<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Pirondini, Elvira</dc:creator>
  <dc:creator>Goldshuv-Ezra, Nurit</dc:creator>
  <dc:creator>Zinger, Nofya</dc:creator>
  <dc:creator>Britz, Juliane</dc:creator>
  <dc:creator>Soroker, Nachum</dc:creator>
  <dc:creator>Deouell, Leon Y.</dc:creator>
  <dc:creator>Van De Ville, Dimitri</dc:creator>
  <dc:date>2020-01-01</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Theoretical advances in the neurosciences are leading to the development of  an increasing number of proposed interventions for the enhancement of  functional recovery after brain damage. Integration of these novel approaches in  clinical practice depends on the availability of reliable, simple, and sensitive  biomarkers of impairment level and extent of recovery, to enable an informed  clinical-decision process. However, the neuropsychological tests currently in  use do not tap into the complex neural re-organization process that occurs after  brain insult and its modulation by treatment. Here we show that topographical  analysis of resting-state electroencephalography (rsEEG) patterns using  singular value decomposition (SVD) could be used to capture these processes.  In two groups of subacute stroke patients, we show reliable detection of deviant  neurophysiological patterns over repeated measurement sessions on separate  days. These patterns generalized across patients groups. Additionally, they  maintained a significant association with ipsilesional attention bias,  discriminating patients with spatial neglect of different severity levels. The  sensitivity and reliability of these rsEEG topographical analyses support their  use as a tool for monitoring natural and treatment-induced recovery in the  rehabilitation process.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/308540</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/308540/files/bri_rse.pdf</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/308540/files/bri_rse_sm.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nicl.2020.102237</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>NeuroImage: Clinical. - 2020, vol. 26, p. 102237</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Unilateral spatial neglect</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Stroke</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Rehabilitation</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Outcome measurement</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Computer-enhanced measurement</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">EEG analysis</dc:subject>
  <dc:subject xmlns:ns7="xml" ns7:lang="en">EEG topography features</dc:subject>
  <dc:subject xmlns:ns8="xml" ns8:lang="en">Resting-state EEG biomarkers</dc:subject>
  <dc:subject xmlns:ns9="xml" ns9:lang="en">Machine learning</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/57</dc:subject>
  <dc:title xmlns:ns10="xml" ns10:lang="en">Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
