Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback; A pilot study

Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation

Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.

Spinal and corticospinal excitability changes with voluntary modulation of motor cortex oscillations

AIM

The aim of this study was to investigate the effects of EEG neurofeedback (NF)-induced modulation of sensorimotor alpha (i.e., mu) rhythm on spinal and corticospinal tract (CST) excitability.

METHODS

Forty-three healthy volunteers participated in 3 sessions of EEG-NF for upregulation (N=24) or downregulation (N=19) of individual alpha oscillations at central location Cz. Spinal excitability was studied before and during NF using H-reflex of the soleus muscle, and CST excitability was tested before and after NF, through Motor-Evoked Potential (MEP) of the tibialis anterior muscle. Mu rhythm was extracted using current source density. Differences in MEP and H-reflex before and during/after NF were analysed using repeated measures analysis. The relationship with motor cortexcortical excitability was estimated through linear regression between change in MEP/H-reflex, and change in power of mu rhythm and the upper portion of mu rhythm, muh.

RESULTS

CST excitability changes were significantly correlated to change in muh (p-value < 0.044, |r|>0.42), while spinal excitability changes were correlated to broad mu power modulation (p-value < 0.04, |r|> 0.43). While no distinct effect of NF on spinal versus CST excitability was found, the correlations indicate an inverted U-shape relationship between cortical and subcortical excitability. The trends of the correlations between spinal/CST excitability change and EEG power change were preserved when participants were grouped by success at NF task, and by mu modulation outcome, indicating that the net effect of power change at Cz weighs more than the task the participants attempted to accomplish.

CONCLUSIONS

The consistent direction of mu power correlation with both MEP, tested after NF, and H-reflex, tested during NF, indicates that modifications in mu activity are associated with spinal and CST adaptations lasting beyond the NF session, evidencing neuroplasticity. Together with the inverted U-shape relationship found between amplitude of mu modulation and spinal/CST excitability change, the results provide support for further research and clinical implementation of NF to induce CNS plasticity, a prerequisite for effective neural rehabilitation.

Age-related differences in agility are related to both muscle strength and corticospinal tract function

Agility is essential for "healthy" aging, but neuromuscular contributions to age-related differences in agility are not entirely understood. We recruited healthy (n = 32) non-athletes (30-84 years) to determine: (1) if aging is associated with agility and (2) whether muscle strength or corticospinal tract function predicts agility. We assessed muscle strength via a validated knee extension test, corticospinal tract function via transcranial magnetic stimulation, and agility via spatiotemporal values (i.e., leg length-adjusted hop length and hop length variability) collected during a novel propulsive bipedal hopping (agility) task on an electronic walkway. Pearson correlation revealed aging is associated with leg length-adjusted hop length (r = -0.671, p < 0.001) and hop length variability (r = 0.423, p = 0.016). Further, leg length-adjusted hop length and hop length variability correlated with quadriceps strength (r = 0.581, p < 0.001; r = -0.364, p = 0.048) and corticospinal tract function (r = -0.384, p = 0.039; r = 0.478, p = 0.007). However, hierarchical regressions indicated that, when controlling for sex, muscle strength only predicts leg length-adjusted hop length (R2 = 0.345, p = 0.002), whereas corticospinal tract function only predicts hop length variability (R2 = 0.239, p = 0.014). Therefore, weaker quadriceps decrease the distance hopped, and deteriorating corticospinal tract function increases variability in hop length.

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

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.

Behavior-Dependent Corticocortical Contributions to Imagined Grasping: A BCI-Triggered TMS Study

Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting. We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Could Brain-Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person's anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain-computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronization of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.