Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data.
Overview
- Department of Neurology, University Hospital Cologne, University of Cologne,Cologne, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich,Jülich, Germany
- Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Jülich,Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University,Düsseldorf, Germany
- Department of Neurology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
- Department of Biology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf,Düsseldorf, Germany
Abstract
Brain size measures are well-studied and often treated as a confound in volumetric neuroimaging analyses. Yet their relationship with body anthropometric measures and demographics remains underexplored. In this study, we examined those relationships alongside age- and sex-related differences in global brain volumes. Using brain magnetic resonance imaging (MRI) of healthy participants in the UK Biobank, we derived global measures of brain morphometry, including total intracranial volume (TIV), total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid (CSF). We extracted these measures using the Computational Anatomy Toolbox (CAT) and FreeSurfer. Our analyses were structured in three approaches: across-sex analysis, sex-specific analysis, and impact of age analysis. Employing machine learning (ML), we found that TIV was strongly predicted by sex (across-sex 0.68), reflecting sex difference. On the other hand, TBV, GMV, WMV, and CSF were more sensitive to age, with higher prediction accuracy when age was included as a feature, highlighting age-related changes in the brain structure, such as fluid expansion. Sex-specific models showed reduced TIV prediction ( 0.25) but improved TBV accuracy ( 0.44), underscoring sex-specific body-brain relationships. Anthropometric measures, particularly seated height and weight, improved prediction of TIV and TBV, while waist and hip circumference showed negative associations, though their effects generally remained secondary to age and sex. These findings advance our understanding of brain-body scaling relationships and underscore the necessity of accounting for age and sex in neuroimaging studies of brain morphology.
Supplementary Information: The online version contains supplementary material available at 10.1007/
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Code
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KNazarzadeh/brainsize
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- 30 September 2026: the link is dead
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Data
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Data availability
All data used in this study are publicly available through the UK Biobank, accessible via their standard data access procedure at (http://
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Versions
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 19 MeSH terms, 1 funder, 39 references.
Cite
This paper
Nazarzadeh, K., Eickhoff, S. B., Antonopoulos, G., Hensel, L., Tscherpel, C., Komeyer, V., Raimondo, F., Grefkes, C., & Patil, K. R. (2026). Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data. Brain structure & function, 231(3), 37. https://
BibTeX
@article{nazarzadeh2026p
author = {Nazarzadeh, Kimia and Eickhoff, Simon B. and Antonopoulos, Georgios and Hensel, Lukas and Tscherpel, Caroline and Komeyer, Vera and Raimondo, Federico and Grefkes, Christian and Patil, Kaustubh R.},
title = {{Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data}},
journal = {Brain structure \& function},
year = {2026},
month = mar,
volume = {231},
number = {3},
pages = {37},
publisher = {Springer Science+Business Media},
issn = {1863-2653},
doi = {10.1007/
url = {https://
pmid = {41811501},
pmcid = {PMC12979296}
}
RIS
TY - JOUR
AU - Nazarzadeh, Kimia
AU - Eickhoff, Simon B.
AU - Antonopoulos, Georgios
AU - Hensel, Lukas
AU - Tscherpel, Caroline
AU - Komeyer, Vera
AU - Raimondo, Federico
AU - Grefkes, Christian
AU - Patil, Kaustubh R.
TI - Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data
T2 - Brain structure & function
J2 - Brain Struct Funct
PY - 2026
DA - 2026/
VL - 231
IS - 3
SP - 37
SN - 1863-2653
PB - Springer Science+Business Media
DO - 10.1007/
UR - https://
LA - en
ER -
CSL-JSON
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