The Time Course of Changes during Motor Sequence Learning: A Whole-Brain fMRI Study

Toni, Ivan; Krams, Michael; Turner, Robert; Passingham, Richard E.

doi:10.1006/nimg.1998.0349

Cited by 369 publications

(295 citation statements)

References 27 publications

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“…Secondly, Huber et al (2004) recently reported that daytime learning of a motor adaptation task results in a discrete increase in the subsequent amount of NREM slow-wave activity over the parietal cortex, and that this slow-wave increase was proportional to the amount of delayed learning that developed the next day; signifying a potential link between overnight plastic changes in the parietal cortex and the degree of sleep-dependent improvement. Supporting these findings, and based on our current results, we hypothesize that as learning and task optimization is improved overnight (Kuriyama et al, 2004), there is a corresponding decreased need for conscious mapping of spatial relationships between finger movements and position (Seitz et al, 1990;Toni et al, 1998;Muller et al, 2002), the consequence of which is reduced post-sleep parietal involvement.…”

Section: Discussionsupporting

confidence: 83%

“…Firstly, decreased activity in the parietal lobes has been consistently reported in relation to enhanced motor skill learning (Seitz et al, 1990;Toni et al, 1998;Muller et al, 2002), a phenomena believe to reflect improved sequence automation. Secondly, Huber et al (2004) recently reported that daytime learning of a motor adaptation task results in a discrete increase in the subsequent amount of NREM slow-wave activity over the parietal cortex, and that this slow-wave increase was proportional to the amount of delayed learning that developed the next day; signifying a potential link between overnight plastic changes in the parietal cortex and the degree of sleep-dependent improvement.…”

Section: Discussionmentioning

confidence: 97%

“…These reports have described increased activity or responsiveness in the primary motor cortex (M1), and to a lesser extent, the pre-motor cortex, basal ganglia and cerebellum (Karni et al, 1995;Pascual-Leone et al, 1995;Penhune and Doyon, 2002). Enhanced motor skill learning has also been associated with signal decreases throughout the parietal cortex, thought to reflect automaticity of performance as skill level improves (Seitz et al, 1990;Toni et al, 1998;Muller et al, 2002;Sakai et al, 2002). Despite these findings, however, no study has yet explored the beneficial effect of a night of sleep on delayed motor-sequence learning and associated plastic brain changes, or examined alterations in brain plas-ticity following episodes of sleep relative to equivalent waking periods in an attempt to understanding the neural basis of sleep-dependent learning.…”

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confidence: 99%

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Sleep-dependent motor memory plasticity in the human brain

et al. 2005

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Abstract-Growing evidence indicates a role for sleep in off-line memory processing, specifically in post-training consolidation. In humans, sleep has been shown to trigger overnight learning on a motor-sequence memory task, while equivalent waking periods produce no such improvement. But while the behavioral characteristics of sleep-dependent motor learning become increasingly well characterized, the underlying neural basis remains unknown. Here we present functional magnetic resonance imaging data demonstrating a change in the representation of a motor memory after a night of sleep. Subjects trained on a motor-skill memory and 12 hours later, after either sleep or wake, were retested during functional magnetic resonance imaging. Following sleep relative to wake, regions of increased activation were expressed in the right primary motor cortex, medial prefrontal lobe, hippocampus and left cerebellum; changes that can support faster motor output and more precise mapping of key-press movements. In contrast, signal decreases were identified in parietal cortices, the left insular cortex, temporal pole and fronto-polar region, reflecting a reduced need for conscious spatial monitoring and a decreased emotional task burden. This evidence of an overnight, systems- A large body of evidence, spanning a wide range of neuroscientific disciplines, now describes evidence of sleepdependent learning in both humans and animals , already complemented by cellular and molecular models of sleep-dependent plasticity (Graves et al., 2001;Tononi and Cirelli, 2001;Benington and Frank, 2003). In particular, sleep has been implicated in the ongoing process of consolidation, following initial memory acquisition.Within the procedural memory domain, sleep in humans has been shown to trigger significant overnight learning enhancements, whereby performance is selectively improved across sleeping intervals, while equivalent waking periods confer no such performance benefit (for reviews see Walker, in press). Demonstrations of overnight, sleep-dependent learning have now been reported across both sensory (Karni et al., 1994;Gais et al., 2000; Stickgold et al., 2000a,b;Fenn et al., 2003;Atienza et al., 2004;Gaab et al., 2004) Regarding motor-sequence learning, Walker et al. (2002, 2003a,b) have shown that a night of sleep can trigger significant improvements in both performance speed and accuracy on a finger-tapping task, while equivalent periods of time awake do not result in any such learning enhancements. Furthermore, these overnight learning gains correlated with the amount of stage two non-rapid eye movement (NREM) sleep, particularly late in the night (Walker et al., 2002). Adding to these findings, it also appears that there is no transfer of sleep-dependent procedural learning to either new motor sequences, or to performance of the same sequence using the opposite hand (Fischer et al., 2002;Korman et al., 2003), suggesting that the influence of sleep is highly specific. But while the behavioral characteristics of sleep-dependent motor...

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 97%

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confidence: 99%

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Sleep-dependent motor memory plasticity in the human brain

et al. 2005

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“…Most interestingly for us, all involved brain areas, but particularly premotor areas, have been reported also to underlie the planning and production of motor sequences that follow an external sequential target stimulus, as particularly evident from imaging studies using the serial reaction task paradigm (Gordon et al, 1995;Grafton et al, 1995;Hazeltine et al, 1997;Hikosaka et al, 1998Hikosaka et al, , 1996Honda et al, 1998;Sadato et al, 1996;Sakai et al, 1998;Toni et al, 1998). As expected, the present outcome indicates that an attentively observed sequential signal can be a stimulus sufficient to elicit activations within a brain network closely related to that one that participates in sequential motor behavior.…”

Section: Discussionsupporting

confidence: 76%

A Blueprint for Target Motion: fMRI Reveals Perceived Sequential Complexity to Modulate Premotor Cortex

Schubotz

Cramon

2002

NeuroImage

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The execution of movements that are guided by an increasingly complex target motion is known to draw on premotor cortices. Whole-brain functional magnetic resonance imaging was used to investigate whether, in the absence of any movement, attending to and predicting increasingly complex target motion also rely on premotor cortices. Complexity was varied as a function of number of sequential elements and amount of dynamic sequential trend in a pulsing target motion. As a result, serial prediction caused activations in premotor and parietal cortices, particularly within the right hemisphere. Parametric analyses revealed that the right ventrolateral premotor cortex and the right anterior intraparietal sulcus were the only areas that, in addition, covaried positively with both behavioral and physical measures of sequential complexity. Further areas that covaried positively with increasing task difficulty reflected influences of both number and trend manipulation. In particular, increasing element number drew on dorsal premotor and corresponding posterior intraparietal regions, whereas increasing trend drew on the visual motion area and area V4. The present findings demonstrate that premotor involvement directly reflects perceptual complexity in attended and predicted target motion. It is suggested that when we try to predict how a target will move, the motor system generates a "blueprint" of the observed motion that allows potential sensorimotor integration. In the absence of any motor requirement, this blueprint appears to be not a byproduct of motor planning, but rather the basis for target motion prediction.

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“…In a series of studies with positron-emission tomography (6-9) and functional MRI (10)(11)(12), the area M1 consistently was found to be involved in the acquisition of motor skills. After motor-skill learning, the topography of representations in the motor cortex has been found to be altered (13)(14)(15).…”

Section: Discussionmentioning

confidence: 99%

Cortical correlates of learning in monkeys adapting to a new dynamical environment

Gandolfo

Benda

et al. 2000

Proc. Natl. Acad. Sci. U.S.A.

185

139

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In this paper, we describe the neural changes observed in the primary motor cortex of two monkeys while they learned a new motor skill. The monkeys had to adapt their reaching movements to external forces that interfered with the execution of their arm movements. We found a sizable population of cells that changed their tuning properties during exposure to the force field. These cells took on the properties of neurons that are involved in the control of movement. Furthermore, the cells maintained the acquired activity as the monkey readapted to the no-force condition. Recent imaging studies in humans have reported the effects of motor learning in the primary motor cortex. Our results are consistent with the findings of these studies and provide evidence for single-cell plasticity in the primary motor cortex of primates.I n a number of recent studies investigators have shown that when networks of neurons are repeatedly exposed to sensorymotor associations, learning of motor tasks occurs. Primates learn a new task as the result of repeated exposures to sensory signals coming from a variety of visual and proprioceptive sources. The sensory inputs are funneled to the motor areas of the central nervous system each time a movement is produced. The current view is that learning results from a change in the internal structure of the cortical and subcortical networks brought about by sensory and recurrent signals.Presumably, the iterative sensory-motor process leads to the establishment of an internal model of the controlled dynamics through a gradual change of the synaptic strength (1) of the neurons of the cortical and subcortical motor areas. The internal model is embedded in the newly formed connectivity of a group of neurons, and the activity of these trained neurons generates the neural impulses necessary for the execution of the learned motor task. According to this view, motor learning and the control of dynamics are two facets of the same process. In this paper, we describe the cellular changes in the circuitry of the primary cortical motor area of the monkey during the acquisition of a motor skill.In the experiments described here, a key feature of the task to which the monkeys were exposed involved a change in the mechanical environment with which their hand interacted. Because of this change, the neural representation of the arm would have to develop a new model to deal with the new dynamics of the environment. In this paper, we present psychophysical evidence for the formation of this new internal model and we describe the neural changes observed in the primary motor cortex as the new model was formed. MethodsDescription of the Motor Task. Two monkeys (Macaca nemestrina) were trained to grasp the manipulandum of a 2-df, lightweight, low-friction robot with a force-torque transducer mounted on the handle. Two torque motors were mounted on the base of the robot and produced force fields upon the hand of the monkey as the animal performed reaching movements (see ref. 2 for details).During an experimental sessio...

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The Time Course of Changes during Motor Sequence Learning: A Whole-Brain fMRI Study

Cited by 369 publications

References 27 publications

Sleep-dependent motor memory plasticity in the human brain

Sleep-dependent motor memory plasticity in the human brain

A Blueprint for Target Motion: fMRI Reveals Perceived Sequential Complexity to Modulate Premotor Cortex

Cortical correlates of learning in monkeys adapting to a new dynamical environment

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