Whatever the hell neural guidance is? Might and could are used instead of does and will, so further followup research will be needed to create protocols.
https://www.frontiersin.org/articles/10.3389/fneur.2018.00810/abstract
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1The Chinese University of Hong Kong, Hong Kong
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2Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, China
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3Brain and Mind Institute, The Chinese University of Hong Kong, China
Robot-assisted training combined with neural guided strategy has
been increasingly applied to stroke rehabilitation. However, the
induced neuroplasticity is seldom characterized. It is still uncertain
whether this kind of guidance
could enhance the long-term training
effect for stroke motor recovery. This study was conducted to explore
the clinical improvement and the neurological changes after 20-session
guided or non-guided robot hand training using two measures: changes in
brain discriminant ability between motor-imagery and resting states
revealed from electroencephalography(EEG) signals and changes in brain
network variability revealed from resting-state functional magnetic
resonance imaging(fMRI) data in twenty-four chronic stroke subjects. The
subjects were randomly assigned to receive either combined action
observation(AO) with EEG-guided robot-hand training(RobotEEG_AO, n=13)
or robot-hand training without AO and EEG guidance(Robotnon-EEG_Text,
n=11). The robot hand in RobotEEG_AO group was activated only when
significant mu suppression(8-12Hz) was detected from subjects’ EEG
signals in ipsilesional hemisphere, while the robot hand in
Robotnon-EEG_Text group was randomly activated regardless of their EEG
signals. Paretic upper-limb motor functions were evaluated at three
time-points: before, immediately after and 6 months after the
interventions. Only RobotEEG_AO group showed a long-term significant
improvement in their upper-limb motor functions while no significant and
long-lasting training effect on the paretic motor functions was shown
in Robotnon-EEG_Text group. Significant neuroplasticity changes were
only observed in RobotEEG_AO group as well. The brain discriminant
ability based on the ipsilesional EEG signals significantly improved
after intervention. For brain network variability, the whole brain was
first divided into six functional subnetworks, and significant increase
in the temporal variability was found in four out of the six
subnetworks, including sensory-motor areas, attention network, auditory
network and default mode network after intervention. Our results
revealed the differences in the long-term training effect and the
neuroplasticity changes following the two interventional strategies:
with and without neural guidance. The findings
might imply that
sustainable motor function improvement could be achieved through proper
neural guidance, which
might provide insights into strategies for
effective stroke rehabilitation. Furthermore, neuroplasticity
could be
promoted more profoundly by the intervention with proper neurofeedback,
and
might be shaped in relation to better motor skill acquisition.
Keywords:
long-term training effect, Motor Imagery, EEG discriminant rate,
resting state fMRI, Temporal variability, brain network, action
observation, motor recovery
Received: 16 May 2018;
Accepted: 07 Sep 2018.
Reviewed by:
Bernhard Sehm, Max-Planck-Institut für Kognitions- und Neurowissenschaften, Germany
Sheng Li, University of Texas Health Science Center at Houston, United States
© 2018 Wang, Wong, Sun, Chu and Tong. This is an open-access article distributed under the terms of the
Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited
and that the original publication in this journal is cited, in
accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
* Correspondence:
Prof. Raymond K. Tong, The Chinese University of Hong Kong, Shatin, Hong Kong, kytong@cuhk.edu.hk
Xin Wang
Wan-wa Wong
Raymond K. Tong