Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction
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 ([Formula: see text] 93%) than scaling and proportions adjusted-data [Formula: see text] 68%) or raw data ([Formula: see text] 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 GMVOL features 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 [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL.
| Errataetall: |
ErratumIn: Sci Rep. 2020 Oct 29;10(1):18937. - PMID 33122664 |
|---|---|
| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
Scientific reports - 10(2020), 1 vom: 31. Juli, Seite 12953 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sanchis-Segura, Carla [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Clinical Trial |
|---|
| Anmerkungen: |
Date Completed 09.12.2020 Date Revised 14.02.2024 published: Electronic ErratumIn: Sci Rep. 2020 Oct 29;10(1):18937. - PMID 33122664 Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41598-020-69361-9 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM31312812X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM31312812X | ||
| 003 | DE-627 | ||
| 005 | 20240214232228.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41598-020-69361-9 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1292.xml |
| 035 | |a (DE-627)NLM31312812X | ||
| 035 | |a (NLM)32737332 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Sanchis-Segura, Carla |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 09.12.2020 | ||
| 500 | |a Date Revised 14.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a ErratumIn: Sci Rep. 2020 Oct 29;10(1):18937. - PMID 33122664 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 ([Formula: see text] 93%) than scaling and proportions adjusted-data [Formula: see text] 68%) or raw data ([Formula: see text] 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 GMVOL features 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 [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL | ||
| 650 | 4 | |a Clinical Trial | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Ibañez-Gual, Maria Victoria |e verfasserin |4 aut | |
| 700 | 1 | |a Aguirre, Naiara |e verfasserin |4 aut | |
| 700 | 1 | |a Cruz-Gómez, Álvaro Javier |e verfasserin |4 aut | |
| 700 | 1 | |a Forn, Cristina |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Scientific reports |d 2011 |g 10(2020), 1 vom: 31. Juli, Seite 12953 |w (DE-627)NLM215703936 |x 2045-2322 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2020 |g number:1 |g day:31 |g month:07 |g pages:12953 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41598-020-69361-9 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2020 |e 1 |b 31 |c 07 |h 12953 | ||