comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8033
comunitat-uji-handle3:10234/8636
comunitat-uji-handle4:
INVESTIGACION
Metadata
Title
Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction
Date
2020
Publisher
Nature Research
ISSN
2045-2322
Bibliographic 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
Type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
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, ... [+]
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 [-]
Is part of
Scientific Reports, 2020, vol. 10, núm. 1, p. 1-15
Investigation project
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.
Rights
info:eu-repo/semantics/openAccess
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