Neural Substrates Related to Motor Memory with Multiple Timescales in Sensorimotor Adaptation

Recent computational and behavioral studies suggest that motor adaptation results from the update of multiple memories with different timescales. Here, we designed a model-based functional magnetic resonance imaging (fMRI) experiment in which subjects adapted to two opposing visuomotor rotations. A computational model of motor adaptation with multiple memories was fitted to the behavioral data to generate time-varying regressors of brain activity. We identified regional specificity to timescales: in particular, the activity in the inferior parietal region and in the anterior-medial cerebellum was associated with memories for intermediate and long timescales, respectively. A sparse singular value decomposition analysis of variability in specificities to timescales over the brain identified four components, two fast, one middle, and one slow, each associated with different brain networks. Finally, a multivariate decoding analysis showed that activity patterns in the anterior-medial cerebellum progressively represented the two rotations. Our results support the existence of brain regions associated with multiple timescales in adaptation and a role of the cerebellum in storing multiple internal models.A model-based functional MRI study reveals the existence of brain regions associated with four distinct timescales in motor adaptation.Author Summary: Motor adaptation, a form of motor learning in which motor commands are modified to compensate for disturbances in the external environment, usually proceeds at a rapid pace initially and is then followed by more gradual adjustments. This suggests that at least two learning processes are involved, but little is known about how many distinct memories the brain actually updates during motor adaptation. In addition, it is unclear whether these putative multiple motor memories reside within a single neural system that encompasses different timescales or in qualitatively distinct neural systems. We addressed these issues using a model-based functional imaging approach in which we first used behavioral data to derive a large number of possible memory “states,” each with different dynamics, and then correlated these memory states with neural activities. We identified four components: two fast, one intermediate, and one slow, each associated with different brain networks. In particular, areas in the prefrontal and parietal lobes and the posterior part of the cerebellum were associated with formation of memories for short timescales. By contrast, the inferior parietal region and the anterior-medial cerebellum were associated with formation of memories for intermediate and long timescales, respectively.