Comparison of neuronal activity in the supplementary motor area and primary motor cortex

Tanji, Jun; Mushiake, Hajime

doi:10.1016/0926-6410(95)00039-9

Cited by 82 publications

(39 citation statements)

References 76 publications

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“…On the storage side, the insular activation in the verbal learning task, and the SMA activation in the maze-learning task, may represent regions where specific information for the performance of the task is stored. SMA might be a likely candidate for the storage of the sequential (40,41) and͞or temporal aspects of a motor sequence (22,23). Alternatively, SMA and insula may represent regions controlling access to that information, which might be stored elsewhere.…”

Section: Discussionmentioning

confidence: 99%

“…These three conditions paralleled the Naive, Practiced, and Novel conditions of the verb generation task. A control condition (Square Fast), comparable to the simple reading task, was used as the basic control for this study [for more complete analysis, see van Mier et al (22,23)]. In the Square Fast condition, subjects traced the simple square design as quickly as possible.…”

Section: Practice Effects On Maze Tracingmentioning

confidence: 99%

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The effects of practice on the functional anatomy of task performance

Petersen

Mier

Fiez

et al. 1998

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

473

271

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The effects of practice on the functional anatomy observed in two different tasks, a verbal and a motor task, are reviewed in this paper. In the first, people practiced a verbal production task, generating an appropriate verb in response to a visually presented noun. Both practiced and unpracticed conditions utilized common regions such as visual and motor cortex. However, there was a set of regions that was affected by practice. Practice produced a shift in activity from left frontal, anterior cingulate, and right cerebellar hemisphere to activity in Sylvian-insular cortex. Similar changes were also observed in the second task, a task in a very different domain, namely the tracing of a maze. Some areas were significantly more activated during initial unskilled performance (right premotor and parietal cortex and left cerebellar hemisphere); a different region (medial frontal cortex, ''supplementary motor area'') showed greater activity during skilled performance conditions. Activations were also found in regions that most likely control movement execution irrespective of skill level (e.g., primary motor cortex was related to velocity of movement). One way of interpreting these results is in a ''scaffolding-storage'' framework. For unskilled, effortful performance, a scaffolding set of regions is used to cope with novel task demands. Following practice, a different set of regions is used, possibly representing storage of particular associations or capabilities that allow for skilled performance. The specific regions used for scaffolding and storage appear to be task dependent. This paper describes practice-related changes in cerebral blood flow observed during procedural learning or skill acquisition. This type of learning is often gradual or iterative; in other words, skill is acquired over many trials, slowly moving toward a particular goal (1, 2). In some cases, the learning can take place with little attention directed to the learning events (3). It is ''nondeclarative'' in the sense that the person may not be able to explicate what has been learned or how, even though behavior has clearly changed over time. Neurobiologically, medial temporal lobe damage leaves this kind of learning essentially intact, but damage to other regions, such as the basal ganglia (4) and the cerebellum (5, 6), have been implicated in deficits in specific instances of nondeclarative learning.There are at least two different, but nonexclusive, ideas related to skill acquisition mechanisms. The first emphasizes that learning can take place through more efficient use of specific ''neuronal circuits.'' The idea of learning at a Hebbian synapse would be an example of this type of mechanism, where synaptic weight is strengthened when an input neuron and output neuron are active together. Some recent explications of procedural learning liken it almost to a type of high-level priming, in which existing structures are made efficient through recent use (e.g., ref. 4).The second idea emphasizes that skill acquisition goes through ''stag...

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Section: Discussionmentioning

confidence: 99%

Section: Practice Effects On Maze Tracingmentioning

confidence: 99%

The effects of practice on the functional anatomy of task performance

Petersen

Mier

Fiez

et al. 1998

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

473

271

View full text Add to dashboard Cite

show abstract

“…2D . More recently, these regions have been given functional labels now adopted by Tanji and colleagues under the premise that these regions are functionally disw x tinct 69,161,162,173,254,255,277,279 . These labels have also been used to organize data from functional imaging w x experiments carried out on humans 204 .…”

Section: Multiple Sub-areas Of Monkey Dmfcmentioning

confidence: 99%

Eye fields in the frontal lobes of primates

Tehovnik

Sommer

Chou

et al. 2000

Brain Research Reviews

264

197

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Two eye fields have been identified in the frontal lobes of primates: one is situated dorsomedially within the frontal cortex and will be Ž . referred to as the eye field within the dorsomedial frontal cortex DMFC ; the other resides dorsolaterally within the frontal cortex and is Ž . commonly referred to as the frontal eye field FEF . This review documents the similarities and differences between these eye fields. Although the DMFC and FEF are both active during the execution of saccadic and smooth pursuit eye movements, the FEF is more dedicated to these functions. Lesions of DMFC minimally affect the production of most types of saccadic eye movements and have no effect on the execution of smooth pursuit eye movements. In contrast, lesions of the FEF produce deficits in generating saccades to briefly presented targets, in the production of saccades to two or more sequentially presented targets, in the selection of simultaneously presented targets, and in the execution of smooth pursuit eye movements. For the most part, these deficits are prevalent in both monkeys and humans. Single-unit recording experiments have shown that the DMFC contains neurons that mediate both limb and eye movements, whereas the FEF seems to be involved in the execution of eye movements only. Imaging experiments conducted on humans have corroborated these findings. A feature that distinguishes the DMFC from the FEF is that the DMFC contains a somatotopic map with eyes represented rostrally and hindlimbs represented caudally; the FEF has no such topography. Furthermore, experiments have revealed that Ž . the DMFC tends to contain a craniotopic i.e., head-centered code for the execution of saccadic eye movements, whereas the FEF Ž . contains a retinotopic i.e., eye-centered code for the elicitation of saccades. Imaging and unit recording data suggest that the DMFC is more involved in the learning of new tasks than is the FEF. Also with continued training on behavioural tasks the responsivity of the DMFC tends to drop. Accordingly, the DMFC is more involved in learning operations whereas the FEF is more specialized for the execution of saccadic and smooth pursuit eye movements. q

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“…Numerous studies have suggested that the activity in motor areas during this period reflects preparation of movement (Kurata and Wise, 1988;Mitz et al, 1991;Tanji and Mushiake, 1996;Wise et al, 1998;Paz et al, 2003;Padoa-Schioppa et al, 2004).…”

Section: Premovement Variability and Motor Statesmentioning

confidence: 99%

Trial-to-Trial Variability of Single Cells in Motor Cortices Is Dynamically Modified during Visuomotor Adaptation

Mandelblat-Cerf¹,

Paz²,

Vaadia³

2009

J. Neurosci.

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Neurons in all brain areas exhibit variability in their spiking activity. Although part of this variability can be considered as noise that is detrimental to information processing, recent findings indicate that variability can also be beneficial. In particular, it was suggested that variability in the motor system allows for exploration of possible motor states and therefore can facilitate learning and adaptation to new environments. Here, we provide evidence to support this idea by analyzing the variability of neurons in the primary motor cortex (M1) and in the supplementary motor area (SMA-proper) of monkeys adapting to new rotational visuomotor tasks. We found that trial-to-trial variability increased during learning and exhibited four main characteristics: (1) modulation occurred preferentially during a delay period when the target of movement was already known, but before movement onset; (2) variability returned to its initial levels toward the end of learning; (3) the increase in variability was more apparent in cells with preferred movement directions close to those experienced during learning; and (4) the increase in variability emerged at early phases of learning in the SMA, whereas in M1 behavior reached plateau levels of performance. These results are highly consistent with previous findings that showed similar trends in variability across a population of neurons. Together, the results strengthen the idea that single-cell variability can be much more than mere noise and may be an integral part of the underlying mechanism of sensorimotor learning.

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Comparison of neuronal activity in the supplementary motor area and primary motor cortex

Cited by 82 publications

References 76 publications

The effects of practice on the functional anatomy of task performance

The effects of practice on the functional anatomy of task performance

Eye fields in the frontal lobes of primates

Trial-to-Trial Variability of Single Cells in Motor Cortices Is Dynamically Modified during Visuomotor Adaptation

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