Volitional Control of Brain Motor Activity and Its Therapeutic Potential

Girges, Christine; Vijiaratnam, Nirosen; Zrinzo, Ludvic; Ekanayake, Jinendra; Foltynie, Thomas

doi:10.1016/j.neurom.2022.01.007

Cited by 11 publications

(9 citation statements)

References 83 publications

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“…NFB can be used to solve this problem. MI-NFB provides real-time feedback on brain activity during motor imagery to the participant and improves the accuracy of imagery by activating brain regions related to motor imagery [37]. In this section, the mechanism and effects of MI-NFB are explained on the basis of the results of studies conducted in healthy individuals.…”

Section: Motor Imagery-based Neurofeedbackmentioning

confidence: 99%

“…MI-NFB has been shown to alter brain activity and improve motor imagery ability [37]. Hwang et al tested the effects of MI-NFB with visual stimuli in healthy young individuals [47].…”

Section: Motor Imagery-based Neurofeedbackmentioning

confidence: 99%

“…This result indicates that the effect of MI-NFB alone, as well as the combination of MI-NFB and tDCS, can amplify the effect of MI-NFB. Both MI-NFB [37] and tDCS [55,56] have been shown to improve MI accuracy, but the processes underlying their effects are different. Therefore, the effects of MI-NFB and tDCS do not appear to overlap, indicating the possibility of a synergistic effect.…”

Section: Combination Of Neurofeedback With Transcranial Stimulationmentioning

confidence: 99%

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Motor Imagery-based Neurofeedback in Physiotherapy Practice

Sawai,

Fujikawa,

Yamamoto

et al. 2024

Physical Therapy - Towards Evidence-Based Practice

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Neurofeedback (NFB) is a closed-loop technique in which the patient receives feedback on brain activity to encourage voluntary control of brain activity. NFB promotes neuroplasticity and changes the brain functionally and structurally. Motor imagery-based NFB (MI-NFB) can improve motor imagery ability by providing feedback on brain activity during motor imagery, thereby showing effectiveness in performance and motor learning. Furthermore, the effects of MI-NFB are further enhanced when it is combined with noninvasive brain stimulation and motor exercise. Therefore, MI-NFB is used in the physiotherapy of patients with neurological diseases, such as stroke and Parkinson disease, as well as children with attention deficit-hyperactivity disorder and elderly people. This chapter reviews MI-NFB in physiotherapy practice, thus contributing to the development of effective evidence-based physiotherapy.

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Section: Motor Imagery-based Neurofeedbackmentioning

confidence: 99%

“…MI-NFB has been shown to alter brain activity and improve motor imagery ability [37]. Hwang et al tested the effects of MI-NFB with visual stimuli in healthy young individuals [47].…”

Section: Motor Imagery-based Neurofeedbackmentioning

confidence: 99%

Section: Combination Of Neurofeedback With Transcranial Stimulationmentioning

confidence: 99%

See 1 more Smart Citation

Motor Imagery-based Neurofeedback in Physiotherapy Practice

Sawai,

Fujikawa,

Yamamoto

et al. 2024

Physical Therapy - Towards Evidence-Based Practice

View full text Add to dashboard Cite

show abstract

“…This noninvasive brain modulation technique involves the real-time analysis and visualization of brain activation signals, thereby enabling participants to self-regulate their brain activity. Its efficacy in training participants to modulate their brain activation is wellsupported by numerous studies, including several meta-analyses [3][4][5][6][7][8][9][10][11] . However, the direct impact of rtfMRI-NF on symptom relief is not yet fully understood due to incomplete knowledge of the neural mechanisms by which this training alleviates symptoms through the regulation of specific brain activations.…”

Section: Introductionmentioning

confidence: 99%

Clinical Response to Neurofeedback in Major Depression Relates to Subtypes of Whole-Brain Activation Patterns During Training

Misaki,

Young,

Tsuchiyagaito

et al. 2024

Preprint

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Major Depressive Disorder (MDD) presents a significant public health challenge. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) shows promise as a treatment for this disorder, although its mechanisms of action remain unclear. This study investigated whole-brain response patterns during rtfMRI-NF training to explain interindividual variability in clinical efficacy in MDD. We analyzed data from 95 participants (67 active, 28 control) with MDD from previous rtfMRI-NF studies designed to increase left amygdala activation through positive autobiographical memory recall. We focused on whole-brain activation patterns during two critical epochs of the neurofeedback procedure: activation during the self-regulation period and transient responses to feedback signal presentation. Through a systematic process involving feature selection, manifold extraction, and clustering with cross-validation, we identified subtypes within these patterns. Significant symptom reduction was observed in the active group (t=-4.404, d=-0.704, p<0.001) but not in the control group (t=-1.609, d=-0.430, p=0.111); however, left amygdala activation did not account for the variability in clinical efficacy. Subtyping analysis revealed two subtypes in regulation activation and three subtypes in brain responses to feedback signals (regulation: F=8.735, p=0.005; feedback response: F=5.326, p=0.008; interaction: F=3.471, p=0.039). Subtypes associated with significant symptom reduction were characterized by selective increases in control regions, including lateral prefrontal areas, and decreases in regions associated with self-referential thinking, such as default mode areas. These findings suggest that large-scale brain activity is more critical for clinical efficacy than the level of activation in the neurofeedback target region during training. Tailoring neurofeedback training to incorporate these patterns could significantly enhance its therapeutic efficacy.

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“…In post-stroke rehabilitation, BCI is used to provide feedback to a patient during motor imagery training [1][2][3]. The scientific justification for this method has been the data on the positive effect of the motor imagery process on neuroplasticity due to activation of motor structures of the central nervous system (CNS) [4][5][6][7][8]. By providing feedback during motor imagery, the BCI systems enhance the effectiveness of such training sessions [9].…”

Section: Introductionmentioning

confidence: 99%

Brain–Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review)

Mokienko,

Lyukmanov,

Bobrov

et al. 2023

Sovrem Tehnol Med

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Brain-computer interfaces (BCIs) are a group of technologies that allow mental training with feedback for post-stroke motor recovery. Varieties of these technologies have been studied in numerous clinical trials for more than 10 years, and their construct and software are constantly being improved. Despite the positive treatment results and the availability of registered medical devices, there are currently a number of problems for the wide clinical application of BCI technologies. This review provides information on the most studied types of BCIs and its training protocols and describes the evidence base for the effectiveness of BCIs for upper limb motor recovery after stroke. The main problems of scaling this technology and ways to solve them are also described.

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Volitional Control of Brain Motor Activity and Its Therapeutic Potential

Cited by 11 publications

References 83 publications

Motor Imagery-based Neurofeedback in Physiotherapy Practice

Motor Imagery-based Neurofeedback in Physiotherapy Practice

Clinical Response to Neurofeedback in Major Depression Relates to Subtypes of Whole-Brain Activation Patterns During Training

Brain–Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review)

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