Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning.

Neuroimage

Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany.

Published: February 2021

AI Article Synopsis

  • The study investigates how our brains learn and predict sensory events over time using Bayesian methods, highlighting how our brains process prediction errors (PEs) based on uncertainty.
  • In previous research, low-level PEs linked to visual outcomes engaged the dopaminergic midbrain, while high-level PEs for cue-outcome associations activated the cholinergic basal forebrain, suggesting different influences of dopamine and acetylcholine at various levels of processing.
  • The current research involved two fMRI studies with pharmacological interventions, revealing that specific medications impacted brain responses to low-and high-level PEs, indicating the nuanced role of neurotransmitters in processing prediction errors.

Article Abstract

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

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http://dx.doi.org/10.1016/j.neuroimage.2020.117590DOI Listing

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