Voluntary control of corticomuscular coherence through neurofeedback: A proof-of-principle study in healthy subjects

Carlowitz-Ghori, Katherina von; Bayraktaroğlu, Zübeyir; Waterstraat, Gunnar; Curio, Gabriel; Nikulin, Vadim V.

doi:10.1016/j.neuroscience.2015.01.013

Cited by 24 publications

(19 citation statements)

References 51 publications

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“…For example, Ushiyama and colleagues measured CMC during isometric contraction of the tibialis anterior muscle on 100 healthy young individuals and found only 46 of 100 subjects showing significant CMC (Ushiyama et al ., ). During upper limb motor tasks, significant CMC can be detected in around 80–90% healthy population (Mima & Hallett, ; Grosse et al ., ; Kristeva et al ., ; Mendez‐Balbuena et al ., ; Witham et al ., ; Campfens et al ., ; von Carlowitz‐Ghori et al ., ). However, testing on a small population, Mendez‐Balbuena and colleagues found large interindividual differences in the strength and bandwidth of CMC during low‐level static and dynamic forces generated by forearm muscles (Mendez‐Balbuena et al ., ).…”

Section: Corticomuscular Coherence: What We Know and What Did We Miss?mentioning

confidence: 97%

Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity

Yang

Dewald

Helm

et al. 2017

Eur J of Neuroscience

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Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending somatosensory feedback and descending motor commands. However, corticomuscular coherence cannot separate this bidirectionality. Furthermore, the sensorimotor system is nonlinear, resulting in cross-frequency coupling. Cross-frequency oscillations cannot be assessed nor exploited by linear measures. Here, we emphasise the need of novel coupling measures, which provide directionality and acknowledge nonlinearity, to unveil neural coupling in the sensorimotor system. We highlight recent advances in the field and argue that assessing directionality and nonlinearity of neural coupling will break new ground in the study of the control of movement in healthy and neurologically impaired individuals.

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Section: Corticomuscular Coherence: What We Know and What Did We Miss?mentioning

confidence: 97%

Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity

Yang

Dewald

Helm

et al. 2017

Eur J of Neuroscience

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“…In the present study, we used regression CMC (r-CMC), a multivariate method introduced by Bayraktaroglu, et al 8 , which has been used in a number of previous studies 8,76,77 . This method finds spatial filters for EEG maximizing the synchronization between EEG and EMG activity at a specific frequency range simultaneously using information from all available EEG channels.…”

Section: Regression-cmc Analysis (R-cmc)mentioning

confidence: 99%

Corticomuscular interactions during different movement periods in a multi-joint compound movement

Kenville¹,

Maudrich²,

Vidaurre³

et al. 2020

Sci Rep

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While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.Many muscles are involved in the execution and control of a bipedal squat (BpS) 1-3 , with Solomonow, et al. 4 estimating over 200 muscles to be recruited. In contrast to simple movements, BpS, as well as compound everyday life activities, i.e. walking stairs, picking up loads or carrying loads across distance, require extensive intra-and interlimb coordination 5 , which is why BpS is an ideal, naturalistic model for compound movement control. In contrast to simple movements, voluntary control of each muscle during BpS seems unlikely, especially when considering varying requirements on acting muscles due to changes in muscle function throughout different movement periods. Movement periods, i.e. dynamic (eccentric (ECC) and concentric (CON)) and static (isometric (ISO)) contraction periods require all muscles involved to dynamically change their function throughout the movement. This is evident for example from elevated proprioception, reflected by increased muscle spindle activities during eccentric contractions compared to both isometric and concentric contractions 6,7 . Thus, BpS demands continuous, extensive central-nervous information integration while placing high physical stress on the body 1-3 , signifying that motor commands have to be flexibly deployed and adapted throughout this movement in order to enable successful execution. Frequency band related neural synchrony between brain regions and muscles, detectable through corticomuscular coherence (CMC) measurements 8,9 , potentially provides an efficient solution to the challenge of enabling dynamic information processing on a whole body level during compound movement control. Indeed...

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“…This finding indicates that strengthening functional connectivity within the sensorimotor system might boost relating BCI performance. Up-regulation of functional connectivity via neurofeedback has recently been demonstrated in a study on corticomuscular coherence, (von Carlowitz-Ghori et al, 2015). We hypothesize that the up-regulation of functional connectivity between S1 and M1 can enhance further BCI performance via strengthening the communication between neuronal populations involved in motor imagery.…”

Section: Discussionmentioning

confidence: 76%

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Vidaurre

Haufe

Jorajuría

et al. 2020

Preprint

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Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms as measured by means of electroen-cephalography (EEG), about 20% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode user’s intentions requires the identification of neuro-physiological factors determining ‘good’ and ‘poor’ BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post-as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sen-sorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

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Voluntary control of corticomuscular coherence through neurofeedback: A proof-of-principle study in healthy subjects

Cited by 24 publications

References 51 publications

Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity

Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity

Corticomuscular interactions during different movement periods in a multi-joint compound movement

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

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