As the decrease in activation in areas projecting to M1 occurred over a similar time window as the switch in ordering effects that we observed in M1 within the first session, we proposed that this switch reflects changes in modulatory inputs to M1. This initial phase in the acquisition of the skill may be conceptualized as the setting up of a sequence-specific routine (6). Our working hypothesis is that, initially, the evoked response in M1 relates to the component movements of the sequences, which being identical, exert a smaller, i.e., habituated, response on repetition across a time window of 40 sec. By the end of the session, however, after the two sequences each have been repeated a few tens of times, the switch in ordering effect reflects the fact that a given sequence of movements constitutes a special entity of behavioral significance: it is consistently performed as a sequence rather than as unordered component movements. An experience-dependent change from representation of component movements in an explicit sequence to a representation, rather “automatic” (45, 48, 60), in M1 of the sequence as a unitary motor plan can be related to the decrease of activation in the cerebellum and prefrontal cortex through a decreasing need for movement by movement internal monitoring.
Although important changes occur on a short time scale, our results clearly demonstrate that skilled performance of the trained sequence is not the product of a single training session. Both the imaging and the behavioral data show that the initial changes in ordering effects and the gains in performance acquired during the first session were retained after the session and then consolidated; however, it took about 3 weeks of practice on a daily basis for performance to approach asymptote. The correlate of this acquired proficiency was an enlarged representation of the trained, relative to the untrained, sequence in M1. The emergence of this differential in the evoked fMRI signal corresponded in time to the attainment of maximal near asymptotic performance on the trained sequence. This, however, may be a result of a limitation in the sensitivity of our measurement, and it remains to be seen whether a differential representation of the trained sequence begins to evolve even earlier than the attainment of asymptotic performance. Nevertheless, our results have provided what we believe is direct evidence that long-term motor training can result in significant experience-dependent reorganization in the adult human motor cortex. These data provide an important link with a growing body of data in the nonhuman mammalian brain of representational changes associated with the acquisition of skills.
Two main mechanisms have been proposed for the changes induced in motor and sensory representational maps as a function of experience: (i) the transcription dependent improvement and growth of new connections and synapses (e.g., 34, 63); and (ii) the unmasking, or disinhibition, of previously existing lateral connections between neurons within a representational domain through internal or external modulating inputs (3, 30, 64). The latter mechanism can induce changes on a short time scale and may subserve fast learning; the former has been invoked to explain the delayed, time-dependent nature of developmental cortical plasticity and cortical reorganization compensating for injury and subserving learning. These mechanisms are not mutually exclusive, however, and one may conjecture that the pre-existing lateral connections between local populations of neurons, whose outputs result in different sets of movements, provide a basic network that short-term experience may unmask and subsequent practice may selectively improve (63, 65). Thus, our results support the idea that adult skill motor learning is contingent on the functional architecture of the motor system but, at the same time, modifies it.
The human imaging data together with the behavioral measurements of the effects of training over time lead to three important insights into the neurobiological substrates of skill learning in the adult brain. First, practice can set in motion neural processes that continue to evolve many hours after practice has ended. Thus, even a limited training experience can induce behaviorally significant changes in brain activity, and initiate important long-term effects that may provide the basis for the consolidation of the experience. Second, although many brain areas may be important in the initial stages of acquiring a new skill, an important substrate of skill proficiency can be an enlarged, better representation within the earliest level of processing in which a differential representation of those experience parameters that are critical for the performance of the task is available. This may be a basis for the specificity of procedural knowledge for basic parameters of the training experience. It is very likely the case that different parts of the distributed motor system, including subcortical structures, take part and subsequently represent acquired skills. Nevertheless, the data are consistent with the proposal that local changes in discrete representations subserve the long-term memory of skills. Third, motor skill learning requires time and has two distinct phases, analogous to those subserving perceptual skill learning. An initial, fast improvement phase (“fast learning”) is followed by a slowly evolving, post-training incremental performance gains (“slow learning”). The hypothesis is that fast learning involves processes that select and establish an optimal routine or plan for the performance of the given task. Slow learning, on the other hand, may reflect the ongoing long-term, perhaps structural, modifications of basic motor modules; it may be implemented through time-dependent strengthening of links between motor neurons as a function of correlated activity, and their recruitment into a specific representation of the trained sequence of movements.
1. Kaas, J.H. (1991) Annu. Rev. Neurosci. 14, 137–167.
2. Merzenich, M.M. & Sameshima, K. (1993) Curr. Opin. Neurobiol. 3, 187–196.
3. Donoghue, J.P., Hess, G. & Sanes, J.N. (1996) in Acquisition of Motor Behavior in Vertebrates, eds. Bloedel, J., Ebner, T. & Wise, S.P. (MIT Press, Cambridge, MA), pp. 363–386.
4. Aizawa, H., Inase, M., Mushiake, H., Shima, K. & Tanji, J. (1993) Exp. Brain Res. 84, 668–671.
5. Le Bihan, D. & Karni, A. (1995) Curr. Opin. Neurobiol. 5, 231– 237.
6. Karni, A., Meyer, G., Jezzard, P., Adams, M., Turner, R. & Ungerleider, L.G. (1995) Nature (London) 377, 155–158.
7. Nudo, R., Millike, G.W., Jenkins, W.M. & Merzenich M.M. (1996) J. Neurosci. 16, 785–807.
8. Karni, A. & Sagi, D. (1993) Nature (London) 365, 250–252.
9. Karni, A., Tanne, D., Ruhenstein, B.S., Askenasy, J.J.M. & Sagi, D. (1994) Science 265, 679–682.
10. Kandel, E.R. & Schwartz, J.H. (1982) Science 218, 433–443.
11. Dudai, Y. (1996) Neuron 17, 367–370.
12. Mishkin, M., Malamut, B. & Bachevalier, J. (1988) in The Neurobiology of Learning and Memory, eds. Lynch, G., McGaugh, J.L. & Weinberger, N.M. (Guilford, New York), pp. 64–88.
13. Squire, L.R. (1986) Science 232, 1612–1619.
14. Schacter, D.L. & Tulving, E.T. (1994) Memory Systems (MIT Press, Cambridge, MA).
15. Weizkrantz, L. (1990) Philos. Trans. R. Soc. London Biol. 329, 99– 108.
16. Fiorentini, A. & Berardi, N. (1982) Vision Res. 21, 1149–1158.
17. Ball, K. & Sekuler, R. (1987) Vision Res. 27, 953–965.
18. Karni, A. & Sagi, D. (1991) Proc. Natl. Acad. Sci. USA 88, 4966– 4970.
19. Gandolfo, F., Mussa-Ivaldi, F.A. & Bizzi, E. (1996) Proc. Natl. Acad. Sci. USA 93, 3843–3846.