{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:13:08Z","timestamp":1790107988149,"version":"4.0.1"},"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CIHR 2015-2016\/358832"],"award-info":[{"award-number":["CIHR 2015-2016\/358832"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Quebec Pain Research Network"},{"name":"Stichting Neurobionics Foundations"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Due to the complexity of pain, involving physical, psychological, emotional and social aspects, we are still unable to objectively quantify or fully understand this subjective experience. An increasing number of studies have attempted to identify biomarkers of pain using brain imaging tools like magnetoencephalography (MEG). In this study, we used machine learning to investigate the potential of MEG data as a biomarker for chronic pain and used this biomarker to quantify spinal cord stimulation (SCS) treatment effect.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study population consisted of 25 patients with SCS, for whom we recorded resting-state MEG during tonic, burst and sham stimulation, 25 patients with chronic pain and 25 pain-free controls. We derived average power spectral densities across each of the 94 automated anatomical labeling atlas based brain regions and extracted six spectral features: the alpha peak frequency, alpha power ratio, and average power across the theta, alpha, beta, and low-gamma bands. Based on these features, we used automated machine learning to find the optimal combination of machine learning methods to create classification and regression models for pain and pain intensity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The theta power and alpha power ratio were the most promising features to classify chronic pain with an accuracy of 76%. The classification model outputs and self-reported pain scores of patients with SCS showed a Spearman correlation coefficient of 0.12. A regression model based on pain scores of all participants showed Spearman correlation coefficients between 0.27 and 0.41.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study achieved a promising 76% accuracy in classifying patients with chronic pain and pain-free controls using the theta power or alpha power ratio. However, this model\u2019s output poorly correlated with pain scores of patients with SCS. A larger variety of input features and outcome parameters is recommended.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0337726","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:25:46Z","timestamp":1764959146000},"page":"e0337726","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data"],"prefix":"10.1371","volume":"20","author":[{"given":"Bart","family":"Witjes","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5086-7153","authenticated-orcid":true,"given":"Martijn P. A.","family":"Starmans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank J.P.M.","family":"Huygen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7210-1693","authenticated-orcid":true,"given":"Cecile C.","family":"de Vos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"issue":"1","key":"pone.0337726.ref001","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1097\/j.pain.0000000000001384","article-title":"Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11)","volume":"160","author":"R-D Treede","year":"2019","journal-title":"Pain"},{"issue":"10289","key":"pone.0337726.ref002","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/S0140-6736(21)00393-7","article-title":"Chronic pain: an update on burden, best practices, and new advances","volume":"397","author":"SP Cohen","year":"2021","journal-title":"Lancet"},{"issue":"4","key":"pone.0337726.ref003","article-title":"Neuroimaging-based biomarkers for pain: state of the field and current directions","volume":"4","author":"MM van der Miesen","year":"2019","journal-title":"Pain Rep"},{"issue":"10","key":"pone.0337726.ref004","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1038\/nrneurol.2017.122","article-title":"Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations","volume":"13","author":"KD Davis","year":"2017","journal-title":"Nat Rev Neurol"},{"issue":"7","key":"pone.0337726.ref005","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/s41582-020-0362-2","article-title":"Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities","volume":"16","author":"KD Davis","year":"2020","journal-title":"Nat Rev Neurol"},{"key":"pone.0337726.ref006","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1186418","article-title":"In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?","volume":"17","author":"MM Rockholt","year":"2023","journal-title":"Frontiers in Neuroscience"},{"key":"pone.0337726.ref007","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.pain.2007.07.028","article-title":"Spinal cord stimulation versus conventional medical management for neuropathic pain: a multicentre randomised controlled trial in patients with failed back surgery syndrome","volume":"132","author":"K Kumar","year":"2007","journal-title":"Pain"},{"issue":"11","key":"pone.0337726.ref008","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1016\/j.pain.2014.08.031","article-title":"Spinal cord stimulation in patients with painful diabetic neuropathy: a multicentre randomized clinical trial","volume":"155","author":"CC de Vos","year":"2014","journal-title":"Pain"},{"issue":"2","key":"pone.0337726.ref009","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3171\/JNS\/2008\/108\/2\/0292","article-title":"Effect of spinal cord stimulation for chronic complex regional pain syndrome Type I: five-year final follow-up of patients in a randomized controlled trial","volume":"108","author":"MA Kemler","year":"2008","journal-title":"J Neurosurg"},{"key":"pone.0337726.ref010","unstructured":"Brainstorm,\u00a0Available from: http:\/\/neuroimage.usc.edu\/brainstorm"},{"key":"pone.0337726.ref011","unstructured":"Starmans M, van der Voort SR, Phil T, Timbergen MJM, Vos M, Padmos GA. Reproducible radiomics through automated machine learning validated on twelve clinical applications. 2021. https:\/\/arxiv.org\/abs\/2108.08618"},{"key":"pone.0337726.ref012","unstructured":"Starmans MPA, van der Voort SR, Phil T, Klein S. Workflow for optimal radiomics classification (WORC). https:\/\/doi.org\/10.5281\/zenodo3840534"},{"issue":"2","key":"pone.0337726.ref013","article-title":"Magnetoencephalography reveals increased slow-to-fast alpha power ratios in patients with chronic pain","volume":"6","author":"B Witjes","year":"2021","journal-title":"Pain Rep"},{"issue":"5","key":"pone.0337726.ref014","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.neurom.2022.12.005","article-title":"Heterogeneous Cortical Effects of Spinal Cord Stimulation","volume":"26","author":"B Witjes","year":"2023","journal-title":"Neuromodulation"},{"issue":"6","key":"pone.0337726.ref015","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/79.962275","article-title":"Electromagnetic brain mapping","volume":"18","author":"S Baillet","year":"2001","journal-title":"IEEE Signal Process Mag"},{"key":"pone.0337726.ref016","doi-asserted-by":"crossref","first-page":"879716","DOI":"10.1155\/2011\/879716","article-title":"Brainstorm: a user-friendly application for MEG\/EEG analysis","volume":"2011","author":"F Tadel","year":"2011","journal-title":"Comput Intell Neurosci"},{"key":"pone.0337726.ref017","first-page":"13","article-title":"MEG\/EEG Group Analysis With Brainstorm","author":"F Tadel","year":"2019","journal-title":"Frontiers in Neuroscience"},{"key":"pone.0337726.ref018","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.neuroimage.2012.10.001","article-title":"Good practice for conducting and reporting MEG research","volume":"65","author":"J Gross","year":"2013","journal-title":"Neuroimage"},{"issue":"1","key":"pone.0337726.ref019","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s10194-022-01500-1","article-title":"Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning","volume":"23","author":"F-J Hsiao","year":"2022","journal-title":"J Headache Pain"},{"issue":"1","key":"pone.0337726.ref020","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/s10194-023-01677-z","article-title":"Characteristic oscillatory brain networks for predicting patients with chronic migraine","volume":"24","author":"F-J Hsiao","year":"2023","journal-title":"J Headache Pain"},{"issue":"5","key":"pone.0337726.ref021","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.neurom.2022.11.011","article-title":"Holistic Treatment Response: An International Expert Panel Definition and Criteria for a New Paradigm in the Assessment of Clinical Outcomes of Spinal Cord Stimulation","volume":"26","author":"RM Levy","year":"2023","journal-title":"Neuromodulation"},{"issue":"12","key":"pone.0337726.ref022","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.3390\/jcm9124131","article-title":"Predicting the Response of High Frequency Spinal Cord Stimulation in Patients with Failed Back Surgery Syndrome: A Retrospective Study with Machine Learning Techniques","volume":"9","author":"L Goudman","year":"2020","journal-title":"J Clin Med"},{"issue":"5","key":"pone.0337726.ref023","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1227\/neu.0000000000001855","article-title":"Development of Machine Learning-Based Models to Predict Treatment Response to Spinal Cord Stimulation","volume":"90","author":"A Hadanny","year":"2022","journal-title":"Neurosurgery"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337726","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:25:50Z","timestamp":1764959150000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0337726"}},"subtitle":[],"editor":[{"given":"Carlos Eduardo","family":"Thomaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":23,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0337726","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,5]]}}}