Heritability study of superficial white matter microstructure using diffusion MRI and the HCP databa
Poster No:
1785
Submission Type:
Abstract Submission
Authors:
Nabil VINDAS YASSINE1, Nicole Labra2, Ege Kıbrıslıoğlu1, Vincent Frouin3, Antoine Grigis1, 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:
Superficial white matter (SWM) plays a critical role in cortico-cortical communication, yet research into SWM has lagged behind deep white matter (DWM) due to its small diameter and complex tractography near the cortex [7, 8]. Recent advancements in non-invasive imaging now enable more detailed mapping of SWM [7, 3]. Despite growing interest in SWM aging and development, its genetic architecture remains underexplored. Here, we investigate SWM genetics by calculating heritability and genetic correlations of SWM microstructure across 700+ bundles from the ESBA (short-range) and LNAO-SWM79 (mid-range) EBRAINS infrastructure atlases using Human Connectome Project (HCP) [1] data from 1,030 participants.
Methods:
a - Materials
We used data from 1,030 healthy HCP participants (aged 22–36 years, including twins and extended familial relationships), all with diffusion MRI (dMRI) and anatomical T1 data [1]. The ESBA short-range atlas [6] and LNAO-SWM79 mid-range [4] atlas served as SWM references. Microstructural properties were assessed using two DTI metrics (FA, MD) and three NODDI metrics (ICVF, ISOVF, OD).
b - Methods
Tractograms were segmented using GeoLab [9], identifying bundles via ESBA and LNAO-SWM79 atlases across subjects. For each diffusion-derived metric and subject, bundle-level values were computed as the mean across streamlines, resulting in over 3,500 phenotypes per subject (700+ bundles × 5 metrics).
Heritability and bivariate heritability estimates were calculated using SOLAR-Eclipse (version 8.4.2), incorporating HCP pedigree data. Multiple comparisons were addressed by retaining only bundles with heritability p-values < 1e-6. From bivariate heritabiliy analysis, genetic correlation matrices were generated for each metric, capturing correlations across bundles from both atlases.
We used data from 1,030 healthy HCP participants (aged 22–36 years, including twins and extended familial relationships), all with diffusion MRI (dMRI) and anatomical T1 data [1]. The ESBA short-range atlas [6] and LNAO-SWM79 mid-range [4] atlas served as SWM references. Microstructural properties were assessed using two DTI metrics (FA, MD) and three NODDI metrics (ICVF, ISOVF, OD).
b - Methods
Tractograms were segmented using GeoLab [9], identifying bundles via ESBA and LNAO-SWM79 atlases across subjects. For each diffusion-derived metric and subject, bundle-level values were computed as the mean across streamlines, resulting in over 3,500 phenotypes per subject (700+ bundles × 5 metrics).
Heritability and bivariate heritability estimates were calculated using SOLAR-Eclipse (version 8.4.2), incorporating HCP pedigree data. Multiple comparisons were addressed by retaining only bundles with heritability p-values < 1e-6. From bivariate heritabiliy analysis, genetic correlation matrices were generated for each metric, capturing correlations across bundles from both atlases.
Results:
Table 1 summarizes mean heritability values for all metrics across ESBA and LNAO-SWM79 bundles. Heritability was generally higher for LNAO-SWM79 bundles, except for ISOVF. For both atlases, ICVF showed the highest heritability, followed by FA and MD. Figure 1 highlights spatial heterogeneity and high heritability (~0.6) in select bundles, warranting further exploration through genetic correlation.
Figure 2 presents ICVF genetic correlations for bundles associated with language areas, including the left and right pars opercularis and a left hemisphere bundle connecting inferior parietal and middle temporal regions. The left pars opercularis displayed strong asymmetric genetic correlations with nearby bundles in Broca's area, while the right pars opercularis exhibited more diffuse, global correlations. Additional bundles demonstrated symmetrical genetic correlations across broader brain regions, reflecting varying genetic constraints on SWM microstructure.
Figure 2 presents ICVF genetic correlations for bundles associated with language areas, including the left and right pars opercularis and a left hemisphere bundle connecting inferior parietal and middle temporal regions. The left pars opercularis displayed strong asymmetric genetic correlations with nearby bundles in Broca's area, while the right pars opercularis exhibited more diffuse, global correlations. Additional bundles demonstrated symmetrical genetic correlations across broader brain regions, reflecting varying genetic constraints on SWM microstructure.
Conclusions:
This study is one of the first to examine SWM heritability using both DTI and NODDI metrics across 700+ bundles. High heritability for key diffusion metrics aligns with previous findings [2, 5, 10], supporting a strong genetic influence on SWM microstructure. Notably, short-range SWM (ESBA) exhibited lower heritability than mid-range SWM (LNAO-SWM79), suggesting greater environmental effects on short-range bundles. Regional analyses revealed distinct spatial heritability patterns, indicating bundle length may influence genetic contributions. Genetic correlation results showed variability across bundles, with some exhibiting strong local correlations and others sharing broader genetic constraints with the rest of the brain. While heritability estimates from the HCP may overstate genetic effects, these findings provide a robust foundation for future research. Next steps include analyzing larger datasets, such as the UK Biobank, and employing GWAS approaches to refine heritability estimates and deepen our understanding of SWM genetics.
Genetics:
Genetics Other 2
Lifespan Development:
Aging
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Aging
Modeling
MRI
Myelin
NORMAL HUMAN
Statistical Methods
Sub-Cortical
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Heritability
1|2Indicates the priority used for review
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