Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy

Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other

Reinforcement magnitudes modulate subthalamic beta band activity in patients with Parkinson's disease

We set out to investigate whether beta oscillations in the human basal ganglia are modulated during reinforcement learning. Based on previous research, we assumed that beta activity might either reflect the magnitudes of individuals' received reinforcements (reinforcement hypothesis), their reinforcement prediction errors (dopamine hypothesis) or their tendencies to repeat versus adapt responses based upon reinforcements (status-quo hypothesis). We tested these hypotheses by recording local field potentials (LFPs) from the subthalamic nuclei of 19 Parkinson's disease patients engaged in a reinforcement-learning paradigm. We then correlated patients' reinforcement magnitudes, reinforcement prediction errors and response repetition tendencies with task-related power changes in their LFP oscillations. During feedback presentation, activity in the frequency range of 14 to 27 Hz (beta spectrum) correlated positively with reinforcement magnitudes. During responding, alpha and low beta activity (6 to 18 Hz) was negatively correlated with previous reinforcement magnitudes. Reinforcement prediction errors and response repetition tendencies did not correlate significantly with LFP oscillations. These results suggest that alpha and beta oscillations during reinforcement learning reflect patients' observed reinforcement magnitudes, rather than their reinforcement prediction errors or their tendencies to repeat versus adapt their responses, arguing both against an involvement of phasic dopamine and against applicability of the status-quo theory