Cagna, Christopher J.. Feedback-based learning in multiple sclerosis: neurobehavioral mechanisms and potential clinical applications. Retrieved from https://doi.org/doi:10.7282/t3-xaya-bf58
DescriptionLearning from outcomes of our choices and adapting our behavior accordingly are fundamental aspects of human cognition. Feedback signaling adequate performance, or the need to improve performance, increases our chances of successful goal-directed behavior. While processing feedback information is vital for learning, so too, are processes that motivate seeking feedback. Feedback-based learning is comprised of complementary components, including decision-making about seeking feedback, processing of acquired feedback, and demonstrating learning through performance. One unexplored setting for feedback-based learning is cognitive rehabilitation. Given that performance feedback is integral to cognitive remediation interventions in clinical populations, it is imperative to understand how feedback-based learning – and its associated brain mechanisms – operate in these groups. One such population is people with multiple sclerosis (MS), who frequently report debilitating cognitive fatigue (CF). In the brain, CF in MS targets corticostriatal networks – rooted in the striatum and prefrontal regions – known to also process performance feedback in neurotypical (NT) individuals. This neural connection motivated this dissertation, which probed neurobehavioral mechanisms underlying feedback-based learning in MS and corresponding influences of CF. Furthermore, two novel experimental paradigms for enhancing feedback value were also tested. Experiment 1 investigated CF’s impact on feedback processing and associated neural circuitry in MS and NT samples. Both groups displayed comparable learning, despite MS participants’ greater CF. During positive feedback outcomes, MS participants exhibited stronger functional connectivity between the ventral striatum and task-specific regions, and diminished connectivity between the caudate and dorsal anterior cingulate. This inhibited corticostriatal connectivity was also associated with greater CF, suggesting attenuation of feedback value by CF. Experiment 2 probed CF’s impact on feedback-seeking decisions in MS and NT participants. Both groups exhibited comparable likelihoods of seeking feedback, suggesting that feedback valuation is robust to CF in MS. Experiments 3 and 4 assessed the efficacy of the novel feedback value paradigms in NT samples, which revealed preliminary success for these interventions. In sum, this dissertation reveals CF-induced alterations of corticostriatal connectivity during feedback-based learning in MS, identifies motivational factors that may safeguard feedback valuation from CF, and contributes novel tools for application in clinical contexts to maximize feedback-based cognitive rehabilitation efficacy.