Relation of bimanual coordination to activation in the sensorimotor cortex and supplementary motor area: Analysis using functional magnetic resonance imaging

Toyokura, Minoru; Muro, Isao; Komiya, Taizo; Obara, Makoto

doi:10.1016/s0361-9230(98)00165-8

Cited by 92 publications

(59 citation statements)

References 32 publications

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“…Lesions in this area impair motor control (29). The effect seen in the medial wall, comprising the posterior (behind the anterior commissure) SMA and a locus in the cingulate gyrus bilaterally, again is in accordance with results of previous nonparametric neuroimaging studies (24,30,31). SMA lesions have been shown to impair bimanual coordination (32,33).…”

Section: Discussionsupporting

confidence: 86%

Transitions between dynamical states of differing stability in the human brain

Meyer‐Lindenberg

Ziemann

Hajak

et al. 2002

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

170

130

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What mechanisms underlie the flexible formation, adaptation, synchronization, and dissolution of large-scale neural assemblies from the 10 10 densely interconnected, continuously active neurons of the human brain? Nonlinear dynamics provides a unifying perspective on self-organization. It shows that the emergence of patterns in open, nonequilibrium systems is governed by their stability in response to small disturbances and predicts macroscopic transitions between patterns of differing stability. Here, we directly demonstrate that such transitions can be elicited in the human brain by interference at the neural level. As a probe, we used a classic motor coordination paradigm exhibiting well described movement states of differing stability. Functional neuroimaging identified premotor (PMA) and supplementary motor (SMA) cortices as having neural activity linked to the degree of behavioral instability. These regions then were transiently disturbed with graded transcranial magnetic stimulation, which caused sustained and macroscopic behavioral transitions from the less stable out-of-phase to the stable in-phase movement, whereas the stable pattern could not be affected. Moreover, the strength of the disturbance needed (a measure of neural stability) was linked to the degree of behavioral stability, demonstrating the applicability of nonlinear system theory as a powerful predictor of the dynamical repertoire of the human brain.I maging of the living human brain routinely reveals patterns corresponding to the synchronized action of billions of neurons (1). The brain's formation of these large-scale distributed neural assemblies and the rapid transition between them is a striking example of self-organization. In complex systems far from equilibrium, nonlinear systems theory has proposed that the decisive parameter governing such collective emergent behavior is stability to small-scale disturbances; which dynamic patterns get selected from the vast array of possible combinations depends on their relative stability (2). Nonlinear systems exhibit pronounced susceptibility to small disturbances due to a hallmark property called ''sensitive dependence on initial conditions,'' meaning that minute changes to the system's state result in large-scale alterations.A further fundamental prediction of this approach is that self-organization depends on the occurrence of sudden macroscopic transitions between states of differing stability (usually called phase transitions; ref. 3). Therefore, if the presence of transitions between differentially stable patterns could be established, this would uncover a fundamental determinant of the dynamic repertoire of the central nervous system (4). Consequently, transition, synchronization and stability phenomena in neuronal membranes, cells, and small assemblies have been studied intensively (5-7). In humans, correlative evidence comes from the observation of electrophysiological changes as a consequence of alterations of stability in motor behavior, an area to which this theory has been applie...

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Section: Discussionsupporting

confidence: 86%

Transitions between dynamical states of differing stability in the human brain

Meyer‐Lindenberg

Ziemann

Hajak

et al. 2002

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

170

130

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“…During bimanual coordination, higher activation in the SMA and the right PMd during the parallel mode compared to the mirror-symmetrical mode is a well-replicated finding (Sadato et al, 1997;Toyokura et al, 1999;Immisch et al, 2001;Meyer-Lindenberg et al, 2002;Ullen et al, 2003;Debaere et al, 2004, Wenderoth et al, 2005, Aramaki et al, 2006a, which highlights the important role of the SMA and PMd in bimanual coordination. A TMS study in humans revealed that inter-hemispheric PMd-to-M1 interactions added to the M1-to-M1 interaction (Baumer et al, 2006).…”

Section: Possible Pathways For Interhemispheric Interactionmentioning

confidence: 72%

Asymmetric control mechanisms of bimanual coordination: An application of directed connectivity analysis to kinematic and functional MRI data

Maki¹,

Wong²,

Sugiura³

et al. 2008

NeuroImage

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Mirror-symmetrical bimanual movement is more stable than parallel bimanual movement. This is well established at the kinematic level. I used functional MRI (fMRI) to evaluate the neural substrates of the stability of mirror-symmetrical bimanual movement. Right-handed participants (n = 17) rotated disks with their index fingers bimanually, both in mirror-symmetrical and asymmetrical parallel modes. I applied the Akaike causality model to both kinematic and fMRI time-series data. I hypothesized that kinematic stability is represented by the extent of neural "cross-talk": as the fraction of signals that are common to controlling both hands increases, the stability also increases. The standard deviation of the phase difference for the mirror mode was significantly smaller than that for the parallel mode, confirming that the former was more stable. I used the noise-contribution ratio (NCR), which was computed using a multivariate autoregressive model with latent variables, as a direct measure of the cross-talk between both the two hands and the bilateral primary motor cortices (M1s). The mode-by-direction interaction of the NCR was significant in both the kinematic and fMRI data. Furthermore, in both sets of data, the NCR from the right hand to the left was more prominent than vice versa during the mirror-symmetrical mode, whereas no difference was observed during parallel movement or rest. The asymmetric interhemispheric interaction from the left M1 to the right M1 during symmetric bimanual movement might represent cortical-level cross-talk, which contributes to the stability of symmetric bimanual movements.4

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“…Important roles for the medial wall motor areas in spatial bimanual coordination have been firmly established with a variety of techniques, including electrophysiological recordings (Brinkman and Porter 1979;Donchin et al 1998;Tanji et al 1987), lesion studies in nonhuman primates (Brinkman 1981(Brinkman , 1984 and humans (Bleasel et al 1996;Stephan et al 1999a), and neuroimaging recordings (Ehrsson et al 2000b;Goerres et al 1998;Sadato et al 1997;Stephan et al 1999a,b;Toyokura et al 1999). The SMA and preSMA have also been implicated in movement sequence control (see e.g., Sadato et al 1996;Tanji and Shima 1994), and in explicit timing functions (Halsband et al 1993;Ullén et al 2001).…”

Section: Polyrhythmic Coordinationmentioning

confidence: 99%

“…Neurobiological studies of bimanual movements have largely focused on the spatial coordination of continuous movement trajectories (see e.g., Sadato et al 1997;Stephan et al 1999a,b;Toyokura et al 1999). To specifically study temporal coordination, it is important to use rhythmic tasks with brisk discrete movements, where spatial coordination demands are minimized.…”

Section: Introductionmentioning

confidence: 99%

Rhythm and Beat Perception in Motor Areas of the Brain

Grahn¹,

Brett²

2007

Journal of Cognitive Neuroscience

837

872

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. Without practice, bimanual movements can typically be performed either in phase or in antiphase. Complex temporal coordination, e.g., during movements at different frequencies with a noninteger ratio (polyrhythms), requires training. Here, we investigate the organization of the neural control systems for in-phase, antiphase, and polyrhythmic coordination using functional magnetic resonance imaging (fMRI). Brisk rhythmic tapping with the index fingers was used as a model behavior. We demonstrate different patterns of brain activity during in-phase and antiphase coordination. In-phase coordination was characterized by activation of the right anterior cerebellum and cingulate motor area (CMA). Antiphase coordination was accompanied by extensive fronto-parieto-temporal activations, including the supplementary motor area (SMA), the preSMA, and the bilateral inferior parietal gyri, premotor cortex, and superior temporal gyri. When contrasting polyrhythmic tapping with in-phase tapping, activity was seen in the same set of brain regions, and in the posterior cerebellum and the CMA. Antiphase and polyrhythmic coordination may thus to a large extent use common neural control circuitry. In a separate experiment, we analyzed the neural control of the rhythmic structure and the serial order of finger movements during polyrhythmic tapping. Polyrhythmic tapping was compared with an isochronous coordination pattern that retained the same serial order of finger movements as the polyrhythm. This experiment showed that the preSMA and the bilateral superior temporal gyri may be crucial for the rhythmic control of polyrhythmic tapping, while the cerebellum, the CMA, and the premotor cortices presumably are more involved in the ordinal control of the sequence of finger movements.

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Relation of bimanual coordination to activation in the sensorimotor cortex and supplementary motor area: Analysis using functional magnetic resonance imaging

Cited by 92 publications

References 32 publications

Transitions between dynamical states of differing stability in the human brain

Transitions between dynamical states of differing stability in the human brain

Asymmetric control mechanisms of bimanual coordination: An application of directed connectivity analysis to kinematic and functional MRI data

Rhythm and Beat Perception in Motor Areas of the Brain

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