{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T04:44:22Z","timestamp":1783399462442,"version":"3.54.6"},"reference-count":73,"publisher":"SAGE Publications","issue":"13-14","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neurotrauma"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:p>\n                    The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u200992; GOSE\u2009=\u20098) and incomplete (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u200987; GOSE &lt;8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC],\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009&lt;\u20090.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009&lt;\u20090.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.\n                  <\/jats:p>","DOI":"10.1089\/neu.2022.0365","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T02:16:48Z","timestamp":1679969808000},"page":"1317-1338","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study"],"prefix":"10.1177","volume":"40","author":[{"given":"Ma\u00edra","family":"Siqueira Pinto","sequence":"first","affiliation":[{"name":"Department of Radiology, Antwerp University Hospital, Antwerp, Belgium."},{"name":"imec-Vision Lab, University of Antwerp, Antwerp, Belgium."},{"name":"\u03bcNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Winzeck","sequence":"additional","affiliation":[{"name":"BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom."},{"name":"Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgenios N.","family":"Kornaropoulos","sequence":"additional","affiliation":[{"name":"Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Richter","sequence":"additional","affiliation":[{"name":"Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Paolella","sequence":"additional","affiliation":[{"name":"imec-Vision Lab, University of Antwerp, Antwerp, Belgium."},{"name":"\u03bcNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium."},{"name":"Icometrix, Leuven, Belgium."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta M.","family":"Correia","sequence":"additional","affiliation":[{"name":"MRC Cognition and Brain Sciences Unit, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Glocker","sequence":"additional","affiliation":[{"name":"BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Williams","sequence":"additional","affiliation":[{"name":"Wolfson Brain Imaging Centre, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Vik","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway."},{"name":"Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jussi P.","family":"Posti","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery and Turku Brain Injury Center, Turku University Hospital and University of Turku, Turku, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asta","family":"Haberg","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway."},{"name":"Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Stenberg","sequence":"additional","affiliation":[{"name":"Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway."},{"name":"Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter-Jan","family":"Guns","sequence":"additional","affiliation":[{"name":"Physiopharmacology, University of Antwerp, Antwerp, Belgium."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnold J.","family":"den Dekker","sequence":"additional","affiliation":[{"name":"imec-Vision Lab, University of Antwerp, Antwerp, Belgium."},{"name":"\u03bcNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David K.","family":"Menon","sequence":"additional","affiliation":[{"name":"Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Sijbers","sequence":"additional","affiliation":[{"name":"imec-Vision Lab, University of Antwerp, Antwerp, Belgium."},{"name":"\u03bcNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter","family":"Van Dyck","sequence":"additional","affiliation":[{"name":"Department of Radiology, Antwerp University Hospital, Antwerp, Belgium."},{"name":"mVISION, University of Antwerp, Antwerp, 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