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The genetic and environmental etiology of novel frequency-driven regional parcellations of abnormal white matter
Shu-Ju Lin1,2, Nathan A Gillespie3,4, Randy Notestine5
1Department of Psychiatry, University of California, San Diego, La Jolla, California, USA.
Abstract:
The prevalence of white matter disease increases with age and is associated with cerebrovascular disease, cognitive decline, and risk for dementia. MRI measures of abnormal signal in the white matter (AWM) provide estimates of damage, however, regional patterns of AWM may be differentially influenced by genetic or environmental factors. With our data-driven regional parcellation approach, we created a probability distribution atlas using Vietnam Era Twin Study of Aging (VETSA) data (n = 475, mean age 67.6 years) and applied a watershed algorithm to define separate regional parcellations. We report biometrical twin modeling for five anatomically distinct regions: (1) Posterior, (2) Superior frontal and parietal, (3) Anterior and inferior frontal with deep areas, (4) Occipital, and (5) Anterior periventricular. We tested competing multivariate hypotheses to identify unique influences and to explain sources of covariance among the parcellations. Family aggregation could be entirely explained by additive genetic influences, with additive genetic variance (heritability) ranging from 0.69 to 0.79. Most genetic correlations between parcellations ranged from moderate to high (rg = 0.57-0.85), although two were small (rg = 0.35-0.39), consistent with varying degrees of unique genetic influences. This proof-of-principle investigation demonstrated the value of our novel, data-driven parcellations, with identifiable genetic and environmental differences, for future exploration.
Insights
Genetic factors significantly influence white matter disease patterns in aging brains. This study identified substantial heritability for abnormal white matter signals across distinct brain regions.
Area of Science:
- Neuroimaging
- Genetics
- Aging Research
Background:
- White matter disease prevalence rises with age, linked to cognitive decline and dementia risk.
- Magnetic Resonance Imaging (MRI) quantifies white matter damage, but regional patterns may have distinct genetic or environmental origins.
Purpose of the Study:
- To develop and apply a novel data-driven parcellation approach for analyzing regional white matter abnormalities.
- To investigate the genetic and environmental influences on distinct white matter regions using biometrical twin modeling.
Main Methods:
- Utilized Vietnam Era Twin Study of Aging (VETSA) data (n=475, mean age 67.6 years).
- Created a probability distribution atlas and applied a watershed algorithm for regional parcellation.
- Performed multivariate biometrical twin modeling on five defined anatomical regions.
Main Results:
- Additive genetic influences fully explained family aggregation of white matter abnormalities.
- Heritability estimates for abnormal white matter (AWM) ranged from 0.69 to 0.79 across regions.
- Genetic correlations between regions were mostly moderate to high (0.57-0.85), indicating shared genetic influences, with some smaller correlations (0.35-0.39) suggesting unique genetic effects.
Conclusions:
- The novel data-driven parcellations are valuable for dissecting genetic and environmental contributions to white matter disease.
- This approach provides a foundation for future research into the etiology of age-related white matter changes and their impact on cognition.
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