Corpus Callosum Aging Patterns: Insights from Diffusion MRI Parameterization
Poster No:
937
Submission Type:
Late-Breaking Abstract Submission
Authors:
Caio Pinheiro Santana1, Richard Frayne2, Leticia Rittner1
Institutions:
1Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil, 2University of Calgary, Calgary, Alberta, Canada
First Author:
Co-Author(s):
Introduction:
The corpus callosum (CC) is the largest white matter (WM) structure in the brain and the primary pathway for interhemispheric communication. Understanding age-related changes in the CC is crucial for assessing its role in cognition and disease (Turgut et al., 2023). Diffusion tensor imaging (DTI) enables the study of CC microstructure. However, the CC is often subdivided arbitrarily to extract average DTI metrics, which may obscure localized differences. Tract-Based Spatial Statistics (TBSS) (Smith et al., 2006) provides a voxel-wise analysis alternative but relies on a skeletonized representation, potentially missing changes near the CC's edges. This study proposes and evaluates a parameterization method to analyze age-related DTI changes in the CC, allowing a more detailed assessment while minimizing registration-related challenges.
Methods:
We used diffusion data from 303 healthy adults (58.7% female, mean±sd age: 48.5±17.6y, range: 18.2-91.8y) from the Calgary Normative Study (McCreary et al., 2020). Data preprocessing included denoising, motion and eddy current correction, resizing to 1.25mm isotropic voxels (MRtrix3), brain extraction (FSL), CC segmentation (TractSeg) (Wasserthal et al., 2018), and selection of the midsagittal slice (MS). The DTI model was computed (DIPY) to extract fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) maps.
The superior and inferior CC boundaries on the MS were each identified and sampled using 100 equally spaced points. Transverse lines consisting of 21 equally spaced samples connected ordered pairs of superior/inferior points. After removing extremity points, we obtained a 19×96 sample grid for each CC with interpolated DTI map values at each location. Individuals were grouped into seven age ranges, mean sampled DTI maps for each group and mean differences between groups were computed. Group comparisons were assessed using the Kruskal-Wallis and Dunn's post-hoc tests. Multiple comparisons were corrected using the false discovery rate method. Significance set at p<0.05. Results were mapped onto an averaged parameterized CC for visualization.
The superior and inferior CC boundaries on the MS were each identified and sampled using 100 equally spaced points. Transverse lines consisting of 21 equally spaced samples connected ordered pairs of superior/inferior points. After removing extremity points, we obtained a 19×96 sample grid for each CC with interpolated DTI map values at each location. Individuals were grouped into seven age ranges, mean sampled DTI maps for each group and mean differences between groups were computed. Group comparisons were assessed using the Kruskal-Wallis and Dunn's post-hoc tests. Multiple comparisons were corrected using the false discovery rate method. Significance set at p<0.05. Results were mapped onto an averaged parameterized CC for visualization.
Results:
Parameterized results reveal diffusion metrics changes with aging (Fig. 1), with change most evident in the two oldest groups. Overall, FA decreased, while MD, RD, and AD increased with age, though these patterns were not uniform across the CC. Group comparisons (Fig. 2) showed declines in FA, primarily concentrated in the genu, with additional changes seen in the anterior and posterior body and anterior splenium. MD and RD increased across most of the CC, except in the posterior splenium. AD increases were mainly found in the body and posterior genu, while portions of the genu and splenium showed AD reductions from young to middle adulthood. The results also suggested regional variation in the timing of changes.
Our findings align with previous studies. Bennett et al. (2009) reported FA reductions and AD and RD increases in the genu of younger (18-20y) to older (63-72y) adults. We found similar trends but less pronounced AD increases. Burzynska et al. (2009) observed FA decreases and RD and MD increases in the genu and body when comparing young (20-32y) and older (60-71y) adults. FA reductions in the body were not significant after accounting for WM hyperintensity, and AD increases were more prominent in the body than in the genu. We found FA reductions in the body limited to specific areas and primarily in the oldest adults. Additionally, we observed pronounced differences near the edges of the anterior splenium that may not have been detected in other TBSS-based studies due to their focus on central WM regions.
Our findings align with previous studies. Bennett et al. (2009) reported FA reductions and AD and RD increases in the genu of younger (18-20y) to older (63-72y) adults. We found similar trends but less pronounced AD increases. Burzynska et al. (2009) observed FA decreases and RD and MD increases in the genu and body when comparing young (20-32y) and older (60-71y) adults. FA reductions in the body were not significant after accounting for WM hyperintensity, and AD increases were more prominent in the body than in the genu. We found FA reductions in the body limited to specific areas and primarily in the oldest adults. Additionally, we observed pronounced differences near the edges of the anterior splenium that may not have been detected in other TBSS-based studies due to their focus on central WM regions.
Conclusions:
The parameterization method allows a detailed characterization of age-related changes in the CC, revealing localized diffusion midsagittal changes that other approaches may miss. These findings align with the existing literature yet offer new insights into the regional variability of CC aging.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
Aging
Data analysis
Development
MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Corpus Callosum
1|2Indicates the priority used for review
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2. Burzynska, A., Preuschhof, C., Bäckman, L., Nyberg, L., Li, S., Lindenberger, U., & Heekeren, H. (2009). Age-related differences in white matter microstructure: Region-specific patterns of diffusivity. NeuroImage, 49(3), 2104–2112. https://doi.org/10.1016/j.neuroimage.2009.09.041
3. McCreary, C. R., Salluzzi, M., Andersen, L. B., Gobbi, D., Lauzon, L., Saad, F., Smith, E. E., & Frayne, R. (2020). Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan. BMJ Open, 10(8), e038120. https://doi.org/10.1136/bmjopen-2020-038120
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5. Turgut, M., Tubbs, R. S., Turgut, A. T., & Bui, C. C. (2023). The corpus callosum: Embryology, Neuroanatomy, Neurophysiology, Neuropathology, and Surgery. Springer Nature.
6. Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018). TractSeg - Fast and accurate white matter tract segmentation. NeuroImage, 183, 239-253. https://doi.org/10.1016/j.neuroimage.2018.07.070