DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jungsoo | ko |
dc.contributor.author | Park, Eunhee | ko |
dc.contributor.author | Lee, Ahee | ko |
dc.contributor.author | Chang, Won Hyuk | ko |
dc.contributor.author | Kim, Dae-Shik | ko |
dc.contributor.author | Kim, Yun-Hee | ko |
dc.date.accessioned | 2020-06-17T03:20:10Z | - |
dc.date.available | 2020-06-17T03:20:10Z | - |
dc.date.created | 2020-06-10 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS, v.13, pp.175628642092567 | - |
dc.identifier.issn | 1756-2864 | - |
dc.identifier.uri | http://hdl.handle.net/10203/274700 | - |
dc.description.abstract | Background: Recovery prediction can assist in the planning for impairment-focused rehabilitation after a stroke. This study investigated a new prediction model based on a lesion network analysis. To predict the potential for recovery, we focused on the next link-step connectivity of the direct neighbors of a lesion. Methods: We hypothesized that this connectivity would contribute to recovery after stroke onset. Each lesion in a patient who had suffered a stroke was transferred to a healthy subject. First link-step connectivity was identified by observing voxels functionally connected to each lesion. Next (second) link-step connectivity of the first link-step connectivity was extracted by calculating statistical dependencies between time courses of regions not directly connected to a lesion and regions identified as first link-step connectivity. Lesion impact on second link-step connectivity was quantified by comparing the lesion network and reference network. Results: The lower the impact of a lesion was on second link-step connectivity in the brain network, the better the improvement in motor function during recovery. A prediction model containing a proposed predictor, initial motor function, age, and lesion volume was established. A multivariate analysis revealed that this model accurately predicted recovery at 3 months poststroke (R (2)& x2004;=& x2004;0.788; cross-validation, R (2)& x2004;=& x2004;0.746, RMSE & x2004;=& x2004;13.15). Conclusion: This model can potentially be used in clinical practice to develop individually tailored rehabilitation programs for patients suffering from motor impairments after stroke. | - |
dc.language | English | - |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.title | Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke | - |
dc.type | Article | - |
dc.identifier.wosid | 000537057300001 | - |
dc.identifier.scopusid | 2-s2.0-85085201816 | - |
dc.type.rims | ART | - |
dc.citation.volume | 13 | - |
dc.citation.beginningpage | 175628642092567 | - |
dc.citation.publicationname | THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS | - |
dc.identifier.doi | 10.1177/1756286420925679 | - |
dc.contributor.localauthor | Kim, Dae-Shik | - |
dc.contributor.nonIdAuthor | Lee, Jungsoo | - |
dc.contributor.nonIdAuthor | Park, Eunhee | - |
dc.contributor.nonIdAuthor | Lee, Ahee | - |
dc.contributor.nonIdAuthor | Chang, Won Hyuk | - |
dc.contributor.nonIdAuthor | Kim, Yun-Hee | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | lesion network | - |
dc.subject.keywordAuthor | motor function | - |
dc.subject.keywordAuthor | motor recovery | - |
dc.subject.keywordAuthor | prediction model | - |
dc.subject.keywordAuthor | stroke | - |
dc.subject.keywordPlus | FUNCTIONAL REORGANIZATION | - |
dc.subject.keywordPlus | VOLUME | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | LOCALIZATION | - |
dc.subject.keywordPlus | OUTCOMES | - |
dc.subject.keywordPlus | SIGNAL | - |
dc.subject.keywordPlus | AGE | - |
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