RADIOMICS
Patterns of White Matter Lesions in the Brain: Impact of Air Pollution on the Variability of Lifetime Trajectories
To better understand the impact of influencing factors on the occurrence of White Matter Lesion (WML) occurrence in the brain, such as ambient air pollution, the development of automated algorithms to detect the spatial distribution patterns of WML and the definition of an easy-to-use quantification parameter for this distribution are essential.
RADIOMICS
Patterns of White Matter Lesions in the Brain: Impact of Air Pollution on the Variability of Lifetime Trajectories
To better understand the impact of influencing factors on the occurrence of White Matter Lesion (WML) occurrence in the brain, such as ambient air pollution, the development of automated algorithms to detect the spatial distribution patterns of WML and the definition of an easy-to-use quantification parameter for this distribution are essential.
Overview
A very common affection of the aging brain is the occurrence of alterations of the white matter. These so-called white matter lesions (WML) are thought to be of cerebrovascular origin, with additional neuropathological factors contributing. Due to their location, they affect the connectivity of diverse cognitive networks and can thus lead to severe cognitive decline or contribute to the pathophysiological symptomatology of dementia.
Long-term exposure to ambient air pollution has been associated with cerebrovascular disease and with increased risk of cognitive decline, cognitive impairment and dementia, with traffic-related exposures being particularly important. Ambient particulate matter (PM) may have direct neurotoxic effects on the brain as well as indirect impacts through effects on blood-brain barrier function or promotion of cardiovascular or cerebrovascular disease. However, relatively little work has been done to examine the link between ambient air pollution and brain structural changes such as reduced white matter and increased WML volumes.
- to optimize and compare lesion-growth vs. machine-learning algorithms for quantification of WML volume as well as developing a mapping and quantification algorithm of WML spatial distribution patterns
- to quantify the association of long-term exposure to residential air pollution with WML volume, also with regard to different participant groups (e.g. high BMI, hypertension)
The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study, conducted at 18 sites throughout Germany and aims to recruit 200.000 participants. In five of 18 study centers a whole-body 3T MRI on dedicated scanners is additionally done in 30,000 individuals. Furthermore, repeated MRI data of about 7.500 participants (ongoing follow-up examination) from all five MRI sites as well as face-to-face interviews and self-administered questionnaires are collected.
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