{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T11:21:48Z","timestamp":1793186508399,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)\/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87\u20130.88 (\u201cgood\u201d) for the SVC and 0.88\u20130.91 (\u201cgood\u201d to \u201cexcellent\u201d) for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to ALS.<\/jats:p>","DOI":"10.3389\/fneur.2021.745475","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T04:30:58Z","timestamp":1637123458000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept"],"prefix":"10.3389","volume":"12","author":[{"given":"Thomas D.","family":"Kocar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Behler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert C.","family":"Ludolph","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans-Peter","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Kassubek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1016\/S0140-6736(17)31287-4","article-title":"Amyotrophic lateral sclerosis","volume":"390","author":"van Es","year":"2017","journal-title":"Lancet."},{"key":"B2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1080\/14737175.2020.1715798","article-title":"Advanced neuroimaging approaches in amyotrophic lateral sclerosis: refining the clinical diagnosis","volume":"20","author":"Kassubek","year":"2020","journal-title":"Expert Rev Neurother."},{"key":"B3","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1080\/14737175.2018.1463160","article-title":"Neuroimaging in amyotrophic lateral sclerosis: current and emerging uses","volume":"18","author":"Agosta","year":"2018","journal-title":"Expert Rev Neurother."},{"key":"B4","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3389\/fnhum.2013.00251","article-title":"The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain","volume":"7","author":"Welsh","year":"2013","journal-title":"Front Hum Neurosci."},{"key":"B5","doi-asserted-by":"publisher","first-page":"e0167331","DOI":"10.1371\/journal.pone.0167331","article-title":"Development of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: a quantitative disease-state classification study","volume":"11","author":"Schuster","year":"2016","journal-title":"PLoS ONE."},{"key":"B6","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.nicl.2017.08.002","article-title":"Multimodal structural MRI in the diagnosis of motor neuron diseases","volume":"16","author":"Ferraro","year":"2017","journal-title":"Neuroimage Clin."},{"key":"B7","doi-asserted-by":"crossref","DOI":"10.1177\/20406223211051002","article-title":"Feature selection from magnetic resonance imaging data in ALS: a systematic review","volume":"12","author":"Kocar","year":"2021","journal-title":"Ther Adv Chronic Dis."},{"key":"B8","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3389\/fnins.2019.00135","article-title":"Machine learning in amyotrophic lateral sclerosis: achievements, pitfalls, and future directions","volume":"13","author":"Grollemund","year":"2019","journal-title":"Front Neurosci."},{"key":"B9","doi-asserted-by":"publisher","first-page":"101659","DOI":"10.1016\/j.compmedimag.2019.101659","article-title":"Texture classification of MR images of the brain in ALS using M-CoHOG: a multi-center study","volume":"79","author":"E Elahi","year":"2020","journal-title":"Comput Med Imaging Graph."},{"key":"B10","doi-asserted-by":"publisher","first-page":"102223","DOI":"10.1016\/j.nicl.2020.102223","article-title":"Focal alterations of the callosal area III in primary lateral sclerosis: an MRI planimetry and texture analysis","volume":"26","author":"M\u00fcller","year":"2020","journal-title":"Neuroimage Clin."},{"key":"B11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/s0022-510x(99)00210-5","article-title":"The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. 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