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Prog Rehabil            Med, 2020, 5: 20200006.","DOI":"10.2490\/prm.20200006"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6) Warrington              S, Bryant              KL,                Khrapitchev              AA, et al.:            XTRACT\u2014standardised protocols for automated tractography in the human and            macaque brain. Neuroimage, 2020,            217: 116923.","DOI":"10.1016\/j.neuroimage.2020.116923"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7) Koyama              T, Mochizuki              M, Uchiyama              Y, et al.:            Applicability of fractional anisotropy from standardized automated            tractography for outcome prediction of patients after stroke. J            Phys Ther Sci, 2023, 35:            838\u2013844.","DOI":"10.1589\/jpts.35.838"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8) Mochizuki              M, Uchiyama              Y, Domen              K, et al.:            Automated tractography for the assessment of aphasia in acute care stroke            rehabilitation: a case series. Prog Rehabil Med,            2023, 8: 20230041.","DOI":"10.2490\/prm.20230041"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9) Mochizuki              M, Uchiyama              Y, Domen              K, et al.:            Applicability of automated tractography during acute care stroke            rehabilitation. J Phys Ther Sci, 2023,            35: 156\u2013162.","DOI":"10.1589\/jpts.35.156"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10) Koyama              T, Mochizuki              M, Uchiyama              Y, et al.:            Outcome prediction by combining corticospinal tract lesion load with            diffusion-tensor fractional anisotropy in patients after hemorrhagic            stroke. Prog Rehabil Med, 2024,            9: 20240001.","DOI":"10.2490\/prm.20240001"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11) Mochizuki              M, Uchiyama              Y, Domen              K, et al.:            Clinical applicability of automated tractography for stroke            rehabilitation: Z-score conversion of fractional anisotropy. J            Phys Ther Sci, 2024, 36:            319\u2013324.","DOI":"10.1589\/jpts.36.319"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12) Koyama              T, Marumoto              K, Miyake              H, et al.:            Relationship between diffusion tensor fractional anisotropy and motor            outcome in patients with hemiparesis after corona radiata infarct.            J Stroke Cerebrovasc Dis, 2013, 22:            1355\u20131360.","DOI":"10.1016\/j.jstrokecerebrovasdis.2013.02.017"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13) Uchiyama              Y, Domen              K, Koyama              T: Outcome            prediction of patients with intracerebral hemorrhage by measurement of lesion volume in            the corticospinal tract on computed tomography. Prog Rehabil            Med, 2021, 6: 20210050.","DOI":"10.2490\/prm.20210050"},{"key":"14","doi-asserted-by":"publisher","unstructured":"14) van                Swieten              JC, Koudstaal              PJ, Visser              MC, et al.:            Interobserver agreement for the assessment of handicap in stroke            patients. Stroke, 1988,            19: 604\u2013607.","DOI":"10.1161\/01.STR.19.5.604"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15) Tournier              JD, Smith              R, Raffelt              D, et al.:            MRtrix3: A fast, flexible and open software framework for medical image            processing and visualisation. Neuroimage,            2019, 202: 116137.","DOI":"10.1016\/j.neuroimage.2019.116137"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16) Jenkinson              M, Beckmann              CF, Behrens              TE, et al.:            FSL. Neuroimage, 2012,            62: 782\u2013790.","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17) Brunnstrom              S: Motor            testing procedures in hemiplegia: based on sequential recovery stages.            Phys Ther, 1966, 46:            357\u2013375.","DOI":"10.1093\/ptj\/46.4.357"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18) Paternostro-Sluga              T,                Grim-Stieger              M, Posch              M, et al.:            Reliability and validity of the Medical Research Council (MRC) scale and            a modified scale for testing muscle strength in patients with radial            palsy. J Rehabil Med, 2008,            40: 665\u2013671.","DOI":"10.2340\/16501977-0235"},{"key":"19","doi-asserted-by":"publisher","unstructured":"19) Ward              NS, Brown              MM, Thompson              AJ, et al.:            Neural correlates of motor recovery after stroke: a longitudinal fMRI            study. Brain, 2003, 126:            2476\u20132496.","DOI":"10.1093\/brain\/awg245"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20) Grefkes              C, Ward              NS: Cortical            reorganization after stroke: how much and how            functional? Neuroscientist, 2014,            20: 56\u201370.","DOI":"10.1177\/1073858413491147"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21) Kang              HJ, Stewart              R, Park              MS, et al.:            White matter hyperintensities and functional outcomes at 2 weeks and 1            year after stroke. Cerebrovasc Dis, 2013,            35: 138\u2013145.","DOI":"10.1159\/000346604"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22) Etherton              MR, Wu              O, Rost              NS: Recent            advances in leukoaraiosis: white matter structural integrity and functional outcomes            after acute ischemic stroke. Curr Cardiol Rep,            2016, 18: 123.","DOI":"10.1007\/s11886-016-0803-0"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23) Christidi              F, Tsiptsios              D, Sousanidou              A, et al.:            The clinical utility of leukoaraiosis as a prognostic indicator in            ischemic stroke patients. Neurol Int,          2022, 14: 952\u2013980.","DOI":"10.3390\/neurolint14040076"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24) Fazekas              F, Chawluk              JB, Alavi              A, et al.:            MR signal abnormalities at 1.5 T in Alzheimer\u2019s dementia and normal            aging. AJR Am J Roentgenol, 1987,            149: 351\u2013356.","DOI":"10.2214\/ajr.149.2.351"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25) Scheltens              P, Barkhof              F, Leys              D, et al.:            A semiquantative rating scale for the assessment of signal            hyperintensities on magnetic resonance imaging. J Neurol            Sci, 1993, 114:            7\u201312.","DOI":"10.1016\/0022-510X(93)90041-V"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26) Takashima              N, Arima              H, Kita              Y, et            al. Shiga Stroke and Heart Attack Registry            Group: Two-year recurrence after first-ever stroke in a            general population of 1.4 million Japanese patients\u2014the Shiga stroke and heart attack            registry study. Circ J, 2020,            84: 943\u2013948.","DOI":"10.1253\/circj.CJ-20-0024"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27) Kolmos              M,                Christoffersen              L, Kruuse              C: Recurrent            ischemic stroke\u2014a systematic review and meta-analysis. J Stroke            Cerebrovasc Dis, 2021, 30: 105935.","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.105935"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28) Nakanishi              Y, Furuta              Y, Hata              J, et al.:            Long-term trends in the 5-year risk of recurrent stroke over a half            century in a Japanese community: the Hisayama study. J            Atheroscler Thromb, 2022, 29:            1759\u20131773.","DOI":"10.5551\/jat.63344"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29) Mukherjee              D, Patil              CG:            Epidemiology and the global burden of stroke.            World Neurosurg, 2011, 76:            S85\u2013S90.","DOI":"10.1016\/j.wneu.2011.07.023"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30) Feigin              VL, Norrving              B, Mensah              GA: Global            burden of stroke. 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