{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:23:59Z","timestamp":1792794239746,"version":"4.5.0"},"reference-count":116,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deutsche Forschungsgemeinschaft German Research Foundation","award":["\u2013390648296"],"award-info":[{"award-number":["\u2013390648296"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51\u2009\u00b1\u200916.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts\u2019 profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model\u2019s performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.<\/jats:p>","DOI":"10.1093\/braincomms\/fcac141","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T15:41:33Z","timestamp":1654011693000},"source":"Crossref","is-referenced-by-count":13,"title":["Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1290-6630","authenticated-orcid":false,"given":"Boshra","family":"Shams","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Klinik f\u00fcr Neurochirurgie mit Arbeitsbereich P\u00e4diatrische Neurochirurgie, Campus Charit\u00e9 Mitte , Charit\u00e9platz 1, 10117 Berlin, Germany"},{"name":"Cluster of Excellence: \u2018Matters of Activity. Image Space Material\u2019, Humboldt University Berlin , Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Klinik f\u00fcr Neurochirurgie mit Arbeitsbereich P\u00e4diatrische Neurochirurgie, Campus Charit\u00e9 Mitte , Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Roine","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Biomedical Engineering, Aalto University School of Science , Espoo, Finland"},{"name":"Turku Brain and Mind Center, University of Turku , Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dogu Baran","family":"Aydogan","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Biomedical Engineering, Aalto University School of Science , Espoo, Finland"},{"name":"Department of Psychiatry, Helsinki University and Helsinki University Hospital , Helsinki, Finland"},{"name":"A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland , Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Vajkoczy","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Klinik f\u00fcr Neurochirurgie mit Arbeitsbereich P\u00e4diatrische Neurochirurgie, Campus Charit\u00e9 Mitte , Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Lippert","sequence":"additional","affiliation":[{"name":"Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam , Potsdam, Germany"},{"name":"Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai , New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8630-1466","authenticated-orcid":false,"given":"Thomas","family":"Picht","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Klinik f\u00fcr Neurochirurgie mit Arbeitsbereich P\u00e4diatrische Neurochirurgie, Campus Charit\u00e9 Mitte , Charit\u00e9platz 1, 10117 Berlin, Germany"},{"name":"Cluster of Excellence: \u2018Matters of Activity. 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