Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Biasiucci, Andrea; Leeb, Robert; Iturrate, Iñaki; Perdikis, Serafeim; Al-Khodairy, Abdul; Corbet, Tiffany; Schnider, Armin; Schmidlin, Thomas W.; Zhang, H; Bassolino, Michela; Viceic, Dragana; Vuadens, Philippe; Guggisberg, Adrian G.; Millán, José del R.

doi:10.1038/s41467-018-04673-z

Cited by 419 publications

(555 citation statements)

References 69 publications

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“…As an example, in a recent study, the group of Millan showed that the use of functional electrical stimulation (FES) based on a BCI was able to induce significant functional recovery in stroke patients, and that such recovery was retained 6 to 12 months after the end of therapy [162]. In this study, decoding of movement intention by the BCI triggered activation of hand muscles through FES, and the authors showed that motor recovery came along with significant cortical reorganization.…”

Section: Robotic Neurorehabilitation Coupled To Brain Computer Interfmentioning

confidence: 81%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

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Section: Robotic Neurorehabilitation Coupled To Brain Computer Interfmentioning

confidence: 81%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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“…Introduction similar tasks as the BCI training even after the completion of the therapy [21]. In 1 spite of some promising results achieved so far, BCI stroke rehabilitation is still a 2 young field where different works report variable clinical outcomes [22]. The neces- 3 sary functional connectivity changes among brain regions induced in stroke patients 4 with lasting recovery effect remain unclear, with only several putative mechanisms 5 been proposed [23].…”

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confidence: 99%

“…18 Although correlation-based connectivity using fMRI can investigate the relation- 19 ship between distant brain regions, this kind of connectivity cannot provide any 20 precise information related to the directionality of the information flow due to its 21 poor time resolution. Electroencephalograph (EEG) is a prominent tool which offers 22 a more direct measure of the electrophysiological signal with higher time resolution 23 to explore the dynamic brain processes [32]. EEG signal can be regarded as the 24 integration of all concurrently active sources in the brain [33].…”

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confidence: 99%

“…Fourteen chronic stroke patients (13 males, mean age = 54±8 years) with the right 22 (n=9) or left (n=5) hemisphere impaired were recruited from local community. The 23 inclusion criteria were: (1) first-ever stroke, (2) onset of stroke diagnose more than 6 24 months prior to the beginning of individual experiment trial, (3) a single unilateral 25 brain lesion, (4) sufficient cognition and comprehensive ability to understand and 26 perform corresponding tasks tested by Mini-Mental State Examination (MMSE) 27 with score > 21, (4) moderate to severe motor dysfunctions for the paretic upper 28 extremity (Fugl-Meyer Assessment score for upper-extremity < 47) [36] and (5) no 29 additional rehabilitation therapies applied to the patient.…”

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confidence: 99%

“…When under no load, the maximum contraction 20 speed of the robotic hand was approximately 2 seconds to fully open or closed po-21 sitions of the robotic hand. During each trial, the subjects were asked to relax for 22 2 s followed by a white cross for 2 s to remind the subjects to do preparation. A 23 text cue of 'hand grasp' or 'hand open' was then displayed for 2 s to illustrate the 24 following motion.…”

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BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

Tong

Yuan

Chen

et al. 2020

Preprint

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Background: Brain-computer interface (BCI) guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multi-modality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement.Method: 14 chronic stroke subjects received 20 sessions of BCI-guided robot hand training. Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes and electrophysical changes derived from the two neuroimaging modalities were also investigated. Results: This work suggested: (a) significant motor function improvement could be obtained after BCI training therapy; (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI; (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG; (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change.Conclusions: Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change. This study was registered at https://clinicaltrials.gov (NCT02323061) on 23 December 2014.

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Associative cued asynchronous BCI induces cortical plasticity in stroke patients

Niazi

Navid

Rashid

et al. 2022

Ann Clin Transl Neurol

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Objective We propose a novel cue‐based asynchronous brain–computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways. Methods The proposed BCI detects the intention to move from single‐trial EEG signals in real time, but, contrary to classic asynchronous‐BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue‐based asynchronous‐BCI was compared with two traditional BCI modes (asynchronous‐BCI and offline synchronous‐BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor‐evoked potentials in the tibialis‐anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention. Results The proposed cue‐based asynchronous‐BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous‐BCI. Linear‐mixed‐models showed that motor‐evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue‐based asynchronous‐BCI resulted in the largest relative increase in peak‐to‐peak motor‐evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%). Interpretation These findings prove the high performance of a newly proposed cue‐based asynchronous‐BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

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Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Cited by 419 publications

References 69 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

Associative cued asynchronous BCI induces cortical plasticity in stroke patients

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