Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction.

ABSTRACT: Sex differences in 116 local gray matter volumes (GM_VOL) 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??VOL features in predicting individuals' sex with 12 different machine learning classifiers. Sex could be reliably predicted (>?80%) when using raw local GM_VOL, 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 [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GM_VOL.

SUBMITTER: Sanchis-Segura C 

PROVIDER: S-EPMC7395772 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature