Retzler, C. | bioRxiv Science

bioRxiv2024

Neural markers of abstinence in alcohol dependence: Insights from Reward Learning

Maladaptive reward learning and decision-making circuity are key factors in the onset and progression of alcohol use disorder and have therefore emerged as key targets for neuropsychological and pharmacological interventions. Probabilistic reversal learning studies have consistently reported impaired learning in recently detoxified alcohol dependent (AD) participants. However, the neural and behavioural changes associated with reward learning which occur throughout abstinence remain unexplored. Here, we show that AD participants, with mean abstinence of 20 months, exhibit intact behavioural performance within an electroencephalography (EEG) probabilistic reversal learning task. Reinforcement learning modelling reveals reward and punishment related learning rates and exploration rates are comparable between AD and healthy control (HC) participants, suggesting recovery of even the nuanced aspects of learning in longer term abstinence. However, EEG analysis indicates that AD, compared to HC participants, show globally elevated event-related potential (ERP) feedback related negativity (FRN) following reward valuation. Furthermore, Feedback-P3 valence prediction error signal is negatively associated with abstinence duration indicating a potential state marker of AD recovery. We then employ unsupervised machine learning (canonical polyadic tensor decomposition) to identify spatiotemporal EEG patterns of reward valuation in a purely data-driven manner. Classification analysis shows these tensor components can predict group membership with 80.4% accuracy. By probing group differences in tensor components, we discover early hyperfunctioning in centro-frontal regions linked to alcohol dependence and associated with early abstinence. The clinically meaningful EEG biomarkers presented here could guide the development of more targeted treatments and support big data approaches to objective patient monitoring.

neuroscience↗

bioRxiv2016

Spatiotemporal characterization of the neural correlates of outcome valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. Two separate outcome dimensions, namely the valence (positive or negative) and surprise (the absolute degree of deviation from expectations) of an outcome are thought to subserve adaptive decision-making and learning, however their neural correlates and relative contribution to learning remain debated. Here, we coupled single-trial analyses of electroencephalography with simultaneously acquired fMRI, while participants performed a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of outcome valence and surprise in the human brain. Electrophysiological variability in outcome valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Variability in outcome surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations we also found a linear superposition of the two outcome dimensions in a smaller network encompassing visuo-mnemonic and reward areas. This spatiotemporal overlap was uniquely exposed by our EEG-informed fMRI approach. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

neuroscience↗