1. Winters, C, van Wegen, EE, Daffertshofer, A, et al. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair. 2015;29:614-622.
Google Scholar |
SAGE Journals |
ISI2. Prabhakaran, S, Zarahn, E, Riley, C, et al. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:64-71.
Google Scholar |
SAGE Journals |
ISI3. Smith, MC, Byblow, WD, Barber, PA, et al. Proportional recovery from lower limb motor impairment after stroke. Stroke. 2017;48:1400-1403.
Google Scholar |
Crossref |
Medline |
ISI4. Hope, TMH, Friston, K, Price, CJ, et al. Recovery after stroke: Not so proportional after all?. Brain. 2019;142:15-22.
Google Scholar |
Crossref |
Medline5. Feng, W, Wang, J, Chhatbar, PY, et al. Corticospinal tract lesion load: An imaging biomarker for stroke motor outcomes. Ann Neurol. 2015;78:860-870.
Google Scholar |
Crossref |
Medline |
ISI6. Granziera, C, Daducci, A, Meskaldji, DE, et al. A new early and automated MRI-based predictor of motor improvement after stroke. Neurology. 2012;79:39-46.
Google Scholar |
Crossref |
Medline7. Liu, G, Tan, S, Dang, C, et al. Motor Recovery Prediction With Clinical Assessment and Local Diffusion Homogeneity After Acute Subcortical Infarction. Stroke. 2017;48:2121-2128.
Google Scholar |
Crossref |
Medline8. Buch, ER, Rizk, S, Nicolo, P, et al. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology. 2016;86:1924-1925.
Google Scholar |
Crossref |
Medline9. Rehme, AK, Volz, LJ, Feis, DL, et al. Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional imaging. Hum Brain Mapp. 2015;36:4553-4565.
Google Scholar |
Crossref |
Medline10. Gauthier, LV, Taub, E, Mark, VW, et al. Improvement after constraint-induced movement therapy is independent of infarct location in chronic stroke patients. Stroke. 2009;40:2468-2472.
Google Scholar |
Crossref |
Medline |
ISI11. Lo, R, Gitelman, D, Levy, R, et al. Identification of critical areas for motor function recovery in chronic stroke subjects using voxel-based lesion symptom mapping. Neuroimage. 2010;49:9-18.
Google Scholar |
Crossref |
Medline |
ISI12. Siegel, JS, Ramsey, LE, Snyder, AZ, et al. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci USA. 2016;113:E4367-E4376.
Google Scholar |
Crossref |
Medline |
ISI13. Burke, E, Dobkin, BH, Noser, EA, et al. Predictors and biomarkers of treatment gains in a clinical stroke trial targeting the lower extremity. Stroke. 2014;45:2379-2384.
Google Scholar |
Crossref |
Medline |
ISI14. Kim, B, Winstein, C. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review. Neurorehabil Neural Repair. 2017;31:3-24.
Google Scholar |
SAGE Journals |
ISI15. Quallo, MM, Price, CJ, Ueno, K, et al. Gray and white matter changes associated with tool-use learning in macaque monkeys. Proc Natl Acad Sci USA. 2009;106:18379-18384.
Google Scholar |
Crossref |
Medline |
ISI16. Gryga, M, Taubert, M, Dukart, J, et al. Bidirectional gray matter changes after complex motor skill learning. Front Syst Neurosci. 2012;6:37.
Google Scholar |
Crossref |
Medline17. Aljondi, R, Szoeke, C, Steward, C, et al. A decade of changes in brain volume and cognition. Brain Imaging Behav. 2019;13:554-563.
Google Scholar |
Crossref |
Medline18. Dekker, I, Eijlers, AJ, Popescu, V, et al. Predicting clinical progression in multiple sclerosis after 6 and 12 years. Eur J Neurol. 2019;26:893-902.
Google Scholar |
Crossref |
Medline19. Obermeyer, Z, Emanuel, EJ. Predicting the future—Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216-1219.
Google Scholar |
Crossref |
Medline20. He, H, Garcia, EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21:1263-1284.
Google Scholar |
Crossref |
ISI21. Li, J, Cheng, K, Wang, S, et al. Feature selection: A data perspective. ACM Comput Surv. 2017;50:1-45.
Google Scholar |
Crossref22. Sagi, O, Rokach, L. Ensemble learning: A survey. Wiley Interdiscip Rev Data Min Knowl Discov. 2018;8:e1249.
Google Scholar |
Crossref23. Liu, G, Tan, S, Peng, K, et al. Network change in the ipsilesional cerebellum is correlated with motor recovery following unilateral pontine infarction. Eur J Neurol. 2019;26:1266-1273.
Google Scholar |
Crossref |
Medline24. Liu, G, Dang, C, Chen, X, et al. Structural remodeling of white matter in the contralesional hemisphere is correlated with early motor recovery in patients with subcortical infarction. Restor Neurol Neurosci. 2015;33:309-319.
Google Scholar |
Crossref |
Medline25. Liu, G, Dang, C, Chen, X, et al. Increased spontaneous neuronal activity in structurally damaged cortex is correlated with early motor recovery in patients with subcortical infarction. Eur J Neurol. 2015;22:1540-1547.
Google Scholar |
Crossref |
Medline26. Mori, S, Wu, D, Ceritoglu, C, et al. MRICloud: Delivering high-throughput MRI neuroinformatics as cloud-based software as a service. Comput Sci Eng. 2016;18:21-35.
Google Scholar |
Crossref27. Tang, X, Oishi, K, Faria, AV, et al. Bayesian parameter estimation and segmentation in the multi-atlas random orbit model. PLoS One. 2013;8:e65591.
Google Scholar |
Crossref |
Medline28. Tang, X, Crocetti, D, Kutten, K, et al. Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: Testing using data with a broad range of anatomical and photometric profiles. Front Neurosci. 2015;9:61.
Google Scholar |
Crossref |
Medline29. Chawla, NV, Bowyer, KW, Hall, LO, Kegelmeyer, WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321-357.
Google Scholar |
Crossref |
ISI30. Brown, G, Pocock, A, Zhao, MJ, et al. Conditional likelihood maximisation: A unifying framework for information theoretic feature selection. J Mach Learn Res. 2012;13:27-66.
Google Scholar |
ISI31. Robnik-Šikonja, M, Kononenko, I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn. 2003;53:23-69.
Google Scholar |
Crossref |
ISI32. Breiman, L . Bagging predictors. Mach Learn. 1996;24:123-140.
Google Scholar |
Crossref |
ISI33. Friedman, JH . Greedy function approximation: A gradient boosting machine. Ann Stat. 2001:1189-1232.
Google Scholar |
Crossref |
ISI34. Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825-2830.
Google Scholar |
ISI35. Byblow, WD, Stinear, CM, Barber, PA, et al. Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol. 2015;78:848-859.
Google Scholar |
Crossref |
Medline |
ISI36. Stinear, C . Prediction of recovery of motor function after stroke. Lancet Neurol. 2010;9:1228-1232.
Google Scholar |
Crossref |
Medline |
ISI37. Escobar, I, Xu, J, Jackson, CW, Perez-Pinzon, MA. Altered neural networks in the Papez Circuit: Implications for cognitive dysfunction after cerebral ischemia. J Alzheim Dis. 2019;67:425-446.
Google Scholar |
Crossref |
Medline38. Leung, AW, Cheng, SK, Mak, AK, et al. Functional gain in hemorrhagic stroke patients is predicted by functional level and cognitive abilities measured at hospital admission. NeuroRehabilitation. 2010;27:351-358.
Google Scholar |
Crossref |
Medline39. Öneş, K, Yalçinkaya, EY, Toklu, BÇ, Çağlar, N. Effects of age, gender, and cognitive, functional and motor status on functional outcomes of stroke rehabilitation. NeuroRehabilitation. 2009;25:241-249.
Google Scholar |
Crossref |
Medline |
ISI40. Cirstea, CM, Ptito, A, Levin, MF. Feedback and cognition in arm motor skill reacquisition after stroke. Stroke. 2006;37:1237-1242.
Google Scholar |
Crossref |
Medline |
ISI41. McEwen, SE, Huijbregts, MP, Ryan, JD, Polatajko, HJ. Cognitive strategy use to enhance motor skill acquisition post-stroke: A critical review. Brain Inj. 2009;23:263-277.
Google Scholar |
Crossref |
Medline |
ISI42. Fan, F, Zhu, C, Chen, H, et al. Dynamic brain structural changes after left hemisphere subcortical stroke. Hum Brain Mapp. 2013;34:1872-1881.
Google Scholar |
Crossref |
Medline |
ISI43. Cai, J, Ji, Q, Xin, R, et al. Contralesional cortical structural reorganization contributes to motor recovery after sub-cortical stroke: A longitudinal voxel-based morphometry study. Front Hum Neurosci. 2016;10:393.
Google Scholar |
Crossref |
Medline44. Liu, H, Peng, X, Dahmani, L, et al. Patterns of motor recovery and structural neuroplasticity after basal ganglia infarcts. Neurology. 2020;95:e1174-e1187.
Google Scholar |
Crossref |
Medline45. Gauthier, LV, Taub, E, Perkins, C, et al. Remodeling the brain plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:1520-1525.
Google Scholar |
Crossref |
Medline |
ISI46. Wilkins, KB, Owen, M, Ingo, C, et al. Neural plasticity in moderate to severe chronic stroke following a device-assisted task-specific arm/hand intervention. Front Neurol. 2017;8:284.
Google Scholar |
Crossref |
Medline47. Weimar, C, Ziegler, A, Konig, IR, et al. Predicting functional outcome and survival after acute ischemic stroke. J Neurol. 2002;249:888-895.
Google Scholar |
Crossref |
Medline |
ISI48. Stinear, CM, Byblow, WD, Ackerley, SJ, et al. PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol. 2017;4:811-820.
Google Scholar |
Crossref |
Medline49. Tozlu, C, Edwards, D, Boes, A, et al. Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. Neurorehabil Neural Repair. 2020;34:428-439.
Google Scholar |
SAGE Journals |
ISI
Comments (0)