Neural oscillatory correlates of motor vigor: an magnetoencephalographic study

Abstract

Previous studies have shown that the anticipation of reward enhances motor performance, which reduces movement time and increases velocity [1]. In our recent study [2], we observed that when participants are required to infer the changing probabilities of a reward in a dynamic and uncertain setting, heightened expectations are consistently associated with faster motor performance. The study showed that performance time sensitivity to prediction strength remained consistent among both young and older healthy adults, as well as those with Parkinson’s disease. While the effects of dynamic motor strength have been observed, the neurological processes involved remain to be determined [3]. The study examined the neural oscillatory connections to motor vigor in dynamic and unpredictable settings. We used magnetoencephalography (MEG) and individual structural magnetic resonance imaging (MRI) to record readings from 25 healthy human participants (18 females) during the execution of our newly developed reward-based motor decision-making task [2]. This study used a reversal learning paradigm with shifting stimulus-outcome relationships. Participants were required to deduce which of two stimuli was linked to a reward on each trial, and indicate their choice through one of two finger press sequences, each with a distinct auditory response. The task was conducted in an unstable context, leading to fluctuations in the probability of reward associated with each response over time. First, we examined decision-making behavior using the validated Hierarchical Gaussian Filter (HGF, [4]). The model that most accurately described the behavioral data was the three-level “extended” HGF for binary categorical inputs, which is paired with a response model where decisions are dependent on the trial-wise estimate of volatility. This study allowed for the generation of reward probability trajectories on a trial-by-trial basis. Subsequently, applying Bayesian linear mixed models, we found a relationship between belief strength regarding reward contingencies and performance tempo on a trial-by-trial basis. The analysis of MEG signals is centered on reconstructing oscillatory activity sources using Linearly Constrained Minimum Variance beamforming [5]. Currently, we use convolution models in the source space to identify neural oscillatory correlations that differentiate motor performance and decision making. Next, we will evaluate connectivity patterns between frontal and motor regions that underlie the effects of motor invigoration. Identifying particular patterns of oscillatory connectivity that modulate motor vigor can provide insights into motor deficits observed in neurological and neuropsychiatric conditions associated with behavioral apathy.