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Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, July 2017
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Title
Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery
Published in
Journal of NeuroEngineering and Rehabilitation, July 2017
DOI 10.1186/s12984-017-0277-3
Pubmed ID
Authors

Gavin R. Philips, Janis J. Daly, José C. Príncipe

Abstract

Biomarkers derived from neural activity of the brain present a vital tool for the prediction and evaluation of post-stroke motor recovery, as well as for real-time biofeedback opportunities. In order to encapsulate recovery-related reorganization of brain networks into such biomarkers, we have utilized the generalized measure of association (GMA) and graph analyses, which include global and local efficiency, as well as hemispheric interdensity and intradensity. These methods were applied to electroencephalogram (EEG) data recorded during a study of 30 stroke survivors (21 male, mean age 57.9 years, mean stroke duration 22.4 months) undergoing 12 weeks of intensive therapeutic intervention. We observed that decreases of the intradensity of the unaffected hemisphere are correlated (r s =-0.46;p<0.05) with functional recovery, as measured by the upper-extremity portion of the Fugl-Meyer Assessment (FMUE). In addition, high initial values of local efficiency predict greater improvement in FMUE (R (2)=0.16;p<0.05). In a subset of 17 subjects possessing lesions of the cerebral cortex, reductions of global and local efficiency, as well as the intradensity of the unaffected hemisphere are found to be associated with functional improvement (r s =-0.60,-0.66,-0.75;p<0.05). Within the same subgroup, high initial values of global and local efficiency, are predictive of improved recovery (R (2)=0.24,0.25;p<0.05). All significant findings were specific to the 12.5-25 Hz band. These topological measures show promise for prognosis and evaluation of therapeutic outcomes, as well as potential application to BCI-enabled biofeedback.

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The data shown below were compiled from readership statistics for 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 23%
Researcher 19 15%
Student > Master 14 11%
Student > Bachelor 12 9%
Student > Postgraduate 9 7%
Other 15 12%
Unknown 29 23%
Readers by discipline Count As %
Neuroscience 23 18%
Engineering 16 13%
Medicine and Dentistry 14 11%
Nursing and Health Professions 13 10%
Psychology 7 5%
Other 10 8%
Unknown 45 35%