Age-Related Variations in Glymphatic System Captured by Perivascular Diffusivity
Poster No:
1603
Submission Type:
Abstract Submission
Authors:
Chao-Hsin Ding1, Chih-Chin Heather Hsu1, Shin Tai Chong1, Ching-Po Lin1,2
Institutions:
1Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
First Author:
Co-Author(s):
Chih-Chin Heather Hsu
Institute of Neuroscience, National Yang Ming Chiao Tung University
Taipei, Taiwan
Institute of Neuroscience, National Yang Ming Chiao Tung University
Taipei, Taiwan
Ching-Po Lin
Institute of Neuroscience, National Yang Ming Chiao Tung University|Department of Education and Research, Taipei City Hospital
Taipei, Taiwan|Taipei, Taiwan
Institute of Neuroscience, National Yang Ming Chiao Tung University|Department of Education and Research, Taipei City Hospital
Taipei, Taiwan|Taipei, Taiwan
Introduction:
The glymphatic system plays a crucial role in brain waste clearance. In recent years, research has delved into understanding the relationship between aging and the glymphatic system functionality [1]. In this system, the influx of CSF into the perivascular space (PVS) plays an important role in clearing brain waste, enabling fluid and solute exchange in the brain, and supporting overall brain health. Previous studies have suggested that the dysfunction of PVS influx may contribute to various neurological diseases [2]. Capturing the PVS diffusivity may serve as a biomarker for evaluating glymphatic system performance. However, methods for animal studies are usually invasive [3], while human studies, though effective in capturing glymphatic function in disease or aging, rely on indirect, region-specific measures [4]. In this study, we aim to directly quantify the diffusivity within the PVS area. We utilized the T1-weighted (T1w) images for PVS segmentation [5] and pseudo diffusion tensor images (pDTI) [6] for quantifying the diffusivity within the PVS area. We examined the relation between PVS volume fraction (PVSVF) and the diffusivity within the PVS in adult group to evaluate its potential as a biomarker of glymphatic function.
Methods:
Eighty-nine healthy participants (age range: 20-50, median: 27.9±6.15) underwent T1w and diffusion-weighted imaging (DWI) scans on a 3T MRI scanner at National Yang Ming Chiao Tung University. DWIs were acquired at two b-values (300 and 500) with 20 directions in each shell. For PVS mapping, T1w images were preprocessed by following steps: image alignment, bias correction, and denoising via ANTs, FSL, and an adaptive non-local means filter developed by José V. Manjón [7], respectively. After preprocessing, Frangi filter was used for PVS segmentation with a threshold of t ̅=2.3 [5, 8]. Finally, we calculated the PVSVF by dividing the voxel count within the PVS area by the total intracranial volume. On the other hand, DWIs were preprocessed via iDIO and the pDTI was calculated by MRtrix . The PVS regions were then overlaid on the pDTI map to investigate their diffusivity characteristics.
Results:
Figure 1 shows the T1w image, PVS area map, and the pseudo-diffusivity (D*) map from a representative participant. Across participants, the median PVSVF was 0.002±3.67×10-4. The scatter plot in Figure 2a showed no clear correlation among PVSVF, D*, and age. K-mean clustering categorized participants into four subgroups. The Kruskal-Wallis's test didn't indicate a statistically significant difference in age across groups (p=0.09), yet a trend of increasing PVSVF with age was noted. The youngest participants (G4) had lower PVSVF and higher D* (median age=25.6±5.30), whereas the oldest participants (G3) demonstrated higher values in both measures (median age=32.9±6.63) (Figure 2b and 2d). Moreover, when aggregating the data by PVSVF, G1+G3, with higher PVSVF, corresponded to an older age profile (median age=29.4±6.08), while G2+G4, with lower PVSVF, had a younger demographic (median age=26.8±6.14) (Figure 2c). Similarly, when groups were combined based on D*, participants with lower D* (G1+G2) were older (median age=29.3±6.11) compared to those with higher D* (group G3+G4) who were younger (median age=26.1±6.07) (Figure 2e).
Conclusions:
Despite the absence of a clear correlation between PVSVF, D*, and age in the overall sample, the application of the K-mean clustering revealed distinct age-related patterns within specific subgroups. Our findings suggest an association of lower PVSVF and higher D* with younger individuals. Conversely, in the aggregated groups, participants with higher PVSVF and lower D* tended to be older. These patterns underline the potential of PVSVF and D* as indicators for assessing glymphatic function across age demographics. Future research could benefit from expanding the age range and sample size or incorporating longitudinal data to substantiate the reliability and clinical relevance of these measures.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Methods Development 2
Keywords:
Data analysis
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Perivascular Space
1|2Indicates the priority used for review
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