Superficial White Matter ageing study using advanced diffusion MRI from UK Biobank database (N=36287
Poster No:
1786
Submission Type:
Abstract Submission
Authors:
Nabil VINDAS YASSINE1, Nicole Labra2, Vincent Frouin3, Jean-François Mangin4
Institutions:
1Neurospin, Paris, Paris, 2University College London, London, London, 3CEA/Neurospin, Gif-sur_Yvette, France, 4Université Paris-Saclay, Gif-sur-Yvette, France
First Author:
Co-Author(s):
Introduction:
White matter (WM) aging involves structural and functional changes to synapses, critical for neuronal communication. While deep WM (DWM) aging is well-documented, superficial WM (SWM) remains underexplored due to its small bundle diameter and complex cortical proximity [7]. Recent advancements in imaging techniques now enable detailed SWM mapping [2, 7]. Using diffusion MRI (dMRI) and T1 data from 36,287 UK Biobank participants, we investigated associations between age, sex, and white matter volume with SWM microstructure across 700+ bundles from the ESBA (short-range) [5], LNAO-SWM79 (mid-range) [3], and LNAO-DWM12 (DWM) [4] EBRAINS infrastructure atlases.
Methods:
a - Materials
We analyzed data from 36,287 healthy participants (45–82 years, 52% female) with multi-shell dMRI and anatomical T1 data from UKBiobank (https://www.ukbiobank.ac.uk/). Tractograms were segmented in SWM bundles using using the ESBA and LNAO-SWM79 atlases, while LNAO-DWM12 served as a DWM reference. Microstructural measures included two DTI metrics (FA, MD) and three NODDI metrics (ICVF, ISOVF, OD).
b - Methods
Probabilistic tractograms were computed using MRtrix [9] and segmented with GeoLab [10] for SWM, and bundleSeg [8] for DWM. Bundle-level values for each subject and metric were calculated as the mean across streamlines. Associations with age, sex, and white matter volume (WMV) were modeled as:
metric = β0 + β1 * age + β2 * age² + β3 * sex + β4 * age x sex + β2 * WMV
Only variables with p < 1e-6 (Bonferroni correction) were retained. For models with significant age2 term, an ANOVA comparison determined inclusion of the age2 term based on performance. Aging speed (first derivative) and acceleration (~age2) were derived to assess aging dynamics. To identify SWM resilience/vulnerability, aging speeds for each Desikan-Killiany region were averaged across bundles passing through each region. Regions were ranked based on aging speeds at 50, 65, and 80 years. A voting system identified the top 5 fastest and slowest aging regions across 30 tests (2 atlases × 5 metrics × 3 ages), normalized for consistency.
We analyzed data from 36,287 healthy participants (45–82 years, 52% female) with multi-shell dMRI and anatomical T1 data from UKBiobank (https://www.ukbiobank.ac.uk/). Tractograms were segmented in SWM bundles using using the ESBA and LNAO-SWM79 atlases, while LNAO-DWM12 served as a DWM reference. Microstructural measures included two DTI metrics (FA, MD) and three NODDI metrics (ICVF, ISOVF, OD).
b - Methods
Probabilistic tractograms were computed using MRtrix [9] and segmented with GeoLab [10] for SWM, and bundleSeg [8] for DWM. Bundle-level values for each subject and metric were calculated as the mean across streamlines. Associations with age, sex, and white matter volume (WMV) were modeled as:
metric = β0 + β1 * age + β2 * age² + β3 * sex + β4 * age x sex + β2 * WMV
Only variables with p < 1e-6 (Bonferroni correction) were retained. For models with significant age2 term, an ANOVA comparison determined inclusion of the age2 term based on performance. Aging speed (first derivative) and acceleration (~age2) were derived to assess aging dynamics. To identify SWM resilience/vulnerability, aging speeds for each Desikan-Killiany region were averaged across bundles passing through each region. Regions were ranked based on aging speeds at 50, 65, and 80 years. A voting system identified the top 5 fastest and slowest aging regions across 30 tests (2 atlases × 5 metrics × 3 ages), normalized for consistency.
Results:
Table 1 shows mean aging speed at 65 years and mean acceleration for all metrics and atlases. SWM exhibited higher aging speed than DWM for all metrics except ICVF. Conversely, DWM showed greater acceleration for FA, ICVF, and ISOVF, while SWM had higher acceleration for MD and OD. Figure 1 highlights FA and ICVF acceleration patterns, revealing spatial heterogeneity across bundles and atlases.
Figure 2 identifies regions exhibiting resilience or vulnerability (≥25% occurrence). Resilient regions in the left hemisphere include occipital, medial temporal, and cingulate areas, while in the right hemisphere, they appear in occipital, inferior temporal, and cingulate areas. Vulnerable regions in both hemispheres are primarily frontal, middle temporal, and Broca's regions, with additional vulnerability in the right inferior parietal region. These findings align with the anterior-posterior gradient hypothesis [6], where anterior regions show vulnerability and posterior regions exhibit resilience.
Figure 2 identifies regions exhibiting resilience or vulnerability (≥25% occurrence). Resilient regions in the left hemisphere include occipital, medial temporal, and cingulate areas, while in the right hemisphere, they appear in occipital, inferior temporal, and cingulate areas. Vulnerable regions in both hemispheres are primarily frontal, middle temporal, and Broca's regions, with additional vulnerability in the right inferior parietal region. These findings align with the anterior-posterior gradient hypothesis [6], where anterior regions show vulnerability and posterior regions exhibit resilience.
Conclusions:
To our knowledge, this is the first SWM aging study involving 36,287 participants, 700+ bundles, and both DTI and NODDI metrics. Our results reveal that SWM follows similar age-related trends as DWM but it exhibits greater heterogeneity and faster aging dynamics overall. Spatial analyses further identified Desikan-Killiany regions with the fastest (vulnerable) and slowest (resilient) aging, consistent with the anterior-posterior gradient hypothesis. Vulnerable regions, primarily in frontal areas, contrast with resilient regions in posterior areas, many of which align with cortical features linked to cognitive aging [1]. Future work will explore the relationship between SWM microstructure in these regions and cognitive decline, offering insights into brain aging mechanisms.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
ADULTS
Aging
Demyelinating
MRI
Statistical Methods
Sub-Cortical
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Superficial White Matter
1|2Indicates the priority used for review
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Provide references using APA citation style.
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