White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.

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Publication Details

Journal
NeuroImage
Published
2025-03-15
DOI
https://doi.org/10.1016/j.neuroimage.2025.121132
Citations
4
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
5.44

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article

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Lars Tjelta Westlye, Ole A. Andreassen, Mario Tranfa, Giuseppe Pontillo et al.
4 citations
NeuroImage
Advanced Neuroimaging Techniques and Applications
5.44
article

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Lars Tjelta Westlye, Ole A. Andreassen, Mario Tranfa, Giuseppe Pontillo, Ivan I. Maximov, Max Korbmacher, Dennis van der Meer, Mengyun Wang
article en
4 citations

Abstract

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.

NeuroImageVol. 310
Oslo University Hospital (NO), Western Norway University of Applied Sciences (NO), University of Oslo (NO), Haukeland University Hospital (NO), Max Planck Institute for Psycholinguistics (NL), University College London (GB), Vrije Universiteit Amsterdam (NL)
European Commission, Norges Forskningsråd, Helse Sør-Øst RHF
Openalex Percentile: Top 5%
Advanced Neuroimaging Techniques and Applications
5.44
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