{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T04:47:20Z","timestamp":1785646040345,"version":"3.56.0"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China","award":["JYB2025XDXM606"],"award-info":[{"award-number":["JYB2025XDXM606"]}]},{"DOI":"10.13039\/501100012401","name":"Beijing Science and Technology Planning Project","doi-asserted-by":"crossref","award":["Z25110100040000"],"award-info":[{"award-number":["Z25110100040000"]}],"id":[{"id":"10.13039\/501100012401","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Natural Science Foundation","award":["4262007"],"award-info":[{"award-number":["4262007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Visual evoked potential (VEP) is widely used to assess optic nerve function, but its ability to differentiate optic neuropathies remains limited. This study investigated the diagnostic value of spin-exchange relaxation-free magnetoencephalography (SERF-MEG), alone and in combination with VEP, using machine learning. A total of 142 eyes, including 71 healthy controls (HC), 34 eyes with optic neuritis (ON), and 37 eyes with ischemic optic neuropathy (ION), were enrolled. Three feature sets were constructed from VEP, SERF-MEG, and their combination. Repeated stratified 5-fold nested cross-validation was used to evaluate nine supervised machine learning algorithms, and SHAP analysis was used to evaluate the interpretability of the models. The multilayer perceptron model obtained an AUC of 0.792 and an MCC of 0.470, whereas the combination VEP\u2013MEG model performed the best in differentiating HC from optic neuropathies. The MEG model fared better than the VEP and combined models for distinguishing ON from ION, with logistic regression obtaining the greatest AUC of 0.847. Cortical network measurements and visually evoked response properties were found to contribute the most to model prediction, according to SHAP analysis. These results imply that SERF-MEG may enhance the diagnosis and differential diagnosis of optic neuropathies by offering supplementary neurophysiological data beyond traditional VEP.<\/jats:p>","DOI":"10.3390\/bioengineering13080885","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:20:18Z","timestamp":1785486018000},"page":"885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Integrating SERF-MEG and VEP for Diagnosis and Differential Diagnosis of Optic Neuropathies"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-7749","authenticated-orcid":false,"given":"Helei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Hangzhou National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0512-7471","authenticated-orcid":false,"given":"Yuankun","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Hangzhou National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-1771","authenticated-orcid":false,"given":"Yu","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Hangzhou National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Hangzhou National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China"},{"name":"State Key Laboratory of Traditional Chinese Medicine Syndrome, Hangzhou 310051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinda","family":"Song","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Hangzhou National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China"},{"name":"State Key Laboratory of Traditional Chinese Medicine Syndrome, Hangzhou 310051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S1474-4422(13)70259-X","article-title":"Optic neuritis","volume":"13","author":"Toosy","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.preteyeres.2008.11.002","article-title":"Ischemic optic neuropathy","volume":"28","author":"Hayreh","year":"2009","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/S1474-4422(22)00200-9","article-title":"Diagnosis and classification of optic neuritis","volume":"21","author":"Petzold","year":"2022","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Salvetat, M.L., Pellegrini, F., Spadea, L., Salati, C., and Zeppieri, M. (2023). Non-Arteritic Anterior Ischemic Optic Neuropathy (NA-AION): A Comprehensive Overview. Vision, 7.","DOI":"10.3390\/vision7040072"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1056\/NEJMra1413352","article-title":"Ischemic Optic Neuropathies","volume":"372","author":"Biousse","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10633-025-10042-1","article-title":"ISCEV standard for clinical visual evoked potentials (2025 update)","volume":"151","author":"Habjan","year":"2025","journal-title":"Doc. Ophthalmol. Adv. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1038\/sj.eye.6701573","article-title":"Electrophysiological assessment of optic nerve disease","volume":"18","author":"Holder","year":"2004","journal-title":"Eye"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1002\/ana.21474","article-title":"Axonal loss and myelin in early ON loss in postacute optic neuritis","volume":"64","author":"Klistorner","year":"2008","journal-title":"Ann. Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jocn.2005.02.007","article-title":"Pattern electroretinography and visual evoked potentials in optic nerve diseases","volume":"13","author":"Atilla","year":"2006","journal-title":"J. Clin. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1016\/j.clinph.2006.01.013","article-title":"Electrophysiological findings in patients with nonarteritic anterior ischemic optic neuropathy","volume":"117","author":"Benedek","year":"2006","journal-title":"Clin. Neurophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/brain\/awu335","article-title":"Vision and vision-related outcome measures in multiple sclerosis","volume":"138","author":"Balcer","year":"2015","journal-title":"Brain J. Neurol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/hbm.21424","article-title":"Functional connectivity changes in multiple sclerosis patients: A graph analytical study of MEG resting state data","volume":"34","author":"Schoonheim","year":"2013","journal-title":"Hum. Brain Mapp."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nn.4502","article-title":"Network neuroscience","volume":"20","author":"Bassett","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nn.4504","article-title":"Magnetoencephalography for brain electrophysiology and imaging","volume":"20","author":"Baillet","year":"2017","journal-title":"Nat. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.tins.2022.05.008","article-title":"Magnetoencephalography with optically pumped magnetometers (OPM-MEG): The next generation of functional neuroimaging","volume":"45","author":"Brookes","year":"2022","journal-title":"Trends Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/00107514.2023.2182950","article-title":"Quantum enabled functional neuroimaging: The why and how of magnetoencephalography using optically pumped magnetometers","volume":"63","author":"Schofield","year":"2022","journal-title":"Contemp. Phys."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Miraglia, F., Vecchio, F., Pappalettera, C., Nucci, L., Cotelli, M., Judica, E., Ferreri, F., and Rossini, P.M. (2022). Brain Connectivity and Graph Theory Analysis in Alzheimer\u2019s and Parkinson\u2019s Disease: The Contribution of Electrophysiological Techniques. Brain Sci., 12.","DOI":"10.3390\/brainsci12030402"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.neuroimage.2016.05.070","article-title":"How reliable are MEG resting-state connectivity metrics?","volume":"138","author":"Colclough","year":"2016","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/nrn3801","article-title":"Modern network science of neurological disorders","volume":"15","author":"Stam","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.neuroimage.2013.10.022","article-title":"Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: An MEG source-space study","volume":"88","author":"Tewarie","year":"2014","journal-title":"NeuroImage"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neuroimage.2019.05.048","article-title":"A better way to define and describe Morlet wavelets for time-frequency analysis","volume":"199","author":"Cohen","year":"2019","journal-title":"NeuroImage"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neuroimage.2011.01.055","article-title":"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias","volume":"55","author":"Vinck","year":"2011","journal-title":"NeuroImage"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: Uses and interpretations","volume":"52","author":"Rubinov","year":"2010","journal-title":"NeuroImage"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123667","DOI":"10.1016\/j.eswa.2024.123667","article-title":"Analysis and comparison of feature selection methods towards performance and stability","volume":"249","author":"Barbieri","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_25","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_27","first-page":"3149","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, M., Mai, L., Li, X., Bellou, A., and Wu, L. (2025). An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records. BMC Med. Inf. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-02922-y"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Miller, C., Portlock, T., Nyaga, D.M., and O\u2019Sullivan, J.M. (2024). A review of model evaluation metrics for machine learning in genetics and genomics. Front. Bioinform., 4.","DOI":"10.3389\/fbinf.2024.1457619"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"McKyton, A., Abulafia, R., and Levin, N. (2025). Functional MRI in optic neuritis: Insights into cortical modulation and possible recovery mechanisms. Front Neurol., 16.","DOI":"10.3389\/fneur.2025.1675926"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1177\/1352458516649677","article-title":"Cortical functional modifications following optic neuritis","volume":"23","author":"Benoliel","year":"2017","journal-title":"Mult. Scler."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/8\/885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T04:20:59Z","timestamp":1785644459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/8\/885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["bioengineering13080885"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13080885","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]}}}