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Bahuguna J, Verstynen T, Rubin JE. How cortico-basal ganglia-thalamic subnetworks can shift decision policies to maximize reward rate. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.05.21.595174. [PMID: 38826315 PMCID: PMC11142098 DOI: 10.1101/2024.05.21.595174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translate into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we present the results of a computational approach to address this problem. Specifically, we simulated decisions driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit an evidence accumulation model to the resulting behavior. Using canonical correlation analysis, we then replicated the identification of three control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each of these subnetworks mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target and found that feedback-driven dopaminergic plasticity on cortico-striatal synapses effectively manages the speed-accuracy tradeoff so as to increase reward rate over time. The learning-related changes in the decision policy can be decomposed in terms of the contributions of each control ensemble, whose influence is driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts subnetworks within CBGT circuits so as to maximize reward rate by strategically modulating how evidence is used to drive decisions.
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Affiliation(s)
- Jyotika Bahuguna
- Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Timothy Verstynen
- Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
| | - Jonathan E Rubin
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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Gilmour W, Mackenzie G, Feile M, Tayler-Grint L, Suveges S, Macfarlane JA, Macleod AD, Marshall V, Grunwald IQ, Steele JD, Gilbertson T. Impaired value-based decision-making in Parkinson's disease apathy. Brain 2024; 147:1362-1376. [PMID: 38305691 PMCID: PMC10994558 DOI: 10.1093/brain/awae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/07/2023] [Accepted: 01/13/2024] [Indexed: 02/03/2024] Open
Abstract
Apathy is a common and disabling complication of Parkinson's disease characterized by reduced goal-directed behaviour. Several studies have reported dysfunction within prefrontal cortical regions and projections from brainstem nuclei whose neuromodulators include dopamine, serotonin and noradrenaline. Work in animal and human neuroscience have confirmed contributions of these neuromodulators on aspects of motivated decision-making. Specifically, these neuromodulators have overlapping contributions to encoding the value of decisions, and influence whether to explore alternative courses of action or persist in an existing strategy to achieve a rewarding goal. Building upon this work, we hypothesized that apathy in Parkinson's disease should be associated with an impairment in value-based learning. Using a four-armed restless bandit reinforcement learning task, we studied decision-making in 75 volunteers; 53 patients with Parkinson's disease, with and without clinical apathy, and 22 age-matched healthy control subjects. Patients with apathy exhibited impaired ability to choose the highest value bandit. Task performance predicted an individual patient's apathy severity measured using the Lille Apathy Rating Scale (R = -0.46, P < 0.001). Computational modelling of the patient's choices confirmed the apathy group made decisions that were indifferent to the learnt value of the options, consistent with previous reports of reward insensitivity. Further analysis demonstrated a shift away from exploiting the highest value option and a reduction in perseveration, which also correlated with apathy scores (R = -0.5, P < 0.001). We went on to acquire functional MRI in 59 volunteers; a group of 19 patients with and 20 without apathy and 20 age-matched controls performing the Restless Bandit Task. Analysis of the functional MRI signal at the point of reward feedback confirmed diminished signal within ventromedial prefrontal cortex in Parkinson's disease, which was more marked in apathy, but not predictive of their individual apathy severity. Using a model-based categorization of choice type, decisions to explore lower value bandits in the apathy group activated prefrontal cortex to a similar degree to the age-matched controls. In contrast, Parkinson's patients without apathy demonstrated significantly increased activation across a distributed thalamo-cortical network. Enhanced activity in the thalamus predicted individual apathy severity across both patient groups and exhibited functional connectivity with dorsal anterior cingulate cortex and anterior insula. Given that task performance in patients without apathy was no different to the age-matched control subjects, we interpret the recruitment of this network as a possible compensatory mechanism, which compensates against symptomatic manifestation of apathy in Parkinson's disease.
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Affiliation(s)
- William Gilmour
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
- Department of Neurology, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Graeme Mackenzie
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
- Department of Neurology, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Mathias Feile
- Rehabilitation Psychiatry, Murray Royal Hospital, Perth PH2 7BH, UK
| | | | - Szabolcs Suveges
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
| | - Jennifer A Macfarlane
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
- Medical Physics, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
- SINAPSE, University of Glasgow, Imaging Centre of Excellence, Level 2, Queen Elizabeth University Hospital, Glasgow G51 4TF, Scotland, UK
| | - Angus D Macleod
- Institute of Applied Health Sciences, School of Medicine, University of Aberdeen, Foresterhill, Aberdeen AB24 2ZD, UK
- Department of Neurology, Aberdeen Royal Infirmary, Foresterhill, Aberdeen AB24 2ZD, UK
| | - Vicky Marshall
- Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Iris Q Grunwald
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
| | - J Douglas Steele
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
| | - Tom Gilbertson
- Division of Imaging Science and Technology, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
- Department of Neurology, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
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