{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T13:32:16Z","timestamp":1790429536934,"version":"4.1.0"},"reference-count":75,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2019,5,11]],"date-time":"2019-05-11T00:00:00Z","timestamp":1557532800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201013\u2010APPR\u20100010"],"award-info":[{"award-number":["ANR\u201013\u2010APPR\u20100010"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2019,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Neurofibromatosis Type 1 leads to brain anomalies involving both gray and white matter. The extent and granularity of these anomalies, together with their possible impact on brain activity, is still unknown. In this multicentric cross\u2010sectional study we submitted a sample of 42 typically developing and 38 neurofibromatosis\u20101 children to a multimodal MRI assessment including T1, diffusion weighted and resting state functional sequences. We used a pipeline involving several features selection steps coupled with multivariate statistical analysis (supporting vector machine) to discriminate between the two groups while having interpretable models. We used MRI indexes measuring macro (gray matter volume) and microstructural (fractional anisotropy, mean diffusivity) characteristics of the brain, as well as indexes of brain activity (fractional amplitude of low frequency fluctuations) and connectivity (local and global correlation) at rest. We found that structural indexes could discriminate between the two groups, with the mean diffusivity leading to performance as high as the combination of all structural indexes combined (accuracy\u2009=\u20090.86), while functional indexes had worse performances. The MRI signature of NF1 brain pathology is a combination of gray and white matter abnormalities, as measured with gray matter volume, fractional anisotropy, and mean diffusivity.<\/jats:p>","DOI":"10.1002\/hbm.24612","type":"journal-article","created":{"date-parts":[[2019,5,11]],"date-time":"2019-05-11T14:06:40Z","timestamp":1557583600000},"page":"3508-3521","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Discriminating between neurofibromatosis\u20101 and typically developing children by means of multimodal MRI and multivariate analyses"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9806-8326","authenticated-orcid":false,"given":"Federico","family":"Nemmi","sequence":"first","affiliation":[{"name":"ToNIC, Toulouse NeuroImaging Center Universit\u00e9 de Toulouse, Inserm, UPS  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabien","family":"Cignetti","sequence":"additional","affiliation":[{"name":"CNRS, LNC Aix Marseille Universit\u00e9  Marseille France"},{"name":"CNRS, F\u00e9d\u00e9ration 3C Aix Marseille Universit\u00e9  Marseille France"},{"name":"CNRS, TIMC\u2010IMAG Universit\u00e9 Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Assaiante","sequence":"additional","affiliation":[{"name":"CNRS, LNC Aix Marseille Universit\u00e9  Marseille France"},{"name":"CNRS, F\u00e9d\u00e9ration 3C Aix Marseille Universit\u00e9  Marseille France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Maziero","sequence":"additional","affiliation":[{"name":"ToNIC, Toulouse NeuroImaging Center Universit\u00e9 de Toulouse, Inserm, UPS  Toulouse France"},{"name":"URI Octogone\u2010Lordat (EA 4156) Universit\u00e9 de Toulouse  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrique","family":"Audic","sequence":"additional","affiliation":[{"name":"Service de Neurologie P\u00e9diatrique CHU Timone\u2010Enfants  Marseille France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrice","family":"P\u00e9ran","sequence":"additional","affiliation":[{"name":"ToNIC, Toulouse NeuroImaging Center Universit\u00e9 de Toulouse, Inserm, UPS  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yves","family":"Chaix","sequence":"additional","affiliation":[{"name":"ToNIC, Toulouse NeuroImaging Center Universit\u00e9 de Toulouse, Inserm, UPS  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,5,11]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Anderson J. Jenkinson M. &Smith N.(2010).Non\u2010linear registration aka spatial normalisation. Retrieved fromhttp:\/\/www.fmrib.ox.ac.uk\/datasets\/techrep\/"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2015.08.025"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.07.007"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2016.06.013"},{"key":"e_1_2_7_6_1","doi-asserted-by":"crossref","unstructured":"Baddeley A. Rubak E. &Turner R.(2015).Spatial point patterns: Methodology and applications with R. Retrieved fromhttp:\/\/spatstat.org\/","DOI":"10.1201\/b19708"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10609"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.04.042"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.04.005"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.02.014"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2010.02.004"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/09297049.2017.1313970"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11065-018-9380-x"},{"key":"e_1_2_7_14_1","volume-title":"Statistical models in S","author":"Cleveland W.","year":"1992"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2015.01.010"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1212\/01.WNL.0000032370.68306.8A"},{"key":"e_1_2_7_17_1","unstructured":"Dahnke R. Ziegler G. &Gaser C.(2012). Local adaptive segmentation.HBM. Poster #521."},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.54.8.1646"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1162\/jocn_a_00534"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22161"},{"issue":"1","key":"e_1_2_7_21_1","first-page":"190","article-title":"MR imaging of the corpus callosum in pediatric patients with neurofibromatosis type 1","volume":"22","author":"Dubovsky E. C.","year":"2001","journal-title":"AJNR. American Journal of Neuroradiology"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.15274\/NRJ-2014-10055"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejpn.2011.10.002"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.9590"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012372560-8\/50002-4"},{"key":"e_1_2_7_26_1","unstructured":"Gaser C. &Dahnke R.(2016). CAT\u2014a computational anatomy toolbox for the analysis of structural MRI data.HBM. Poster 4057."},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00180-008-0119-7"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s11689-015-9128-3"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1212\/01.wnl.0000179303.72345.ce"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.06.032"},{"key":"e_1_2_7_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(01)00036-6"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0387-7604(03)00102-5"},{"key":"e_1_2_7_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/1098-2779(2000)6:2<117::AID-MRDD5>3.0.CO;2-X"},{"key":"e_1_2_7_35_1","doi-asserted-by":"publisher","DOI":"10.1177\/088307380001500206"},{"key":"e_1_2_7_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"e_1_2_7_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"e_1_2_7_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-016-9641-3"},{"key":"e_1_2_7_39_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008280620621"},{"key":"e_1_2_7_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2005.01.048"},{"key":"e_1_2_7_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.05.026"},{"key":"e_1_2_7_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2012.11.004"},{"key":"e_1_2_7_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2015.02.018"},{"key":"e_1_2_7_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.05.065"},{"key":"e_1_2_7_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.836370"},{"key":"e_1_2_7_46_1","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.1988.00520290115023"},{"key":"e_1_2_7_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/1096-8628(200022)97:2<119::AID-AJMG3>3.0.CO;2-3"},{"key":"e_1_2_7_48_1","unstructured":"Novoselova N. Wang J. Pessler F. &Klawonn F.(2017).Biocomb. Retrieved fromhttps:\/\/cran.r-project.org\/web\/packages\/Biocomb\/index.html"},{"key":"e_1_2_7_49_1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2009.179630"},{"key":"e_1_2_7_50_1","doi-asserted-by":"crossref","unstructured":"Platt J.(1998 January 1).Fast training of support vector machines using sequential minimal optimization. Retrieved fromhttps:\/\/www.microsoft.com\/en-us\/research\/publication\/fast-training-of-support-vector-machines-using-sequential-minimal-optimization\/","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_2_7_51_1","doi-asserted-by":"publisher","DOI":"10.1177\/0883073809350723"},{"key":"e_1_2_7_52_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22334"},{"key":"e_1_2_7_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2017.04.022"},{"key":"e_1_2_7_54_1","doi-asserted-by":"publisher","DOI":"10.1186\/1866-1955-6-4"},{"key":"e_1_2_7_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2014.10.019"},{"key":"e_1_2_7_56_1","unstructured":"Robnik\u2010Sikonja M. &Savicky P.(2017).CORElearn. Retrieved fromhttp:\/\/lkm.fri.uni-lj.si\/rmarko\/software\/"},{"key":"e_1_2_7_57_1","volume-title":"Learning with kernels: Support vector machines, regularization, optimization, and beyond","author":"Sch\u00f6lkopf B.","year":"2002"},{"key":"e_1_2_7_58_1","doi-asserted-by":"publisher","DOI":"10.1586\/14737175.2014.953931"},{"key":"e_1_2_7_59_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0203520"},{"key":"e_1_2_7_60_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.159.1.1609692"},{"key":"e_1_2_7_61_1","doi-asserted-by":"publisher","DOI":"10.1186\/s11689-018-9230-4"},{"key":"e_1_2_7_62_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0148600"},{"key":"e_1_2_7_63_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22937"},{"key":"e_1_2_7_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2009.10.004"},{"key":"e_1_2_7_65_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0038785"},{"key":"e_1_2_7_66_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000003044"},{"key":"e_1_2_7_67_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0038785"},{"key":"e_1_2_7_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0278-2626(03)00056-3"},{"key":"e_1_2_7_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2004.11.001"},{"key":"e_1_2_7_70_1","doi-asserted-by":"publisher","DOI":"10.1089\/brain.2012.0073"},{"key":"e_1_2_7_71_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A2005"},{"key":"e_1_2_7_72_1","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2013.496"},{"key":"e_1_2_7_73_1","volume-title":"Data mining: Practical machine learning tools and techniques","author":"Witten I. H.","year":"2011"},{"issue":"32","key":"e_1_2_7_74_1","article-title":"The neuroanatomical basis for posterior superior parietal lobule control lateralization of visuospatial attention","volume":"10","author":"Wu Y.","year":"2016","journal-title":"Frontiers in Neuroanatomy"},{"issue":"4","key":"e_1_2_7_75_1","first-page":"773","article-title":"Contribution of diffusion tensor MR imaging in detecting cerebral microstructural changes in adults with neurofibromatosis type 1","volume":"28","author":"Zamboni S. L.","year":"2007","journal-title":"AJNR. American Journal of Neuroradiology"},{"key":"e_1_2_7_76_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2008.04.012"}],"container-title":["Human Brain Mapping"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hbm.24612","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/hbm.24612","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hbm.24612","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T20:38:38Z","timestamp":1694291918000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hbm.24612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,11]]},"references-count":75,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8,15]]}},"alternative-id":["10.1002\/hbm.24612"],"URL":"https:\/\/doi.org\/10.1002\/hbm.24612","archive":["Portico"],"relation":{},"ISSN":["1065-9471","1097-0193"],"issn-type":[{"value":"1065-9471","type":"print"},{"value":"1097-0193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,11]]},"assertion":[{"value":"2018-11-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-04-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}