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Brain Sciences
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Robotic Motor Rehabilitation for Brain Injured Patients: State of the...
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Robotic Motor Rehabilitation for Brain Injured Patients: State of the Art on Its Efficacy and Neural Underpinnings

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: closed (5 April 2021) | Viewed by 5161

Special Issue Editors

Prof. Dr. Katiuscia Sacco

E-Mail Website1 Website2
Guest Editor
Department of Psychology, University of Turin, 10100 Turin, Italy
Interests: Learning; Neuroimaging; Neurorehabilitation; Motor Cognition; Virtual Reality
Dr. Alessandro Cicerale

E-Mail Website
Guest Editor
Department of Psychology, University of Turin, 10100 Turin, Italy
Interests: Neuroimaging; Non-invasive Brain stimulation; Motor Planning; Learning; Neurorehabilitation

Special Issue Information

Dear Colleagues,

Motor deficits – most notably, paresis – is a frequent result of brain injury. Robot-assisted therapy has been gaining acceptance as a treatment to improve limb functionality in stroke survivors, but it has also been used for cerebral palsy, multiple sclerosis, and other neurological syndromes (Duret et al, 2019).

While there is evidence that robot-assisted therapy can improve arm function and muscle strength after a stroke (Merholz et al, 2018), less evidence has been collected regarding lower limb rehabilitation (Weber and Stein, 2018). Moreover, the neural mechanisms underlying clinical and functional improvements are not always investigated.

This Special Issue is therefore dedicated to experimental studies that use robot-assisted interventions to treat upper or lower limb paresis after brain damage. Given the lack of data on walking rehabilitation, papers presenting results on the efficacy of exoskeletons or lower limb devices will be prioritized.

Studies comprising both clinical and neuroimaging or neurophysiological evaluations are particularly welcome, but works using lesion symptom mapping will also be considered of interest when shedding light on the impact of cerebral lesion load on the response to robotic-assisted rehabilitation. Finally, reviews devoted to issues of particular relevance for this topic will be evaluated.

References

  1. Mehrholz, J.; Pohl, M.; Platz, T.; Kugler, J.; Elsner, B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2015. Update in Cochrane Database Syst Rev. 2018.
  2. Duret, C., Grosmaire, A.G., Krebs, H.I. Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach. Frontiers in Neurology, 2019, 10, 412.
  3. Weber, L.M.; Stein, J. The use of robots in stroke rehabilitation: A narrative review. NeuroRehabilitation, 2018, 43(1), 99-110.

Prof. Dr. Katiuscia Sacco
Dr. Alessandro Cicerale
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Motor rehabilitation
  • Robot-assisted therapy
  • Limb paresis
  • Neurorehabilitation
  • Neuroimaging
  • Stroke
  • Stroke Rehabilitation
  • Brain-computer Interface

Published Papers (1 paper)

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Research

16 pages, 1043 KiB  
Article
BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study
by Kai Yuan, Cheng Chen, Xin Wang, Winnie Chiu-wing Chu and Raymond Kai-yu Tong
Brain Sci. 2021, 11(1), 56; https://doi.org/10.3390/brainsci11010056 - 6 Jan 2021
Cited by 26 | Viewed by 4724
Abstract
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 multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by [...] Read more.
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 multimodality 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. Fourteen 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 was also investigated. This work suggested that (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, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. 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. Full article
(This article belongs to the Special Issue Robotic Motor Rehabilitation for Brain Injured Patients: State of the Art on Its Efficacy and Neural Underpinnings)
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