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Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates With Motor Adaptation in Gait Training. IEEE Trans Neural Syst Rehabil Eng 2016 Nov;24(11):1265-1275

Date

04/14/2016

Pubmed ID

27071181

DOI

10.1109/TNSRE.2016.2551642

Scopus ID

2-s2.0-85002794169 (requires institutional sign-in at Scopus site)   37 Citations

Abstract

Lower-extremity robotic exoskeletons are used in gait rehabilitation to achieve functional motor recovery. To date, little is known about how gait training and post-training are characterized in brain signals and their causal connectivity. In this work, we used time-domain partial Granger causality (PGC) analysis to elucidate the directed functional connectivity of electroencephalogram (EEG) signals of healthy adults in robot-assisted gait training (RAGT). Our results confirm the presence of EEG rhythms and corticomuscular relationships during standing and walking using spectral and coherence analyses. The PGC analysis revealed enhanced connectivity close to sensorimotor areas ( C3 and CP3 ) during standing, whereas additional connectivities involve the centroparietal ( CP z) and frontal ( F z ) areas during walking with respect to standing. In addition, significant fronto-centroparietal causal effects were found during both training and post-training. Strong correlations were also found between kinematic errors and fronto-centroparietal connectivity during training and post-training. This study suggests fronto-centroparietal connectivity as a potential neuromarker for motor learning and adaptation in RAGT.

Author List

Youssofzadeh V, Zanotto D, Wong-Lin K, Agrawal SK, Prasad G

Author

Vahab Youssofzadeh PhD Assistant Professor in the Neurology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Adaptation, Physiological
Adult
Computer Simulation
Connectome
Frontal Lobe
Gait
Humans
Male
Models, Neurological
Motor Cortex
Movement
Neural Pathways
Neuronal Plasticity
Parietal Lobe
Physical Conditioning, Human
Robotics
Statistics as Topic