Neural function, injury, and stroke subtype predict treatment gains after stroke

Erin Burke Quinlan; Lucy Dodakian; Jill See; Alison McKenzie; Vu Le; Mike Wojnowicz; Babak Shahbaba; Steven C. Cramer

doi:10.1002/ana.24309

Neural function, injury, and stroke subtype predict treatment gains after stroke

Erin Burke Quinlan, Lucy Dodakian, Jill See, Alison McKenzie, Vu Le, Mike Wojnowicz, Babak Shahbaba, Steven C. Cramer^*

^*Corresponding author for this work

Social Genetic & Developmental Psychiatry

Research output: Contribution to journal › Article › peer-review

168 Citations (Scopus)

Abstract

Objective: 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.

Methods: 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).

Results: 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 (r² = 0.44, p = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >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.

Interpretation: 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.

Original language	English
Pages (from-to)	132-145
Number of pages	14
Journal	Annals of Neurology
Volume	77
Issue number	1
Early online date	4 Dec 2014
DOIs	https://doi.org/10.1002/ana.24309
Publication status	Published - 1 Jan 2015

Access to Document

10.1002/ana.24309

Cite this

@article{b8b32eddd9b44e5892a051d36e156030,

title = "Neural function, injury, and stroke subtype predict treatment gains after stroke",

abstract = "Objective: 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. Methods: 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). Results: 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 (r2 = 0.44, p = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >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. Interpretation: 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.",

author = "Quinlan, {Erin Burke} and Lucy Dodakian and Jill See and Alison McKenzie and Vu Le and Mike Wojnowicz and Babak Shahbaba and Cramer, {Steven C.}",

year = "2015",

month = jan,

day = "1",

doi = "10.1002/ana.24309",

language = "English",

volume = "77",

pages = "132--145",

journal = "Annals of Neurology",

issn = "0364-5134",

publisher = "John Wiley and Sons Inc.",

number = "1",

}

TY - JOUR

T1 - Neural function, injury, and stroke subtype predict treatment gains after stroke

AU - Quinlan, Erin Burke

AU - Dodakian, Lucy

AU - See, Jill

AU - McKenzie, Alison

AU - Le, Vu

AU - Wojnowicz, Mike

AU - Shahbaba, Babak

AU - Cramer, Steven C.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Objective: 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. Methods: 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). Results: 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 (r2 = 0.44, p = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >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. Interpretation: 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.

AB - Objective: 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. Methods: 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). Results: 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 (r2 = 0.44, p = 0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >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. Interpretation: 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.

UR - http://www.scopus.com/inward/record.url?scp=84920767966&partnerID=8YFLogxK

U2 - 10.1002/ana.24309

DO - 10.1002/ana.24309

M3 - Article

C2 - 25382315

AN - SCOPUS:84920767966

SN - 0364-5134

VL - 77

SP - 132

EP - 145

JO - Annals of Neurology

JF - Annals of Neurology

IS - 1

ER -

Neural function, injury, and stroke subtype predict treatment gains after stroke

Abstract

Access to Document

Other files and links

Fingerprint

Cite this