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Analyzing brain data by sex: Are we asking the right question?

Nitay Alon, View ORCID ProfileIsaac Meilijson, View ORCID ProfileDaphna Joel
doi: https://doi.org/10.1101/2020.11.09.373258
Nitay Alon
1School of Mathematical Sciences, Tel Aviv University. Tel-Aviv, 6997801, Israel
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Isaac Meilijson
1School of Mathematical Sciences, Tel Aviv University. Tel-Aviv, 6997801, Israel
PhD
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  • ORCID record for Isaac Meilijson
Daphna Joel
2School of Psychological Sciences, Tel-Aviv University. Tel-Aviv, 6997801, Israel
3Sagol School of Neuroscience, Tel-Aviv University. Tel-Aviv, 6997801, Israel
PhD
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  • For correspondence: djoel@tauex.tau.ac.il
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Abstract

The decades old hypothesis that sex effects on the brain result in ‘female’ and ‘male’ phenotypes governs conventional analyses by sex. In these (e.g., Student’s t-test), the null hypothesis is that males and females belong to a single population (or phenotype), and the alternative hypothesis is that they belong to two different populations/phenotypes. Yet, evidence that sex effects may be opposite under different conditions raises a third hypothesis – that both females and males may manifest each of the two phenotypes of a brain measure. Here we applied a mixture analysis, which can test this latter hypothesis, and Student’s t-test to 289 MRI-derived measures of grey and white matter from 23,935 human brains. Whereas Student’s t-test yielded significant sex/gender differences in 225 measures, the mixture analysis revealed that 282 brain measures were better described by the hypothesis that women and men sample from the same two phenotypes, and that, for the most part, they do so with quite similar probabilities. A further analysis of 41 brain measures for which there were a ‘female-favored’ and a ‘male-favored’ phenotype, revealed that brains do not consistently manifested the male-favored (or the female-favored) phenotype. Last, considering the relations between all brain measures, the brain architectures of women and men were remarkably similar. These results do not support the existence of ‘female’ and ‘male’ brain phenotypes but are consistent with other lines of evidence suggesting that sex category explains a very small part of the variability in human brain structure.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Conflict of interest statement: The authors declare no competing financial interests.

  • The main change from the previous version is in framing the research aim in terms of the types of answers we may and may not get depending on how we frame our question regarding the relations between sex category and the brain. We compare the results of a conventional analysis by sex - t-test, in which the null hypothesis is that males and females belong to a single population (or phenotype), and the alternative hypothesis is that they belong to two different populations/phenotypes, with the results of a mixture analysis, in which the null hypothesis is that males and females belong to two different phenotypes and the alternative hypothesis is that that both females and males may manifest each of the two phenotypes of a brain measure. Whereas t-tests yielded significant sex/gender differences in 225 measures, the mixture analysis revealed that 282 brain measures were better described by the hypothesis that women and men sample from the same two phenotypes, and that, for the most part, they do so with quite similar probabilities.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 10, 2021.
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Analyzing brain data by sex: Are we asking the right question?
Nitay Alon, Isaac Meilijson, Daphna Joel
bioRxiv 2020.11.09.373258; doi: https://doi.org/10.1101/2020.11.09.373258
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Analyzing brain data by sex: Are we asking the right question?
Nitay Alon, Isaac Meilijson, Daphna Joel
bioRxiv 2020.11.09.373258; doi: https://doi.org/10.1101/2020.11.09.373258

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