The learned modulation M1 activity was accompanied by a change in striatum activity. Over the course of the training, neurons in the dorsal striatum increased their activity and more of them became sensitive to the task. Neuronal firing in M1 and striatal neurons became more synchronized as well. In addition, the absence of long-term potentiation (a key component of plasticity) in the striatum prevented mice from learning the abstract skill task and prevented any striatal plasticity, i.e. there was no increase in striatal neural activity and no increase in synchronization between M1 and the striatum. These observations highlight the causal role of corticostriatal plasticity in abstract skill learning.
In summary, Koralek and colleagues explored, for the first time, the neural substrate of learning neuroprosthetic control and demonstrated the importance of the corticostriatal pathway for this task.
It brought me a few thoughts:
1) The time course of abstract skill learning (fast initial phase and slow later phase) is very similar to what has been observed in saccade or reaching adaptation (Ethier et al. 2008, Smith et al. 2006) or in skill learning task (Karni et al. 1995). In motor adaptation, the cerebellum is known to drive this initial fast phase of the learning (Xu-Wilson et al. 2009) by modelling the mapping between M1 activity (motor commands of the movements) and the resulting motion of the arms (i.e. by adapting the forward model, Shadmehr et al. 2010). I wonder if the cerebellum plays the same role in the abstract skill learning. Namely, does the cerebellum model the mapping between neural activity of M1 neurons and the modulation of the auditory cursor pitch during the initial fast phase?
2) M1 is a logical place to study abstract skill learning because those neurons are also able to control a prosthetic arm and to adapt their firing patterns to maintain an accurate control of it (Jarosiewicz et al. 2008). However, neurons from other areas can also be used for neuroprosthetic (Hwang and Andersen 2010). Therefore, it is possible that the abstract skill learning task can be implemented with neural ensembles from any brain areas or even with neural ensembles from different brain areas.
3) The abstract skill learning made me think of realtime fMRI neurofeedback in which human subjects have to modulate the BOLD activity of a given brain area and are getting feedback about this level of activity (Yoo and Jolesz 2002). The Koralek paper might be a good model of this task and could be used to model learning of realtime neurofeedback in humans.
Any other thoughts?
Ethier, V., Zee, D. S., & Shadmehr, R. (2008). Spontaneous recovery of motor memory during saccade adaptation. Journal of neurophysiology, 99(5), 2577-83. doi:10.1152/jn.00015.2008
Smith, M. A., Ghazizadeh, A., & Shadmehr, R. (2006). Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS biology, 4(6), e179. doi:10.1371/journal.pbio.0040179
Xu-Wilson, M., Chen-Harris, H., Zee, D. S., & Shadmehr, R. (2009). Cerebellar contributions to adaptive control of saccades in humans. Journal of Neuroscience, 29(41), 12930-9. doi:10.1523/JNEUROSCI.3115-09.2009
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010). Error Correction, Sensory Prediction, and Adaptation in Motor Control. Annual review of neuroscience. doi:10.1146/annurev-neuro-060909-153135
Jarosiewicz, B., Chase, S. M., Fraser, G. W., Velliste, M., Kass, R. E., & Schwartz, A. B. (2008). Functional network reorganization during learning in a brain-computer interface paradigm. Proceedings of the National Academy of Sciences of the United States of America, 105(49), 19486-91. doi:10.1073/pnas.0808113105
Hwang, E. J., & Andersen, R. a. (2010). Cognitively driven brain machine control using neural signals in the parietal reach region. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2010, 3329-32. doi:10.1109/IEMBS.2010.5627277
Yoo, S.-S., & Jolesz, F. a. (2002). Functional MRI for neurofeedback: feasibility study on a hand motor task. Neuroreport, 13(11), 1377-81.