Author
Listed:
- Simon R. Cox
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK
Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh EH8 9JZ, UK)
- Stuart J. Ritchie
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK)
- Elliot M. Tucker-Drob
(University of Texas)
- David C. Liewald
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK)
- Saskia P. Hagenaars
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK
University of Edinburgh, Edinburgh EH10 5HF, UK)
- Gail Davies
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK)
- Joanna M. Wardlaw
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh EH8 9JZ, UK
Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh)
- Catharine R. Gale
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK
MRC Lifecourse Epidemiology Unit, University of Southampton)
- Mark E. Bastin
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh EH8 9JZ, UK
Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh)
- Ian J. Deary
(Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh
University of Edinburgh, Edinburgh EH8 9JZ, UK)
Abstract
Quantifying the microstructural properties of the human brain’s connections is necessary for understanding normal ageing and disease. Here we examine brain white matter magnetic resonance imaging (MRI) data in 3,513 generally healthy people aged 44.64–77.12 years from the UK Biobank. Using conventional water diffusion measures and newer, rarely studied indices from neurite orientation dispersion and density imaging, we document large age associations with white matter microstructure. Mean diffusivity is the most age-sensitive measure, with negative age associations strongest in the thalamic radiation and association fibres. White matter microstructure across brain tracts becomes increasingly correlated in older age. This may reflect an age-related aggregation of systemic detrimental effects. We report several other novel results, including age associations with hemisphere and sex, and comparative volumetric MRI analyses. Results from this unusually large, single-scanner sample provide one of the most extensive characterizations of age associations with major white matter tracts in the human brain.
Suggested Citation
Simon R. Cox & Stuart J. Ritchie & Elliot M. Tucker-Drob & David C. Liewald & Saskia P. Hagenaars & Gail Davies & Joanna M. Wardlaw & Catharine R. Gale & Mark E. Bastin & Ian J. Deary, 2016.
"Ageing and brain white matter structure in 3,513 UK Biobank participants,"
Nature Communications, Nature, vol. 7(1), pages 1-13, December.
Handle:
RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13629
DOI: 10.1038/ncomms13629
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