 76–78 We propose that in PSP, FTD and other neurological and psychiatric disorders, 38 , 79–81 the combination of model-based physiology and targeted psychopharmacology can provide critical evidence to reduce the risk of such trials, reducing cost, duration and failure rates of phase II-III trials.…”

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

“…The authors identified different roles for the different neuromodulatory systems, linking noradrenaline to unexpected uncertainty, acetylcholine to environmental uncertainty, and dopamine to uncertainty representations for fast, adaptive responses. Finally, Aponte et al (2020a) demonstrated that computational quantities sensitive to neuromodulatory processes can also be derived from generative models of reflexive eye movements. Specifically, the authors conducted a double-blind placebo-controlled pharmacological study and found that computational quantities derived from an antisaccade task using the SERIA model can distinguish between dopaminergic and cholinergic effects on action selection and inhibitory control, allowing for out-of-sample predictions about the drug administered with 70% accuracy.…”

“…Specifically, in order to model error rates and reaction times during the task, SERIA postulates two interacting processes (Figure 7, bottom ): (i) a fast GO/NO-GO race between a prepotent response (prosaccade) towards the visual cue and a signal to cancel this erroneous action, and (ii) a slow GO/GO race between two units encoding the cue-action mapping, accounting for slow voluntary saccades. The parameters of this model, which are estimated using a sampling-based hierarchical Bayesian scheme, are sensitive to dopaminergic and cholinergic manipulations and were found to allow for out-of-sample predictions about the drug administered to an individual with 70% accuracy (Aponte et al, 2020a).…”

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

“…The strategy of using parameter estimates from a generative model for subsequent (un-) supervised learning is known as 'generative embedding' (Brodersen et al, 2011) and plays a central role in attempts to establish computational assays for psychiatry (Stephan et al, 2017). For example, recent work suggested that dopaminergic and cholinergic alterations can be predicted out-of-sample from eye movements (Aponte et al, 2020). The generative embedding approach has two main advantages: it offers a theory-led dimensionality reduction (from highdimensional noisy data to a small set of model parameter estimates), and it enables the interpretation of machine learning results in terms of biological mechanisms represented by a model.…”