{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:22:21Z","timestamp":1787026941745,"version":"build-2736575974"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"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. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2024.1402039","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T08:53:49Z","timestamp":1718182429000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study"],"prefix":"10.3389","volume":"18","author":[{"given":"Yuanqing","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Min","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei-Lei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Salvi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohua","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.cortex.2020.10.025","article-title":"The role of the superior parietal lobule in lexical processing of sign language: insights from fMRI and TMS","volume":"135","author":"Banaszkiewicz","year":"2021","journal-title":"Cortex"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"7641","DOI":"10.1073\/pnas.1018985108","article-title":"Dynamic reconfiguration of human brain networks during learning","volume":"108","author":"Bassett","year":"2011","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1111\/coa.13068","article-title":"Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models","volume":"43","author":"Bing","year":"2018","journal-title":"Clin. Otolaryngol."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci10080498","article-title":"Frontal cortical modulation of temporal visual cross-modal re-organization in adults with hearing loss","volume":"10","author":"Campbell","year":"2020","journal-title":"Brain Sci."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1111\/jon.12980","article-title":"Salience network anatomical and molecular markers are linked with cognitive dysfunction in mild cognitive impairment","volume":"32","author":"Chand","year":"2022","journal-title":"J. Neuroimaging"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1007\/s00420-020-01648-w","article-title":"Contributions and limitations of using machine learning to predict noise-induced hearing loss","volume":"94","author":"Chen","year":"2021","journal-title":"Int. Arch. Occup. Environ. Health"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1016\/S0140-6736(21)00516-X","article-title":"Hearing loss prevalence and years lived with disability, 1990-2019: findings from the global burden of Disease study 2019","volume":"397","author":"Collaborators","year":"2021","journal-title":"Lancet"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1097\/AUD.0000000000000993","article-title":"Predicting depression from hearing loss using machine learning","volume":"42","author":"Crowson","year":"2021","journal-title":"Ear Hear."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/nrn2961","article-title":"Emerging concepts for the dynamical organization of resting-state activity in the brain","volume":"12","author":"Deco","year":"2011","journal-title":"Nat. Rev. Neurosci."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the global burden of Disease study 2017","volume":"392","author":"Disease","year":"2018","journal-title":"Lancet"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1093\/bioinformatics\/16.10.906","article-title":"Support vector machine classification and validation of cancer tissue samples using microarray expression data","volume":"16","author":"Furey","year":"2000","journal-title":"Bioinformatics"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1111\/epi.17160","article-title":"Common functional connectivity alterations in focal epilepsies identified by machine learning","volume":"63","author":"Gholipour","year":"2022","journal-title":"Epilepsia"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"905487","DOI":"10.3389\/fnagi.2022.905487","article-title":"Reorganized brain functional network topology in Presbycusis","volume":"14","author":"Guan","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.pnpbp.2018.03.026","article-title":"Increased temporal variability of striatum region facilitating the early antidepressant response in patients with major depressive disorder","volume":"85","author":"Hou","year":"2018","journal-title":"Prog. Neuro-Psychopharmacol. Biol. Psychiatry"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","article-title":"Dynamic functional connectivity: promise, issues, and interpretations","volume":"80","author":"Hutchison","year":"2013","journal-title":"NeuroImage"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1109\/TMI.2023.3274351","article-title":"Multi-connectivity representation learning network for major depressive disorder diagnosis","volume":"42","author":"Kong","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"e1286","DOI":"10.1097\/MAO.0000000000003271","article-title":"Machine learning technique reveals prognostic factors of vibrant Soundbridge for conductive or mixed hearing loss patients","volume":"42","author":"Koyama","year":"2021","journal-title":"Otol. Neurotol."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18062954","article-title":"Identifying the risk factors associated with nursing home Residents' pressure ulcers using machine learning methods","volume":"18","author":"Lee","year":"2021","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1093\/cercor\/bhs185","article-title":"Altered intra-and inter-regional synchronization of superior temporal cortex in deaf people","volume":"23","author":"Li","year":"2013","journal-title":"Cereb. Cortex"},{"key":"ref20","article-title":"Aberrant development of cross-frequency multiplex functional connectome in first-episode, Drug-Naive Major Depressive Disorder and Schizophrenia","author":"Li","year":"2021","journal-title":"Brain Connect."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3389\/fnhum.2013.00247","article-title":"VBM reveals brain volume differences between Parkinson's Disease and essential tremor patients","volume":"7","author":"Lin","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"774921","DOI":"10.3389\/fnhum.2022.774921","article-title":"Reconfiguration of brain network dynamics in autism Spectrum disorder based on hidden Markov model","volume":"16","author":"Lin","year":"2022","journal-title":"Front. Hum. Neurosci."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"103322","DOI":"10.1016\/j.ebiom.2021.103322","article-title":"Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants","volume":"69","author":"Luo","year":"2021","journal-title":"EBioMedicine"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"690","DOI":"10.21037\/atm-22-2135","article-title":"Intrinsic network changes associated with cognitive impairment in patients with hearing loss and tinnitus: a resting-state functional magnetic resonance imaging study","volume":"10","author":"Ma","year":"2022","journal-title":"Ann. Transl. Med."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.heares.2017.04.011","article-title":"Early cortical metabolic rearrangement related to clinical data in idiopathic sudden sensorineural hearing loss","volume":"350","author":"Micarelli","year":"2017","journal-title":"Hear. Res."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"102328","DOI":"10.1016\/j.nicl.2020.102328","article-title":"Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss","volume":"27","author":"Moon","year":"2020","journal-title":"Neuroimage Clin."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1002\/hbm.25714","article-title":"Unveiling whole-brain dynamics in normal aging through hidden Markov models","volume":"43","author":"Moretto","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"646","DOI":"10.2471\/BLT.19.224683","article-title":"Hearing loss: rising prevalence and impact","volume":"97","author":"Olusanya","year":"2019","journal-title":"Bull. World Health Organ."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"13376","DOI":"10.1073\/pnas.1814785115","article-title":"Multilayer network switching rate predicts brain performance","volume":"115","author":"Pedersen","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"107976","DOI":"10.1016\/j.heares.2020.107976","article-title":"Regulation of auditory plasticity during critical periods and following hearing loss","volume":"397","author":"Persic","year":"2020","journal-title":"Hear. 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Disord."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"105408","DOI":"10.1016\/j.nbd.2021.105408","article-title":"Sensorineural hearing loss may lead to dementia-related pathological changes in hippocampal neurons","volume":"156","author":"Shen","year":"2021","journal-title":"Neurobiol. Dis."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"3393","DOI":"10.1038\/s41598-019-40192-7","article-title":"Using machine learning to predict sensorineural hearing loss based on perilymph Micro RNA expression profile","volume":"9","author":"Shew","year":"2019","journal-title":"Sci. Rep."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"2878","DOI":"10.1021\/acschemneuro.1c00256","article-title":"Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study","volume":"12","author":"Shi","year":"2021","journal-title":"ACS Chem. Neurosci."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1148\/radiol.2021203383","article-title":"Cortical thickness from MRI to predict conversion from mild cognitive impairment to dementia in Parkinson Disease: a machine learning-based model","volume":"300","author":"Shin","year":"2021","journal-title":"Radiology"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1038\/s41467-019-08934-3","article-title":"Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep","volume":"10","author":"Stevner","year":"2019","journal-title":"Nat. Commun."},{"key":"ref39","doi-asserted-by":"publisher","first-page":"e52","DOI":"10.1016\/S2214-109X(21)00447-2","article-title":"Global return on investment and cost-effectiveness of WHO's HEAR interventions for hearing loss: a modelling study","volume":"10","author":"Tordrup","year":"2022","journal-title":"Lancet Glob. Health"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"102858","DOI":"10.1016\/j.amjoto.2020.102858","article-title":"Predicting hearing recovery following treatment of idiopathic sudden sensorineural hearing loss with machine learning models","volume":"42","author":"Uhm","year":"2021","journal-title":"Am. J. Otolaryngol."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1002\/jmri.27453","article-title":"Deficit of cross-frequency integration in mild cognitive impairment and Alzheimer's Disease: a multilayer network approach","volume":"53","author":"Wang","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"945274","DOI":"10.3389\/fnagi.2022.945274","article-title":"Develop a diagnostic tool for dementia using machine learning and non-imaging features","volume":"14","author":"Wang","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"4031","DOI":"10.1007\/s12035-020-01995-2","article-title":"Transition and dynamic reconfiguration of whole-brain network in major depressive disorder","volume":"57","author":"Wang","year":"2020","journal-title":"Mol. Neurobiol."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"e32581","DOI":"10.2196\/32581","article-title":"Digital approaches to automated and machine learning assessments of hearing: scoping review","volume":"24","author":"Wasmann","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"8354849","DOI":"10.1155\/2019\/8354849","article-title":"Dissociation between cerebellar and cerebral neural activities in humans with Long-term bilateral sensorineural hearing loss","author":"Xu","year":"","journal-title":"Neural Plast."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1002\/jmri.26665","article-title":"Sensorineural hearing loss and cognitive impairments: contributions of thalamus using multiparametric MRI","volume":"50","author":"Xu","year":"","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1109\/EMBC44109.2020.9175257","article-title":"Use of multilayer network modularity and spatiotemporal network switching rate to explore changes of functional brain networks in Alzheimer's disease","volume":"2020","author":"Yang","year":"2020","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"102819","DOI":"10.1016\/j.nicl.2021.102819","article-title":"Altered regional activity and connectivity of functional brain networks in congenital unilateral conductive hearing loss","volume":"32","author":"Yang","year":"2021","journal-title":"Neuroimage Clin."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1002\/lary.29549","article-title":"Neuroanatomic volume differences in tinnitus and hearing loss","volume":"131","author":"Yousef","year":"2021","journal-title":"Laryngoscope"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"2159","DOI":"10.1007\/s11682-020-00411-6","article-title":"Concurrent alterations of white matter microstructure and functional activities in medication-free major depressive disorder","volume":"15","author":"Zeng","year":"2021","journal-title":"Brain Imaging Behav."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.jneumeth.2008.04.012","article-title":"An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF","volume":"172","author":"Zou","year":"2008","journal-title":"J. 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