{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T07:12:59Z","timestamp":1784272379995,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Ministry of Health & Welfare, Republic of Korea"},{"name":"SMC Research and Development Grant"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are two representative chronic inflammatory demyelinating disorders of the central nervous system. We aimed to determine and compare the alterations of white matter (WM) connectivity between MS, NMOSD, and healthy controls (HC). This study included 68 patients with relapsing\u2013remitting MS, 50 with NMOSD, and 26 HC. A network-based statistics method was used to assess disrupted patterns in WM networks. Topological characteristics of the three groups were compared and their associations with clinical parameters were examined. WM network analysis indicated that the MS and NMOSD groups had lower total strength, clustering coefficient, global efficiency, and local efficiency and had longer characteristic path length than HC, but there were no differences between the MS and NMOSD groups. At the nodal level, the MS group had more brain regions with altered network topologies than did the NMOSD group when compared with the HC group. Network alterations were correlated with Expanded Disability Status Scale score and disease duration in both MS and NMOSD groups. Two distinct subnetworks that characterized the disease groups were also identified. When compared with NMOSD, the most discriminative connectivity changes in MS were located between the thalamus, hippocampus, parahippocampal gyrus, amygdala, fusiform gyrus, and inferior and superior temporal gyri. In conclusion, MS patients had greater network dysfunction compared to NMOSD and altered short connections within the thalamus and inferomedial temporal regions were relatively spared in NMOSD compared with MS.<\/jats:p>","DOI":"10.1038\/s41598-022-09065-4","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T11:03:05Z","timestamp":1648206185000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Disrupted structural network of inferomedial temporal regions in relapsing\u2013remitting multiple sclerosis compared with neuromyelitis optica spectrum disorder"],"prefix":"10.1038","volume":"12","author":[{"given":"Eun Bin","family":"Cho","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daegyeom","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ByeongChang","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Hwa","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeon Hak","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Tae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung Joon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheol E.","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju-Hong","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"9065_CR1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1136\/jnnp-2016-313300","volume":"88","author":"I Kawachi","year":"2017","unstructured":"Kawachi, I. & Lassmann, H. Neurodegeneration in multiple sclerosis and neuromyelitis optica. J. Neurol. Neurosurg. Psychiatry 88, 137\u2013145 (2017).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"9065_CR2","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1177\/1352458516636247","volume":"22","author":"IH Jeong","year":"2016","unstructured":"Jeong, I. H. et al. Comparison of myelin water fraction values in periventricular white matter lesions between multiple sclerosis and neuromyelitis optica spectrum disorder. Mult. Scler. J. 22, 1616\u20131620 (2016).","journal-title":"Mult. Scler. J."},{"key":"9065_CR3","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1177\/1352458518761186","volume":"25","author":"J-W Hyun","year":"2019","unstructured":"Hyun, J.-W. et al. Evaluation of brain lesion distribution criteria at disease onset in differentiating MS from NMOSD and MOG-IgG-associated encephalomyelitis. Mult. Scler. J. 25, 585\u2013590 (2019).","journal-title":"Mult. Scler. J."},{"key":"9065_CR4","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1212\/WNL.0000000000001367","volume":"84","author":"HJ Kim","year":"2015","unstructured":"Kim, H. J. et al. MRI characteristics of neuromyelitis optica spectrum disorder: An international update. Neurology 84, 1165\u20131173 (2015).","journal-title":"Neurology"},{"key":"9065_CR5","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1177\/1352458511431731","volume":"18","author":"Y Liu","year":"2012","unstructured":"Liu, Y. et al. A tract-based diffusion study of cerebral white matter in neuromyelitis optica reveals widespread pathological alterations. Mult. Scler. J. 18, 1013\u20131021 (2012).","journal-title":"Mult. Scler. J."},{"key":"9065_CR6","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1111\/ene.13224","volume":"24","author":"JW Hyun","year":"2017","unstructured":"Hyun, J. W. et al. Deep gray matter atrophy in neuromyelitis optica spectrum disorder and multiple sclerosis. Eur. J. Neurol. 24, 437\u2013445 (2017).","journal-title":"Eur. J. Neurol."},{"key":"9065_CR7","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.biopsych.2014.02.018","volume":"76","author":"MS Korgaonkar","year":"2014","unstructured":"Korgaonkar, M. S., Fornito, A., Williams, L. M. & Grieve, S. M. Abnormal structural networks characterize major depressive disorder: A connectome analysis. Biol. Psychiatry 76, 567\u2013574 (2014).","journal-title":"Biol. Psychiatry"},{"key":"9065_CR8","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1089\/brain.2019.0717","volume":"10","author":"T Welton","year":"2020","unstructured":"Welton, T., Constantinescu, C. S., Auer, D. P. & Dineen, R. A. Graph theoretic analysis of brain Connectomics in multiple sclerosis: Reliability and relationship with cognition. Brain Connect. 10, 95\u2013104 (2020).","journal-title":"Brain Connect."},{"key":"9065_CR9","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1148\/radiol.14132593","volume":"273","author":"JW Prescott","year":"2014","unstructured":"Prescott, J. W. et al. The Alzheimer structural connectome: Changes in cortical network topology with increased amyloid plaque burden. Radiology 273, 175\u2013184 (2014).","journal-title":"Radiology"},{"key":"9065_CR10","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.3389\/fneur.2018.01104","volume":"9","author":"EB Cho","year":"2018","unstructured":"Cho, E. B. et al. White matter network disruption and cognitive dysfunction in neuromyelitis optica spectrum disorder. Front. Neurol. 9, 1104 (2018).","journal-title":"Front. Neurol."},{"key":"9065_CR11","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1093\/cercor\/bhr039","volume":"21","author":"N Shu","year":"2011","unstructured":"Shu, N. et al. Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cereb. Cortex 21, 2565\u20132577 (2011).","journal-title":"Cereb. Cortex"},{"key":"9065_CR12","doi-asserted-by":"publisher","first-page":"e48846","DOI":"10.1371\/journal.pone.0048846","volume":"7","author":"Y Liu","year":"2012","unstructured":"Liu, Y. et al. Altered topological organization of white matter structural networks in patients with neuromyelitis optica. PLoS ONE 7, e48846 (2012).","journal-title":"PLoS ONE"},{"key":"9065_CR13","doi-asserted-by":"publisher","first-page":"e159","DOI":"10.1371\/journal.pbio.0060159","volume":"6","author":"P Hagmann","year":"2008","unstructured":"Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).","journal-title":"PLoS Biol."},{"key":"9065_CR14","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/nrn3901","volume":"16","author":"A Fornito","year":"2015","unstructured":"Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159\u2013172 (2015).","journal-title":"Nat. Rev. Neurosci."},{"key":"9065_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1136\/jnnp-2018-318440","volume":"90","author":"T Charalambous","year":"2019","unstructured":"Charalambous, T. et al. Structural network disruption markers explain disability in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 90, 219\u2013226 (2019).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"9065_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep29383","volume":"6","author":"N Shu","year":"2016","unstructured":"Shu, N. et al. Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Sci. Rep. 6, 1\u201311 (2016).","journal-title":"Sci. Rep."},{"key":"9065_CR17","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.nicl.2016.11.026","volume":"13","author":"S Llufriu","year":"2017","unstructured":"Llufriu, S. et al. Structural networks involved in attention and executive functions in multiple sclerosis. NeuroImage Clin. 13, 288\u2013296 (2017).","journal-title":"NeuroImage Clin."},{"key":"9065_CR18","doi-asserted-by":"publisher","first-page":"102714","DOI":"10.1016\/j.msard.2020.102714","volume":"48","author":"Q Zheng","year":"2020","unstructured":"Zheng, Q. et al. Altered structural networks in neuromyelitis optica spectrum disorder related with cognition impairment and clinical features. Mult. Scler. Relat. Disord. 48, 102714 (2020).","journal-title":"Mult. Scler. Relat. Disord."},{"key":"9065_CR19","doi-asserted-by":"publisher","first-page":"100607","DOI":"10.1016\/j.dcn.2018.12.005","volume":"36","author":"S Oldham","year":"2019","unstructured":"Oldham, S. & Fornito, A. The development of brain network hubs. Dev. Cogn. Neurosci. 36, 100607 (2019).","journal-title":"Dev. Cogn. Neurosci."},{"key":"9065_CR20","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1093\/braincomms\/fcaa196","volume":"2","author":"ET Rolls","year":"2020","unstructured":"Rolls, E. T., Cheng, W. & Feng, J. The orbitofrontal cortex: Reward, emotion and depression. Brain Commun. 2, 196 (2020).","journal-title":"Brain Commun."},{"key":"9065_CR21","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1093\/cercor\/bhn102","volume":"19","author":"G Gong","year":"2009","unstructured":"Gong, G. et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524\u2013536 (2009).","journal-title":"Cereb. Cortex"},{"key":"9065_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-70410-6","volume":"10","author":"AH Palejwala","year":"2020","unstructured":"Palejwala, A. H. et al. Anatomy and white matter connections of the fusiform gyrus. Sci. Rep. 10, 1\u201312 (2020).","journal-title":"Sci. Rep."},{"key":"9065_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1177\/1352458519900972","volume":"27","author":"J Zhang","year":"2020","unstructured":"Zhang, J. et al. Gray matter atrophy cannot be fully explained by white matter damage in patients with MS. Mult. Scler. J. 27, 39 (2020).","journal-title":"Mult. Scler. J."},{"key":"9065_CR24","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3389\/fneur.2020.00507","volume":"11","author":"S Golde","year":"2020","unstructured":"Golde, S. et al. Distinct functional connectivity signatures of impaired social cognition in multiple sclerosis. Front. Neurol. 11, 507 (2020).","journal-title":"Front. Neurol."},{"key":"9065_CR25","doi-asserted-by":"publisher","first-page":"431","DOI":"10.3389\/fnhum.2015.00431","volume":"9","author":"VD Bohbot","year":"2015","unstructured":"Bohbot, V. D. et al. Role of the parahippocampal cortex in memory for the configuration but not the identity of objects: Converging evidence from patients with selective thermal lesions and fMRI. Front. Hum. Neurosci. 9, 431 (2015).","journal-title":"Front. Hum. Neurosci."},{"key":"9065_CR26","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/87565640701190841","volume":"31","author":"ED Bigler","year":"2007","unstructured":"Bigler, E. D. et al. Superior temporal gyrus, language function, and autism. Dev. Neuropsychol. 31, 217\u2013238 (2007).","journal-title":"Dev. Neuropsychol."},{"key":"9065_CR27","doi-asserted-by":"publisher","DOI":"10.1177\/13524585211032800","author":"F Zheng","year":"2021","unstructured":"Zheng, F. et al. Structural and functional hippocampal alterations in multiple sclerosis and neuromyelitis optica spectrum disorder. Mult. Scler. J. https:\/\/doi.org\/10.1177\/13524585211032800 (2021).","journal-title":"Mult. Scler. J."},{"key":"9065_CR28","doi-asserted-by":"publisher","first-page":"e110","DOI":"10.1016\/j.ejrad.2011.01.065","volume":"81","author":"Y Duan","year":"2012","unstructured":"Duan, Y. et al. Comparison of grey matter atrophy between patients with neuromyelitis optica and multiple sclerosis: A voxel-based morphometry study. Eur. J. Radiol. 81, e110\u2013e114 (2012).","journal-title":"Eur. J. Radiol."},{"key":"9065_CR29","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1177\/1352458516636246","volume":"22","author":"S-H Kim","year":"2016","unstructured":"Kim, S.-H. et al. Cognitive impairment differs between neuromyelitis optica spectrum disorder and multiple sclerosis. Mult. Scler. J. 22, 1850\u20131858 (2016).","journal-title":"Mult. Scler. J."},{"key":"9065_CR30","doi-asserted-by":"publisher","first-page":"e0184012","DOI":"10.1371\/journal.pone.0184012","volume":"12","author":"H Masuda","year":"2017","unstructured":"Masuda, H. et al. Comparison of cognitive and brain grey matter volume profiles between multiple sclerosis and neuromyelitis optica spectrum disorder. PLoS ONE 12, e0184012 (2017).","journal-title":"PLoS ONE"},{"key":"9065_CR31","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1002\/hbm.22738","volume":"36","author":"MD Steenwijk","year":"2015","unstructured":"Steenwijk, M. D. et al. Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long-standing multiple sclerosis. Hum. Brain Mapp. 36, 1796\u20131807 (2015).","journal-title":"Hum. Brain Mapp."},{"key":"9065_CR32","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/S1474-4422(17)30470-2","volume":"17","author":"AJ Thompson","year":"2018","unstructured":"Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162\u2013173 (2018).","journal-title":"Lancet Neurol."},{"key":"9065_CR33","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1212\/WNL.0000000000001729","volume":"85","author":"DM Wingerchuk","year":"2015","unstructured":"Wingerchuk, D. M. et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 85, 177\u2013189 (2015).","journal-title":"Neurology"},{"key":"9065_CR34","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3343\/alm.2012.32.5.331","volume":"32","author":"E-S Kang","year":"2012","unstructured":"Kang, E.-S., Min, J.-H., Lee, K. H. & Kim, B. J. Clinical usefulness of cell-based indirect immunofluorescence assay for the detection of aquaporin-4 antibodies in neuromyelitis optica spectrum disorder. Ann. Lab. Med. 32, 331\u2013338 (2012).","journal-title":"Ann. Lab. Med."},{"key":"9065_CR35","doi-asserted-by":"crossref","first-page":"e1250","DOI":"10.1212\/WNL.0000000000007096","volume":"92","author":"PJ Waters","year":"2019","unstructured":"Waters, P. J. et al. A multicenter comparison of MOG-IgG cell-based assays. Neurology 92, e1250\u2013e1255 (2019).","journal-title":"Neurology"},{"key":"9065_CR36","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273\u2013289. https:\/\/doi.org\/10.1006\/nimg.2001.0978 (2002).","journal-title":"Neuroimage"},{"key":"9065_CR37","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1002\/hbm.460020402","volume":"2","author":"KJ Friston","year":"1994","unstructured":"Friston, K. J. et al. Statistical parametric maps in functional imaging: A general linear approach. Hum. Brain Mapp. 2, 189\u2013210. https:\/\/doi.org\/10.1002\/hbm.460020402 (1994).","journal-title":"Hum. Brain Mapp."},{"key":"9065_CR38","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.neuroimage.2015.10.019","volume":"125","author":"JL Andersson","year":"2016","unstructured":"Andersson, J. L. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063\u20131078 (2016).","journal-title":"Neuroimage"},{"key":"9065_CR39","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1002\/(SICI)1097-0185(19990615)257:3<102::AID-AR7>3.0.CO;2-6","volume":"257","author":"S Mori","year":"1999","unstructured":"Mori, S. & Barker, P. B. Diffusion magnetic resonance imaging: Its principle and applications. Anat. Rec. 257, 102\u2013109 (1999).","journal-title":"Anat. Rec."},{"key":"9065_CR40","first-page":"3720","volume":"15","author":"TB Wang","year":"2007","unstructured":"Wang, T. B., Sorensen, A. G. & Wedeen, V. J. Diffusion toolkit: A software package for diffusion imaging data processing and tractography. Proc. Intl. Soc. Mag. Reson. Med. 15, 3720 (2007).","journal-title":"Proc. Intl. Soc. Mag. Reson. Med."},{"key":"9065_CR41","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059\u20131069. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.10.003 (2010).","journal-title":"Neuroimage"},{"key":"9065_CR42","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts, D. J. & Strogatz, S. H. Collective dynamics of \u2018small-world\u2019 networks. Nature 393, 440\u2013442. https:\/\/doi.org\/10.1038\/30918 (1998).","journal-title":"Nature"},{"key":"9065_CR43","doi-asserted-by":"publisher","first-page":"065103","DOI":"10.1103\/PhysRevE.71.065103","volume":"71","author":"J-P Onnela","year":"2005","unstructured":"Onnela, J.-P., Saram\u00e4ki, J., Kert\u00e9sz, J. & Kaski, K. Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71, 065103. https:\/\/doi.org\/10.1103\/PhysRevE.71.065103 (2005).","journal-title":"Phys. Rev. E"},{"key":"9065_CR44","doi-asserted-by":"publisher","first-page":"198701","DOI":"10.1103\/PhysRevLett.87.198701","volume":"87","author":"V Latora","year":"2001","unstructured":"Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701. https:\/\/doi.org\/10.1103\/PhysRevLett.87.198701 (2001).","journal-title":"Phys. Rev. Lett."},{"key":"9065_CR45","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.neuroimage.2012.12.066","volume":"70","author":"MA de Reus","year":"2013","unstructured":"de Reus, M. A. & van den Heuvel, M. P. Estimating false positives and negatives in brain networks. Neuroimage 70, 402\u2013409. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.12.066 (2013).","journal-title":"Neuroimage"},{"key":"9065_CR46","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1016\/j.neuroimage.2010.06.041","volume":"53","author":"A Zalesky","year":"2010","unstructured":"Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: Identifying differences in brain networks. Neuroimage 53, 1197\u20131207. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.06.041 (2010).","journal-title":"Neuroimage"},{"key":"9065_CR47","volume-title":"Bayesian Inference for Structured Additive Regression Models for Large-Scale Problems with Applications to Medical Imaging","author":"P Schmidt","year":"2017","unstructured":"Schmidt, P. Bayesian Inference for Structured Additive Regression Models for Large-Scale Problems with Applications to Medical Imaging (Imu, 2017)."},{"key":"9065_CR48","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1006\/nimg.2001.1037","volume":"15","author":"CR Genovese","year":"2002","unstructured":"Genovese, C. R., Lazar, N. A. & Nichols, T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870\u2013878 (2002).","journal-title":"Neuroimage"},{"key":"9065_CR49","unstructured":"Nichols, T., Ridgway, G., Webster, M. & Smith, S. GLM permutation-nonparametric inference for arbitrary general linear models (2008)."},{"key":"9065_CR50","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289\u2013300 (1995).","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"9065_CR51","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1093\/biostatistics\/kxw030","volume":"18","author":"Y Benjamini","year":"2017","unstructured":"Benjamini, Y. & Cohen, R. Weighted false discovery rate controlling procedures for clinical trials. Biostatistics 18, 91\u2013104. https:\/\/doi.org\/10.1093\/biostatistics\/kxw030 (2017).","journal-title":"Biostatistics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09065-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09065-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09065-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:25:59Z","timestamp":1726878359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-022-09065-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,25]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["9065"],"URL":"https:\/\/doi.org\/10.1038\/s41598-022-09065-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,25]]},"assertion":[{"value":"31 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"JH Min has lectured, consulted and received honoraria from Bayer Schering Pharma, Merk Serono, Biogen Idec, Sanofi Genzyme, UCB, Samsung Bioepis and Roche; received a grant from the National Research Foundation of Korea.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"5152"}}