{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T23:37:11Z","timestamp":1790897831801,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow\u2010up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball\u2010and\u2010Stick models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas\u2010based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball\u2010and\u2010Stick model has the potential to provide novel information about the microstructure of damaged tissue.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow\u2010up. Our study highlights the potential of the Ball\u2010and\u2010Stick model in providing additional insights into the microstructure of the damaged tissue.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.3159","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T01:45:40Z","timestamp":1696038340000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8852-1115","authenticated-orcid":false,"given":"Haykel","family":"Snoussi","sequence":"first","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France Universit\u00e9 de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074 Rennes France"},{"name":"Department of Radiology Boston Children's Hospital, Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Cohen\u2010Adad","sequence":"additional","affiliation":[{"name":"NeuroPoly Lab Institute of Biomedical Engineering, Polytechnique Montreal Montreal Quebec Canada"},{"name":"Functional Neuroimaging Unit CRIUGM, Universit\u00e9 de Montr\u00e9al Montr\u00e9al Quebec Canada"},{"name":"Mila \u2013 Quebec AI Institute Montr\u00e9al Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beno\u00eet","family":"Comb\u00e8s","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France Universit\u00e9 de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074 Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c9lise","family":"Bannier","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France Universit\u00e9 de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074 Rennes France"},{"name":"Department of Radiology Rennes University Hospital Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9133-1803","authenticated-orcid":false,"given":"Slimane","family":"Tounekti","sequence":"additional","affiliation":[{"name":"Department of Radiology Thomas Jefferson University Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Kerbrat","sequence":"additional","affiliation":[{"name":"Departement of Neurology Rennes University Hospital Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Barillot","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France Universit\u00e9 de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074 Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Caruyer","sequence":"additional","affiliation":[{"name":"Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France Universit\u00e9 de Rennes, CNRS, Inria, Inserm, IRISA UMR 6074 Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awm110"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.64.9.1302"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3495(94)80775-1"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.09.018"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10609"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nic.2008.09.005"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.05.010"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.10.041"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.10.009"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awy352"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2014.00008"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.01.045"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.1515"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.06.040"},{"key":"e_1_2_9_16_1","volume-title":"Modern applied statistics with S","author":"Ripley B. 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