Mu rhythm, visual processing and motor control

Sabaté, Magdalena; Llanos, Catalina; Enriquez, E.; Rodrı́guez, Manuel

doi:10.1016/j.clinph.2011.07.034

Cited by 34 publications

(23 citation statements)

References 47 publications

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“…Because the main body of alpha band activity recorded over the sensorimotor area is thought to be due to the mu rhythm (Salmelin and Hari, 1994; Leocani et al, 2001), fluctuations in the mu rhythm may play a significant role in controlling the cortical excitability in M1. In fact, it has been demonstrated that data processing improves when the phase of the mu rhythm is modified, and data processing is inhibited when its phase is unlocked (Sabate et al, 2012). Furthermore, the power of the mu rhythm in the sensorimotor area was recently demonstrated to play an important role in cortico-cortical connectivity (Ronnqvist et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, alpha oscillations are also relevant in the controlling of motor execution through the modulation of gamma-band activity (Yanagisawa et al, 2012b). This alpha oscillation in the sensorimotor cortex, i.e., mu rhythm, has been observed in relation to not only motor execution (Salmelin and Hari, 1994; Leocani et al, 2001) but also motor preparation (Pfurtscheller et al, 1997; Pineda, 2005) and motor imagery (Pfurtscheller et al, 2006; Llanos et al, 2013) as well as beta oscillations, and is considered a mechanism for improving information processing during these tasks (Basar et al, 2001; Palva and Palva, 2007; Sabate et al, 2012). Furthermore, functional connectivities within the range of alpha band activity are suggested to be related to physical and mental fitness (Douw et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Alpha band functional connectivity correlates with the performance of brainâ€“machine interfaces to decode real and imagined movements

Sugata

Hirata

Yanagisawa

et al. 2014

Front. Hum. Neurosci.

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Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain–machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alpha band functional connectivity correlates with the performance of brainâ€“machine interfaces to decode real and imagined movements

Sugata

Hirata

Yanagisawa

et al. 2014

Front. Hum. Neurosci.

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“…In particular, alpha oscillation and alpha band functional connectivity could be the target of sensorimotor processes (Neuper et al, 2006; Sabate et al, 2011, 2012; Lopes da Silva, 2013), also as they are closely involved in motor execution, motor imagery and visuomotor accuracy (Rilk et al, 2011; Sabate et al, 2011). In addition, the consistent report related to increased clustering of alpha networks has led to the model suggesting that alpha oscillation could carry elaborate information about the neurophysiological ongoing processes assuming also the role of local neuronal processing (Athanasiou et al, 2012, 2014, 2016).…”

Section: Introductionmentioning

confidence: 99%

Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study

Lasaponara

Mauro

Carducci

et al. 2017

Front. Hum. Neurosci.

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Quadrato Motor Training (QMT) is a new training paradigm, which was found to increase cognitive flexibility, creativity and spatial cognition. In addition, QMT was reported to enhance inter- and intra-hemispheric alpha coherence as well as Fractional Anisotropy (FA) in a number of white matter pathways including corpus callosum. Taken together, these results seem to suggest that electrophysiological and structural changes induced by QMT may be due to an enhanced interplay and communication of the different brain areas within and between the right and the left hemisphere. In order to test this hypothesis using the exact low-resolution brain electromagnetic tomography (eLORETA), we estimated the current neural density and lagged linear connectivity (LLC) of the alpha band in the resting state electroencephalography (rsEEG) recorded with open (OE) and closed eyes (CE) at three different time points, following 6 and 12 weeks of daily QMT. Significant changes were observed for the functional connectivity. In particular, we found that limbic and fronto-temporal alpha connectivity in the OE condition increased after 6 weeks, while it enhanced at the CE condition in occipital network following 12-weeks of daily training. These findings seem to show that the QMT may have dissociable long-term effects on the functional connectivity depending on the different ways of recording rsEEG. OE recording pointed out a faster onset of Linear Lag Connectivity modulations that tend to decay as quickly, while CE recording showed sensible effect only after the complete 3-months training.

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“…In addition to these findings, our results have shown that ␣-␥ coupling was predominant in the sensorimotor cortex during a motor task, adding further diversity to PAC. It should be noted that in the human sensorimotor cortex, the ␣ oscillation is also known as the rhythm, which is characterized by large amplitudes before movements and attenuation during movements (Sabate et al, 2012).…”

Section: Pac Does Not Represent Movement Typementioning

confidence: 99%

Regulation of Motor Representation by Phase–Amplitude Coupling in the Sensorimotor Cortex

Yanagisawa

Yamashita

Hirata³

et al. 2012

J. Neurosci.

130

131

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High-␥ amplitude (80 -150 Hz) represents motor information, such as movement types, on the sensorimotor cortex. In several cortical areas, high-␥ amplitudes are coupled with low-frequency phases, e.g., ␣ and (phase-amplitude coupling, PAC). However, such coupling has not been studied in the sensorimotor cortex; thus, its potential functional role has yet to be explored. We investigated PAC of high-␥ amplitude in the sensorimotor cortex during waiting for and the execution of movements using electrocorticographic (ECoG) recordings in humans. ECoG signals were recorded from the sensorimotor cortices of 4 epilepsy patients while they performed three different hand movements. A subset of electrodes showed high-␥ activity selective to movement type around the timing of motor execution, while the same electrodes showed nonselective high-␥ activity during the waiting period (Ͼ2 s before execution). Cross frequency coupling analysis revealed that the high-␥ amplitude during waiting was strongly coupled with the ␣ phase (10 -14 Hz) at the electrodes with movement-selective high-␥ amplitudes during execution. This coupling constituted the high-␥ amplitude peaking around the trough of the ␣ oscillation, and its strength and phase were not predictive of movement type. As the coupling attenuated toward the timing of motor execution, the high-␥ amplitude appeared to be released fromthe␣phasetobuildamotorrepresentationwithphase-independentactivity.OurresultssuggestthatPACmodulatesmotorrepresentation in the sensorimotor cortex by holding and releasing high-␥ activity in movement-selective cortical regions.

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Mu rhythm, visual processing and motor control

Cited by 34 publications

References 47 publications

Alpha band functional connectivity correlates with the performance of brainâ€“machine interfaces to decode real and imagined movements

Alpha band functional connectivity correlates with the performance of brainâ€“machine interfaces to decode real and imagined movements

Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study

Regulation of Motor Representation by Phase–Amplitude Coupling in the Sensorimotor Cortex

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