Causal relationships between brain and spinal motor neuron excitability during motor imagery: Using NIRS and evoked electromyogram study

Fukumoto, Yuki; Todo, Mitsugu; Bizen, Hiroki; Kimura, Daisuke; Suzuki, Toshiaki

doi:10.1016/j.ynirp.2022.100083

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…The sample size was calculated to be 12. The sample size estimation considered the possibility of high effect sizes in this study because our previous studies 20,38 showed a high effect size; therefore, an appropriate sample size was necessary to prevent error. Therefore, 21 healthy participants (13 men and 8 women; mean age: 24.8 ± 5.5 years) were included to ensure that the actual sample size was greater than the calculated sample size.…”

Section: Methodsmentioning

confidence: 99%

Differences in motor imagery strategy change behavioral outcome

Fukumoto

Todo²,

Bunno³

et al. 2022

Sci Rep

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Kinesthetic motor imagery (KMI) involves imagining the feeling and experience of movements. We examined the effects of KMI, number visualizing, and KMI with number visualizing on the excitability of spinal motor neurons and a behavioral outcome measure in a pinch force task. Healthy participants (13 men and 8 women; mean age: 24.8 ± 5.5 years) were recruited. We compared the F-waves of the left thenar muscles after stimulating the left median nerve at the wrist during each motor imagery condition after a practice session. The KMI condition consisted of imagining muscle contraction, the number visualizing condition consisted of imagining the pinch force increasing numerically, and the KMI with number visualizing consisted of alternating between the KMI and imagining the pinch force increasing numerically. Before and after motor imagery, the time required to adjust to the target pinch force was compared. The time required to adjust the pinch force was shorter in the KMI with number visualizing condition than in the KMI and number visualizing conditions. There was no difference in the F/M amplitude ratio between each MI strategy condition, indicating the excitability of spinal motor neurons. Numerical information helped to improve the ability of participants to perform KMI.

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Section: Methodsmentioning

confidence: 99%

Differences in motor imagery strategy change behavioral outcome

Fukumoto

Todo²,

Bunno³

et al. 2022

Sci Rep

Self Cite

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“…These signals undergo a translation process to produce control commands for assistive technologies or virtual avatars, therefore enhancing neural pathways associated with motor control and fostering neuroplastic adaptations. BCIs demonstrate potential applicability in post-stroke rehabilitation, spinal cord injuries, and other medical conditions, offering novel opportunities for motor recovery and functional restoration [15,57,58].…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Leveraging Motor Imagery Rehabilitation for Individuals with Disabilities: A Review

Almufareh,

Kausar,

Humayun

et al. 2023

Healthcare

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Motor imagery, an intricate cognitive procedure encompassing the mental simulation of motor actions, has surfaced as a potent strategy within the neuro-rehabilitation domain. It presents a non-invasive, economically viable method for facilitating individuals with disabilities in enhancing their motor functionality and regaining self-sufficiency. This manuscript delivers an exhaustive analysis of the significance of motor imagery in augmenting functional rehabilitation for individuals afflicted with physical impairments. It investigates the fundamental mechanisms governing motor imagery, its applications across diverse disability conditions, and the prospective advantages it renders. Moreover, this document addresses the prevailing obstacles and prospective trajectories in this sector, accentuating the necessity for continued investigation and the invention of cutting-edge technologies that optimize the potentiality of motor imagery in aiding disabled persons.

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“…Additionally, the right DLPFC contributes significantly to KMI since it is instrumentally related to planning actions, attention and memory [37]; such mechanism affects the timing to perform KMI [38]. This is supported by the fact that DLPFC is connected to SMA through an indirect pathway to the parietal lobe [38,39]. During correct MI performance, there is connectivity between PM and DLPFC.…”

Section: Introductionmentioning

confidence: 98%

Characterization and classification of kinesthetic motor imagery levels

Martinez-Peon,

Garcia-Hernandez,

Benavides-Bravo

et al. 2024

J. Neural Eng.

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Objective. Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0, 10, 40, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbours (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

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Causal relationships between brain and spinal motor neuron excitability during motor imagery: Using NIRS and evoked electromyogram study

Cited by 5 publications

References 52 publications

Differences in motor imagery strategy change behavioral outcome

Differences in motor imagery strategy change behavioral outcome

Leveraging Motor Imagery Rehabilitation for Individuals with Disabilities: A Review

Characterization and classification of kinesthetic motor imagery levels

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