Person:

Gonzalez Castro, L. Nicolas

Understanding the neural processes underlying motor learning in humans is important to facilitate the acquisition of new motor skills and to aid the relearning of skills lost after neurologic injury. Although it is known that the learning of a new movement is guided by the error feedback received after each repeated attempt to produce the movement, how the central nervous system (CNS) processes individual errors and how it modulates its learning rate in response to the history of errors experienced are issues that remain to be elucidated. To address these issues we studied the generalization of learning and learning decay – the transfer of what has been learned, or unlearned, in a particular movement condition to new movement conditions. Generalization offers a window into the process of error credit assignment during motor learning, since it allows us to measure which actions benefit the most in terms of learning after experiencing an error. We found that the distributions that describe generalization after learning are unimodal and biased towards the motion directions experienced during training, a finding that suggests that the credit for the learning experienced after a particular trial is assigned to the actual motion (motion-referenced learning) and not to the planned motion (plan-referenced learning) as it had previously been assumed in the motor learning literature. In addition, after training the same action along multiple directions, we found that the pattern of learning decay has two distinct components: one that is time-dependent and affects all trained directions, and one that is trial-dependent and affects mostly the direction where decay was induced, generalizing narrowly with a unimodal pattern similar to the one observed for learning generalization. We finally studied the effect that the consistency of the error perturbations in the training environment has on the learning rate adopted by the CNS. We found that learning rate increases when the perturbations experienced in training are consistent, and decreases when these perturbations are inconsistent. Besides increasing our understanding of the mechanisms underlying motor learning, the findings described in the present dissertation will enable the principled design of skill training and rehabilitation protocols that accelerate learning.

In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned 'credit' for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.

Single cell RNA-sequencing revealed extensive transcriptional cell state diversity in cancer, often observed independently from genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, we performed multi-omics single-cell profiling – integrating DNA methylation, transcriptome, and genotyping within the same cells – of diffuse gliomas, tumors governed by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories, and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to measure cell state heritability and transition dynamics based on high resolution lineage trees directly in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal vs. plastic cell state architectures in IDH-mutant glioma vs. IDH-wildtype glioblastoma, respectively. This work provides a novel framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.