The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Neural function, injury, and stroke subtype predict treatment gains after stroke
Open accessAbstract
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. ANN NEUROL 2015;77:132–145
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kernan, W. N., Ovbiagele, B., Black, H. R., Bravata, D. M., Chimowitz, M. I., Ezekowitz, M. D., Fang, M. C., Fisher, M., Furie, K. L., Heck, D. V., et al. (2014) · Stroke · doi:10.1161/str.0000000000000024 · Cited by 5,170
Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885
Patterns of Brain Activation in People at Risk for Alzheimer's Disease
Bookheimer, S. Y., Strojwas, M. H., Cohen, M. S., Saunders, A. M., Pericak-Vance, M. A., Mazziotta, J. C., & Small, G. W. (2000) · New England Journal of Medicine · doi:10.1056/nejm200008173430701 · Cited by 1,328
Kolominsky-Rabas, P. L., Weber, M., Gefeller, O., Neundoerfer, B., & Heuschmann, P. U. (2001) · Stroke · doi:10.1161/hs1201.100209 · Cited by 1,281
Hermann, D. M., & Chopp, M. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70039-x · Cited by 333
Statistical Analysis of the Primary Outcome in Acute Stroke Trials
Bath, P. M. W., Lees, K. R., Schellinger, P. D., Altman, H., Bland, M., Hogg, C., Howard, G., & Saver, J. L. (2012) · Stroke · doi:10.1161/strokeaha.111.641456 · Cited by 266 · cited 1×
“This extends to clinical trials, where higher intersubject variance means reduced study power and increased cost.(1) This issue has undergone considerable discussion in relation to acute stroke trials, where computed tomography and magnetic resonance imaging (MRI) measures are under study to improve prediction of indi…”
Cited by
Boyd, L. A., Hayward, K. S., Ward, N. S., Stinear, C. M., Rosso, C., Fisher, R. J., Carter, A. R., Leff, A. P., Copland, D. A., Carey, L. M., et al. (2017) · International Journal of Stroke · doi:10.1177/1747493017714176 · Cited by 417
Self-citationIntensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme
Ward, N. S., Brander, F., & Kelly, K. (2019) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2018-319954 · Cited by 341
Dawson, J., Pierce, D., Dixit, A., Kimberley, T. J., Robertson, M., Tarver, B., Hilmi, O., McLean, J., Forbes, K., Kilgard, M. P., et al. (2016) · Stroke · doi:10.1161/strokeaha.115.010477 · Cited by 300
Self-citationSpontaneous and Therapeutic-Induced Mechanisms of Functional Recovery After Stroke
Cassidy, J. M., & Cramer, S. C. (2017) · Translational Stroke Research · doi:10.1007/s12975-016-0467-5 · Cited by 292
Self-citationA large, open source dataset of stroke anatomical brain images and manual lesion segmentations
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.11 · Cited by 283
Self-citation
Metadata
- Authors
- Burke Quinlan, E., Dodakian, L., See, J., McKenzie, A., Le, V., Wojnowicz, M., Shahbaba, B., & Cramer, S. C.
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2014-12-04
- Volume
- 77
- Issue
- 1
- Pages
- 132-145
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- OpenAlex
- W1503485798
- Cited by
- 202
- References
- 6
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neural function, injury, and stroke subtype predict treatment gains after stroke. https://app.cochat.ai/papers/10.1002/ana.24309
- The paper
- Burke Quinlan, E., Dodakian, L., See, J., McKenzie, A., Le, V., Wojnowicz, M., Shahbaba, B., & Cramer, S. C. (2015). Neural function, injury, and stroke subtype predict treatment gains after stroke. Annals of Neurology. https://doi.org/10.1002/ana.24309
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
