The Relationship Between Arterial Lumen Diameter and Cortical Thickness
Poster No:
2105
Submission Type:
Abstract Submission
Authors:
Onurhan Karatay1, Melina Engelhardt1, Ahmed Khalil1, Thomas Picht1
Institutions:
1Charité – Universitätsmedizin Berlin, Berlin, Germany
First Author:
Co-Author(s):
Introduction:
Cardiovascular risk factors and cortical thickness are valuable markers of brain health, providing critical insights into aging, neurodegeneration, and vascular influences on brain structure. The arterial system is crucial in delivering nutrients and maintaining brain function. It is also susceptible to conditions such as stenosis, which can significantly increase the risk of cerebrovascular events like stroke or transient ischemic attacks (TIA) and contribute to cognitive impairment. This study examines the relationship between changes in arterial diameter and cortical thickness to quantify the link between cardiovascular health and brain structure. Understanding this relationship may reveal mechanisms contributing to brain health, highlight potential vulnerabilities, and provide insights into the changes associated with diseases of the brain and arterial system.
Methods:
Data were derived from 493 participants with 663 imaging sessions in the OASIS-3 (LaMontagne, 2019) dataset, including 3 Tesla MRI scans with Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) using a multi-slab 3D gradient-echo sequence (TR = 0.023 s, TE = 0.0036 s) and T1-weighted MPRAGE (TR = 2.3 s, TE = 0.00295 s).
Arteries including the Left and Right Internal Carotid Arteries (l-ICA, r-ICA), Basilar Artery (BAS) and the first segments of Anterior Cerebral Arteries (l-ACA, r-ACA), Middle Cerebral Arteries (l-MCA, r-MCA), Posterior Cerebral Arteries (l-PCA, r-PCA), were segmented from TOF-MRA images using eICAB (Dumais, 2022), a deep-learning-based segmentation toolbox. The mean arterial lumen diameter (Bizeau, 2018) was extracted as the primary vascular metric.
Recon-all outputs from the dataset, processed using FreeSurfer v5.3, were utilized to obtain cortical thickness measurements from T1-weighted MPRAGE scans. Total intracranial volume was used to normalize arterial diameters across subjects.
The Level 2 labels of the Digital 3D Arterial Atlas (Liu, 2023) were applied to assign cortical areas to the perfusion territories of the segmented arteries.
Linear mixed models (LMMs) were applied separately for each arterial area and the corresponding arterial diameter to examine their relationships with cortical thickness. The fixed effects in these models included normalized arterial diameter and age at visit, while a random intercept was specified for each participant to account for within-subject variability. The dependent variable was cortical thickness, which was assessed across all models.
Arteries including the Left and Right Internal Carotid Arteries (l-ICA, r-ICA), Basilar Artery (BAS) and the first segments of Anterior Cerebral Arteries (l-ACA, r-ACA), Middle Cerebral Arteries (l-MCA, r-MCA), Posterior Cerebral Arteries (l-PCA, r-PCA), were segmented from TOF-MRA images using eICAB (Dumais, 2022), a deep-learning-based segmentation toolbox. The mean arterial lumen diameter (Bizeau, 2018) was extracted as the primary vascular metric.
Recon-all outputs from the dataset, processed using FreeSurfer v5.3, were utilized to obtain cortical thickness measurements from T1-weighted MPRAGE scans. Total intracranial volume was used to normalize arterial diameters across subjects.
The Level 2 labels of the Digital 3D Arterial Atlas (Liu, 2023) were applied to assign cortical areas to the perfusion territories of the segmented arteries.
Linear mixed models (LMMs) were applied separately for each arterial area and the corresponding arterial diameter to examine their relationships with cortical thickness. The fixed effects in these models included normalized arterial diameter and age at visit, while a random intercept was specified for each participant to account for within-subject variability. The dependent variable was cortical thickness, which was assessed across all models.
Results:
The participants had a mean age of 70.54 years (±8.15), and age at visit showed a significant relationship with cortical thickness across all arterial models, with older participants exhibiting lower cortical thickness.
Anterior Circulation: Arteries in the anterior circulation, including the l-ICA, r-ICA, l-MCA, r-MCA, l-ACA, and r-ACA, showed a significant relationship with cortical thickness. Larger arterial diameters were associated with higher cortical thickness in their respective perfusion regions.
Posterior Circulation: Arteries in the posterior circulation, including the l-PCA, r-PCA, and BAS, did not show a significant relationship with cortical thickness.
Anterior Circulation: Arteries in the anterior circulation, including the l-ICA, r-ICA, l-MCA, r-MCA, l-ACA, and r-ACA, showed a significant relationship with cortical thickness. Larger arterial diameters were associated with higher cortical thickness in their respective perfusion regions.
Posterior Circulation: Arteries in the posterior circulation, including the l-PCA, r-PCA, and BAS, did not show a significant relationship with cortical thickness.
·Table 1. Mixed Linear Model Results: The Relationships Between Arterial Diameter, Age, and Cortical Thickness
Conclusions:
This study highlights the relationship between the vascular system and cortical integrity, emphasizing the distinct contributions of the anterior and posterior circulations. The significant associations observed in the anterior circulation suggest a stronger link between arterial diameter and cortical structure, which may be attributed to the higher blood flow rates of the anterior circulation (Dunås, 2019) and associated metabolic demand.
These findings demonstrate the need for further research to explore the mechanistic links between vascular structure and cortical health and their implications for monitoring and managing vascular-related conditions, including neurodegeneration and cerebrovascular disease.
These findings demonstrate the need for further research to explore the mechanistic links between vascular structure and cortical health and their implications for monitoring and managing vascular-related conditions, including neurodegeneration and cerebrovascular disease.
Lifespan Development:
Aging
Lifespan Development Other
Modeling and Analysis Methods:
Image Registration and Computational Anatomy
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 1
Keywords:
Morphometrics
MR ANGIOGRAPHY
STRUCTURAL MRI
Other - arterial diameter
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dumais, F. (2022). eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis. NeuroImage, 260, 119425. https://doi.org/10.1016/j.neuroimage.2022.119425
Dunås, T., Holmgren, M., Wåhlin, A., Malm, J., & Eklund, A. (2019). Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI: Evaluation with three segmentation methods. Journal of magnetic resonance imaging : JMRI, 50(2), 511–518. https://doi.org/10.1002/jmri.26641
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
LaMontagne, P.J. (2019). OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. medRxiv. https://doi.org/10.1101/2019.12.13.19014902
Liu, C.F. (2023). Digital 3D brain MRI arterial territories atlas. Scientific Data, 10(1), 74. https://doi.org/10.1038/s41597-022-01923-0