Influence of motor imagination on cortical activation during functional electrical stimulation

Reynolds, Clare M.; Osuagwu, B.A.; Vučković, Aleksandra

doi:10.1016/j.clinph.2014.10.007

Cited by 57 publications

(43 citation statements)

References 33 publications

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“…They were asked to imagine and to sense the opening of their left hand, i.e., finger extension, from a first person perspective without actually moving it. Notably, they were instructed to continue with MI even when FES occurred, because MI has been shown to amplify FES-related ERD when applied concurrently and not only when triggering the peripheral stimulation (Reynolds et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this context, MI-related oscillatory modulation has already been shown to amplify both FES-related cortical effects—sensorimotor ERD (Reynolds et al, 2015 ) and TMS-induced peripheral responses—motor-evoked potentials (MEP; Takemi et al, 2013 )—when these stimulation techniques are applied separately. However, the influence of MI-related oscillatory modulation, e.g., of the sensorimotor rhythm (SMR), on associative pairing of these stimulation techniques remains unclear.…”

Section: Introductionmentioning

confidence: 99%

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Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization

Royter

Gharabaghi

2016

Front. Cell. Neurosci.

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Background: Pairing peripheral electrical stimulation (ES) and transcranial magnetic stimulation (TMS) increases corticospinal excitability when applied with a specific temporal pattern. When the two stimulation techniques are applied separately, motor imagery (MI)-related oscillatory modulation amplifies both ES-related cortical effects—sensorimotor event-related desynchronization (ERD), and TMS-induced peripheral responses—motor-evoked potentials (MEP). However, the influence of brain self-regulation on the associative pairing of these stimulation techniques is still unclear.Objective: The aim of this pilot study was to investigate the effects of MI-related ERD during associative ES and TMS on subsequent corticospinal excitability.Method: The paired application of functional electrical stimulation (FES) of the extensor digitorum communis (EDC) muscle and subsequent single-pulse TMS (110% resting motor threshold (RMT)) of the contralateral primary motor cortex (M1) was controlled by beta-band (16–22 Hz) ERD during MI of finger extension and applied within a brain-machine interface environment in six healthy subjects. Neural correlates were probed by acquiring the stimulus-response curve (SRC) of both MEP peak-to-peak amplitude and area under the curve (AUC) before and after the intervention.Result: The application of approximately 150 pairs of associative FES and TMS resulted in a significant increase of MEP amplitudes and AUC, indicating that the induced increase of corticospinal excitability was mediated by the recruitment of additional neuronal pools. MEP increases were brain state-dependent and correlated with beta-band ERD, but not with the background EDC muscle activity; this finding was independent of the FES intensity applied.Conclusion: These results could be relevant for developing closed-loop therapeutic approaches such as the application of brain state-dependent, paired associative stimulation (PAS) in the context of neurorehabilitation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization

Royter

Gharabaghi

2016

Front. Cell. Neurosci.

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“…Already several studies have implemented a somatosensory feedback to improve the discriminability of MI brain patterns. For instance, some authors used a robotic orthosis in order to induce a passive movement of the joint [25,26,27], a neuromuscular electrical stimulation (NMES) to induce muscular contraction [28,29,30] or a vibrotactile stimulation that provides tactile afferences of the targeted limb [20,31,32,33]. The major conclusion of these experiments is that somatosensory feedback is more appropriate to enhance MI brain patterns.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Vukelić et al (2015) [34] demonstrated that a robotic orthosis was more suitable than a visual feedback to entrain motor network during MI. Reynolds et al (2015) [30] showed that NMES during MI induced a larger desynchronization of the sensorimotor rhythms compared to motor imagery supported only by visual feedback. Cincotti et al (2007) [20] have highlighted the fact that vibrotactile feedback was perceived by subjects as more natural feedback for MI tasks.…”

Section: Introductionmentioning

confidence: 99%

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

et al. 2018

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Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.

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“…However, EA cannot be used to detect latency and amplitude variations from one trial to another; thus, single-trial analysis is better suited for investigations into the dynamics of brain activation. The single-trial VEP estimation is very meaningful in cognitive science research and clinical applications, such as brain-computer interfacing and intraoperative monitoring [2]. …”

Section: Introductionmentioning

confidence: 99%

Single-Trial Sparse Representation-Based Approach for VEP Extraction

Zou

et al. 2016

BioMed Research International

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Sparse representation is a powerful tool in signal denoising, and visual evoked potentials (VEPs) have been proven to have strong sparsity over an appropriate dictionary. Inspired by this idea, we present in this paper a novel sparse representation-based approach to solving the VEP extraction problem. The extraction process is performed in three stages. First, instead of using the mixed signals containing the electroencephalogram (EEG) and VEPs, we utilise an EEG from a previous trial, which did not contain VEPs, to identify the parameters of the EEG autoregressive (AR) model. Second, instead of the moving average (MA) model, sparse representation is used to model the VEPs in the autoregressive-moving average (ARMA) model. Finally, we calculate the sparse coefficients and derive VEPs by using the AR model. Next, we tested the performance of the proposed algorithm with synthetic and real data, after which we compared the results with that of an AR model with exogenous input modelling and a mixed overcomplete dictionary-based sparse component decomposition method. Utilising the synthetic data, the algorithms are then employed to estimate the latencies of P100 of the VEPs corrupted by added simulated EEG at different signal-to-noise ratio (SNR) values. The validations demonstrate that our method can well preserve the details of the VEPs for latency estimation, even in low SNR environments.

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Influence of motor imagination on cortical activation during functional electrical stimulation

Cited by 57 publications

References 33 publications

Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization

Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Single-Trial Sparse Representation-Based Approach for VEP Extraction

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