Predicting brain volumes from anthropometric and demographic features: insights from UK biobank neuroimaging data.
Authors
Affiliations (10)
Affiliations (10)
- Department of Neurology, University Hospital Cologne, University of Cologne, Cologne, Germany. [email protected].
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany. [email protected].
- Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Centre Jülich, Jülich, Germany. [email protected].
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
- Department of Neurology, University Hospital Cologne, University of Cologne, Cologne, 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.
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany. [email protected].
- Institute of Systems Neuroscience, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany. [email protected].
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 [Formula: see text] 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 ([Formula: see text] 0.25) but improved TBV accuracy ([Formula: see text] 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. The online version contains supplementary material available at 10.1007/s00429-025-03070-9.