| Title: | Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head |
| Journal: | Imaging Neuroscience |
| Published: | 6 Dec 2024 |
| DOI: | https://doi.org/10.1162/imag_a_00390 |
Publication 14043
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Abstract
Abstract Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here, we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The reliability and validity of these four distinct measures were tested in two large-scale databases, the UK Biobank and OASIS-3. The measures pertaining to skull BMD, skull bone thickness, and intensity-based adiposity proxy proved to be reliable (ICC = .95/.83/.66, p < .001) and valid, with high correlations to DXA-derived head BMD values (rho = .70, p < .001) and MRI-derived abdominal subcutaneous adipose volume (rho = .62, p < .001). Thickness-based adiposity proxy had only a low retest reliability (ICC = .53, p < .001). The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.</p>
5 Authors
- Polona Kalc
- Felix Hoffstaedter
- Eileen Luders
- Christian Gaser
- Robert Dahnke
1 Application
| Application ID | Title |
|---|---|
| 41655 | Characterizing brain networks and their inter-individual variability by high-throughput imaging and computational modelling |
Enabling scientific discoveries that improve human health