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Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction
dc.contributor.author | Sanchis-Segura, Carla | |
dc.contributor.author | Ibáñez Gual, Maria Victoria | |
dc.contributor.author | Aguirre, Naiara | |
dc.contributor.author | Cruz Gómez, Álvaro Javier | |
dc.contributor.author | Forn, Cristina | |
dc.date.accessioned | 2020-10-19T15:58:42Z | |
dc.date.available | 2020-10-19T15:58:42Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | SANCHIS-SEGURA, Carla, et al. Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Scientific Reports, 2020, vol. 10, núm. 1, p. 1-15 | ca_CA |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10234/190013 | |
dc.description.abstract | Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power‑corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV‑variation and result in sex‑differences that are “small” (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted‑data showed higher replicability ( ≈ 93%) than scaling and proportions adjusted‑data (≈ 68%) or raw data ( ≈ 45%). The replicated effects were meta‑analyzed together and confirmed that, when TIV‑variation is adequately controlled, volumetric sex differences become “small” (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV‑corrected/ TIV‑uncorrected GMVOLfeatures in predicting individuals’ sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted‑data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals’ methods, prediction accuracy dropped to ≈ 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL | ca_CA |
dc.format.extent | 15 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Nature Research | ca_CA |
dc.relation.isPartOf | Scientific Reports, 2020, vol. 10, núm. 1, p. 1-15 | ca_CA |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creat iveco mmons .org/licen ses/by/4.0/. © The Author(s) 2020 | ca_CA |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | * |
dc.title | Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1038/s41598-020-69361-9 | |
dc.relation.projectID | Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investiga-tors: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The authors also thank Dr. César Avila from Universitat Jaume I for kindly providing a second set of scan images used in this study (UJI sample). This research was supported by a grant (UJI B2017-05) awarded to CS-S. This funding source did not play any role in designing the study or in the collection, analysis, and interpretation of the data. | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.nature.com/articles/s41598-020-69361-9 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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© The Author(s) 2020