Neural function, injury, and stroke subtype predict treatment gains after stroke. Issue 1 (4th December 2014)
- Record Type:
- Journal Article
- Title:
- Neural function, injury, and stroke subtype predict treatment gains after stroke. Issue 1 (4th December 2014)
- Main Title:
- Neural function, injury, and stroke subtype predict treatment gains after stroke
- Authors:
- Burke Quinlan, Erin
Dodakian, Lucy
See, Jill
McKenzie, Alison
Le, Vu
Wojnowicz, Mike
Shahbaba, Babak
Cramer, Steven C. - Abstract:
- <abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="ana24309-sec-0001" sec-type="section"> <title>Objective</title> <p>This study was undertaken to better understand the high variability in response seen when treating human subjects with restorative therapies poststroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains; therefore, the current study hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value.</p> </sec> <sec id="ana24309-sec-0002" sec-type="section"> <title>Methods</title> <p>Patients 3 to 6 months poststroke underwent a battery of assessments before receiving 3 weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical status (demographics/medical history, cognitive/mood, and impairment).</p> </sec> <sec id="ana24309-sec-0003" sec-type="section"> <title>Results</title> <p>Among all 29 patients, predictors of treatment gains identified measures of brain injury (smaller corticospinal tract [CST] injury), cortical function (greater ipsilesional motor cortex [M1] activation), and cortical connectivity (greater interhemispheric M1–M1 connectivity). Multivariate modeling found that best prediction was achieved using both CST injury and M1–M1<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="ana24309-sec-0001" sec-type="section"> <title>Objective</title> <p>This study was undertaken to better understand the high variability in response seen when treating human subjects with restorative therapies poststroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains; therefore, the current study hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value.</p> </sec> <sec id="ana24309-sec-0002" sec-type="section"> <title>Methods</title> <p>Patients 3 to 6 months poststroke underwent a battery of assessments before receiving 3 weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical status (demographics/medical history, cognitive/mood, and impairment).</p> </sec> <sec id="ana24309-sec-0003" sec-type="section"> <title>Results</title> <p>Among all 29 patients, predictors of treatment gains identified measures of brain injury (smaller corticospinal tract [CST] injury), cortical function (greater ipsilesional motor cortex [M1] activation), and cortical connectivity (greater interhemispheric M1–M1 connectivity). Multivariate modeling found that best prediction was achieved using both CST injury and M1–M1 connectivity (<italic>r</italic><sup>2</sup> = 0.44, <italic>p</italic> = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with &gt;63% CST injury achieved clinically significant gains. Results differed according to stroke subtype; gains in patients with lacunar stroke were best predicted by a measure of intrahemispheric connectivity.</p> </sec> <sec id="ana24309-sec-0004" sec-type="section"> <title>Interpretation</title> <p>Response to a restorative therapy after stroke is best predicted by a model that includes measures of both neural injury and function. Neuroimaging measures were the best predictors and may have an ascendant role in clinical decision making for poststroke rehabilitation, which remains largely reliant on behavioral assessments. Results differed across stroke subtypes, suggesting the utility of lesion‐specific strategies. ANN NEUROL 2015;77:132–145</p> </sec> </abstract> … (more)
- Is Part Of:
- Annals of neurology. Volume 77:Issue 1(2015:Jan.)
- Journal:
- Annals of neurology
- Issue:
- Volume 77:Issue 1(2015:Jan.)
- Issue Display:
- Volume 77, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 77
- Issue:
- 1
- Issue Sort Value:
- 2015-0077-0001-0000
- Page Start:
- 132
- Page End:
- 145
- Publication Date:
- 2014-12-04
- Subjects:
- Neurology -- Periodicals
Pediatric neurology -- Periodicals
Nervous system -- Surgery -- Periodicals
616.8 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1531-8249 ↗
http://www3.interscience.wiley.com/cgi-bin/jhome/109668537 ↗
http://www3.interscience.wiley.com/cgi-bin/jhome/76507645 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/ana.24309 ↗
- Languages:
- English
- ISSNs:
- 0364-5134
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1043.140000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 4204.xml