EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study. (March 2019)
- Record Type:
- Journal Article
- Title:
- EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study. (March 2019)
- Main Title:
- EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study
- Authors:
- Eldeeb, Safaa
Akcakaya, Murat
Sybeldon, Matthew
Foldes, Stephen
Santarnecchi, Emiliano
Pascual-Leone, Alvaro
Sethi, Amit - Abstract:
- Abstract: In this paper, we introduce electroencephalography (EEG)- PDC based network connectivity average mean degrees (E-PDC) measure to analyze the interhemispheric interaction between the left and right motor cortices after stroke. E-PDC uses a graph and partial directed coherence (PDC) approach to quantify the directional functional connectivity between the motor cortices, which is not only altered after stroke but also is one of the important mechanisms linked with poor recovery of hand function. The brain activity between the two motor cortices is calculated via PDC and is used to form a graph. The PDC based network connectivity average mean degree of connectivity defined over this graph is defined as the E-PDC, which quantifies the directional connectivity between the two motor cortices. We preliminarily validated the novel E-PDC measure with three individuals with stroke, where one individual received a non-invasive brain stimulation (NIBS) intervention and the other two received sham-NIBS intervention. Unlike the two individuals who received sham-NIBS, the individual who received the NIBS intervention showed improvement in E-PDC after intervention, which strongly correlated with improvement in hand function after intervention (Fugl Meyer Upper Extremity Subscale and grip strength). This implies that the introduced E-PDC measure quantifies the interactions between the motor cortices and could be used to elucidate the underlying mechanism in restoring hand functionAbstract: In this paper, we introduce electroencephalography (EEG)- PDC based network connectivity average mean degrees (E-PDC) measure to analyze the interhemispheric interaction between the left and right motor cortices after stroke. E-PDC uses a graph and partial directed coherence (PDC) approach to quantify the directional functional connectivity between the motor cortices, which is not only altered after stroke but also is one of the important mechanisms linked with poor recovery of hand function. The brain activity between the two motor cortices is calculated via PDC and is used to form a graph. The PDC based network connectivity average mean degree of connectivity defined over this graph is defined as the E-PDC, which quantifies the directional connectivity between the two motor cortices. We preliminarily validated the novel E-PDC measure with three individuals with stroke, where one individual received a non-invasive brain stimulation (NIBS) intervention and the other two received sham-NIBS intervention. Unlike the two individuals who received sham-NIBS, the individual who received the NIBS intervention showed improvement in E-PDC after intervention, which strongly correlated with improvement in hand function after intervention (Fugl Meyer Upper Extremity Subscale and grip strength). This implies that the introduced E-PDC measure quantifies the interactions between the motor cortices and could be used to elucidate the underlying mechanism in restoring hand function after stroke. … (more)
- Is Part Of:
- Biomedical signal processing and control. Volume 49(2019)
- Journal:
- Biomedical signal processing and control
- Issue:
- Volume 49(2019)
- Issue Display:
- Volume 49, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 49
- Issue:
- 2019
- Issue Sort Value:
- 2019-0049-2019-0000
- Page Start:
- 419
- Page End:
- 426
- Publication Date:
- 2019-03
- Subjects:
- Interhemispheric inhibition -- Connectivity measure -- EEG -- Stroke
Signal processing -- Periodicals
Biomedical engineering -- Periodicals
Signal Processing, Computer-Assisted -- Periodicals
Image Processing, Computer-Assisted -- Periodicals
Biomedical Engineering -- Periodicals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/17468094 ↗
http://www.elsevier.com/journals ↗
http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%2329675%232006%23999989998%23626449%23FLA%23&_cdi=29675&_pubType=J&_auth=y&_acct=C000045259&_version=1&_urlVersion=0&_userid=836873&md5=664b5cf9a57fc91971a17faf20c32ec1 ↗ - DOI:
- 10.1016/j.bspc.2018.12.022 ↗
- Languages:
- English
- ISSNs:
- 1746-8094
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2087.880400
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9475.xml