Abstract
Interacting with our ever-changing physical environment requires continual recalibration of the motor system. One mechanism by which this occurs is motor adaptation. Understanding how motor adaptation is implemented by the human brain, how different regions work in concert to retain movement accuracy, and how this function is linked to metabolic use of neurochemicals poses an important challenge in neuroscience. In humans, motor sequence learning is related to concentration of γ-aminobutyric acid (GABA) in the primary motor cortex (M1). However, the role of M1 GABA in adaptation - where behaviour is thought to be acquired outside M1, but retained within M1 - is unclear. Here, we used an ultra-high field MR multimodal acquisition to address the hypothesis that M1 GABA and M1-Cerebellar functional connectivity would relate to retention of adaptation, but not acquisition of adaptation. Methods. In this within-subject, crossover study, we quantified [GABA] and [Glutamate] from the hand region of the left human primary motor cortex (M1) using ultra-high-field magnetic resonance spectroscopy (7T-MRS) while participants (n=15; mean age 26 ± 3 years, range 22-32 years) performed a visuomotor task with or without an adaptation component (control condition vs. cursor rotation condition). In the rotation condition, participants were required to adapt their centrifugal shooting movements to a rotation of the visual feedback which increased stepwise by 10 degrees after every block of 40 trials in order to drive adaptation throughout the duration of the scanning session (Fig 1, middle panel). To probe retention participants performed a washout behavioural task after each MR session. We collected resting-state fMRI data immediately before and after the task. Results. In the rotation condition, participants were able to adapt to the stepwise increasing rotation (Fig.1, lower panel), indicated by a significant effect of epoch (χ2(1) = 55.597, p < 0.01) in a linear mixed effect model of error in the first and last epoch across all blocks. Participants retained the previously learned adaptive movement in the first block of the washout (one-sample t-test of error in first block of washout in rotation condition t = -9.635, p < 0.01; red line in Fig.1 lower right panel). We first replicated changes in functional connectivity known to occur in response to adaptation (Albert et al., 2009) to validate the adaptation paradigm - adaptation increased functional connectivity in a cerebellar network (Fig. 2A). Change in strength of functional connectivity within the cerebellar network correlated with adaptation error, such that an increase in network strength was associated with better adaptation (r = -0.648, p = 0.016; Fig. 2C). We next tested our specific hypothesis regarding a link between M1 [GABA], M1-Cerebellar connectivity and retention. We found that higher baseline M1 [GABA] relates to greater retention of adaptation (r = -0.62 p = 0.02; Fig 3A left) but does not relate to adaptation-acquisition (r = 0.07 p = 0.82; Fig. 3A right). Moreover, M1-Cerebellar connectivity change is associated with retention (r= 0.68, p = 0.01; Fig. 3C left), but not adaptation (r = 0.05, p = 0.87; Fig. 3C right). Finally, M1 [GABA] relates to M1-Cerebellar connectivity change (r = -0.63, p = 0.03; Fig 3D left). We further explored whether the link between M1 [GABA] and retention might be mediated by M1- Cerebellar connectivity change. A mediation analysis revealed that M1-Cerebellar connectivity change mediates the relationship between M1 GABA and retention (confidence interval for mediation path coefficient does not include zero; ab= -23.87; 95%-CI[-62.2925, -1.8002]; Fig 4). Conclusions. In summary, our results showed that (a) participants are able to adapt to a stepwise increasing rotation, (b) this adaptation process increases connectivity in a cerebellar network and (c) retention of the adapted state is associated with baseline M1 [GABA]. The relationship between M1 [GABA] and retention is mediated by M1-Cerebellar connectivity change.
Caroline Nettekoven
Postdoctoral Researcher
I am interested in the neural basis of complex behaviour. To study this, I use neuroimaging techniques, computational modelling of behaviour and brain stimulation.