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Augmented DTI-ALPS for Assessing Interstitial Fluid Dynamics Associated with Glymphatic Function

Poster No:

1942 

Submission Type:

Abstract Submission 

Authors:

Chang-Le Chen1, Sang Joon Son2, Hecheng Jin1, Jinghang Li1, Noah Schweitzer1, Linghai Wang1, Chang Hyung Hong2, Hyun Woong Roh2, Yong Hyuk Cho2, Bumhee Park3, Na-Rae Kim3, Jin Wook Choi4, Sang Won Seo5, So Young Moon6, Seong Hye Choi6, Shaolin Yang1, Howard Aizenstein1, Minjie Wu1

Institutions:

1University of Pittsburgh, Pittsburgh, PA, 2Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea, Republic of, 3Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea, Republic of, 4Department of Radiology, Ajou University School of Medicine, Suwon, Korea, Republic of, 5Samsung Medical Center, Seoul, Korea, Republic of, 6Department of Neurology, Ajou University School of Medicine, Suwon, Korea, Republic of

First Author:

Chang-Le Chen  
University of Pittsburgh
Pittsburgh, PA

Co-Author(s):

Sang Joon Son, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Hecheng Jin  
University of Pittsburgh
Pittsburgh, PA
Jinghang Li  
University of Pittsburgh
Pittsburgh, PA
Noah Schweitzer  
University of Pittsburgh
Pittsburgh, PA
Linghai Wang  
University of Pittsburgh
Pittsburgh, PA
Chang Hyung Hong, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Hyun Woong Roh, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Yong Hyuk Cho  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Bumhee Park  
Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of
Na-Rae Kim  
Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of
Jin Wook Choi  
Department of Radiology, Ajou University School of Medicine
Suwon, Korea, Republic of
Sang Won Seo  
Samsung Medical Center
Seoul, Korea, Republic of
So Young Moon  
Department of Neurology, Ajou University School of Medicine
Suwon, Korea, Republic of
Seong Hye Choi  
Department of Neurology, Ajou University School of Medicine
Suwon, Korea, Republic of
Shaolin Yang, PhD  
University of Pittsburgh
Pittsburgh, PA
Howard Aizenstein, M.D., PhD.  
University of Pittsburgh
Pittsburgh, PA
Minjie Wu, PhD  
University of Pittsburgh
Pittsburgh, PA

Introduction:

The perivascular space plays an important role in the glymphatic system of the brain [1,2]. To capture the integrity of the perivascular space, diffusion MRI-based methods such as DTI-ALPS [3,4] have been proposed to quantify interstitial fluid (ISF) dynamics in the deep white matter (WM). However, the original DTI-ALPS focuses only on certain regions of interest (ROI) [5]. Also, it is susceptible to biases in spatial registration when automated methods are used. Thus, we proposed an augmented version of DTI-ALPS to mitigate the limitations, aiming to enhance the validity and better reflect the ISF diffusivity associated with glymphatic function.

Methods:

The DTI-ALPS evaluates the perivascular ISF movement along the medullary conduits perpendicular to WM tracts near lateral ventricles (LV). Inspired by this, we established an algorithm to automatically localize the periventricular space (PVS) horizontal to the LV as the target area (Figure 1). In practice, we implemented and tested our analytic pipeline based on a multi-site multi-modal database initiated by Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer's Disease Study [6.7]. We collected diffusion tensor images (DTI) across 7 sites with different acquisition settings (harmonization was used in further analyses) from 49 healthy controls (age: 71.0[6.0], sex: 24%male, and global clinical dementia rating (CDR): 0.42[0.19]), 295 patients with mild cognitive impairment (MCI) (age: 73.1[6.2], sex: 31%male, and global CDR: 0.51[0.14]) and 134 patients with dementia (age: 74.3[6.9], sex: 37%male, and global CDR: 0.89[0.44]). All DTI data went through standard preprocessing and were then reconstructed to the diffusion tensor in the MNI space using q-space diffeomorphic reconstruction [8], which spatially registers both brain morphologies and fiber orientations. To create a PVS coverage map, we registered a LV prior to the mean diffusivity (MD) map, and it was then expanded along the transverse axis by the region growth algorithm to target the PVS while omitting LV itself and callosal fibers. Moreover, we calculated voxel-wise transverse tensor ratio (TTR, the square of tensor element in the X axis divided by the product of tensor elements in the Y & Z axes) to represent the ISF diffusivity. The average of TTR in the bilateral PVS (namely augmented DTI-ALPS) was estimated to represent the perivascular integrity. To test the biological validity of the augmented DTI-ALPS, we performed partial correlation between diffusion-derived metrics and clinical factors as well as F18-flutemetamol PET-derived measures (adjusting age, sex, and education). Also, we conducted group comparisons (adjusting age, sex, and education) and tested age-by-sex interaction for the original and proposed DTI-ALPS metrics.
Supporting Image: Figure1.png
 

Results:

Partial correlation (significance level = 0.01) revealed that the original DTI-ALPS was significantly correlated with both whole WM fractional anisotropy (FA) and MD compared to the proposed one, suggesting the original may share the common information with DTI metrics (Figure 2). Both original and proposed DTI-ALPS were significantly correlated with clinical factors including global CDR, CDR sum of box (CDR-SB), MMSE, Shiraz verbal learning test (SVLT), and hippocampal atrophy. Moreover, the proposed metrics were exclusively significantly correlated with amyloid PET standard uptake value ratio (SUVR) in multiple regions. Both DTI-ALPS metrics showed significant age-by-sex interactions (p = 0.016 for the original & 0.014 for the proposed), but only the augmented DTI-ALPS showed significant main effect of age (p = 0.001). The proposed metrics showed significant difference (p < 0.001) between groups except the pair of HC-MCI.
Supporting Image: Figure2.png
 

Conclusions:

We established an automated pipeline to estimate augmented DTI-ALPS metrics in the PVS. This approach is more robust to the registration bias. Moreover, the proposed metrics can provide additional information potentially reflective of glymphatic system integrity.

Modeling and Analysis Methods:

Diffusion MRI Modeling and Analysis
Methods Development 1

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Anatomy and Functional Systems 2
White Matter Anatomy, Fiber Pathways and Connectivity

Keywords:

Data analysis
MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - glymphatic system

1|2Indicates the priority used for review

Provide references using author date format

[1] Wardlaw, J. M., Benveniste, H., Nedergaard, M., Zlokovic, B. V., Mestre, H., Lee, H., ... & colleagues from the Fondation Leducq Transatlantic Network of Excellence on the Role of the Perivascular Space in Cerebral Small Vessel Disease. (2020). Perivascular spaces in the brain: anatomy, physiology and pathology. Nature Reviews Neurology, 16(3), 137-153.
[2] Rasmussen, M. K., Mestre, H., & Nedergaard, M. (2018). The glymphatic pathway in neurological disorders. The Lancet Neurology, 17(11), 1016-1024.
[3] Taoka, T., Masutani, Y., Kawai, H., Nakane, T., Matsuoka, K., Yasuno, F., ... & Naganawa, S. (2017). Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases. Japanese journal of radiology, 35, 172-178.
[4] Taoka, T., Ito, R., Nakamichi, R., Kamagata, K., Sakai, M., Kawai, H., ... & Naganawa, S. (2022). Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study. Japanese journal of radiology, 40(2), 147-158.
[5] Moses, J., Sinclair, B., Law, M., O'Brien, T. J., & Vivash, L. (2023). Automated methods for detecting and quantitation of enlarged perivascular spaces on MRI. Journal of Magnetic Resonance Imaging, 57(1), 11-24.
[6] Son, S. J., Park, B., Roh, H. W., Kim, N., Jo, Y. H., Lee, E., ... & Hong, C. H. (2020). Biobank innovations for chronic cerebrovascular disease with Alzheimer's disease study (BICWALZS) Development of new models and analysis methods/screening studies/platforms. Alzheimer's & Dementia, 16, e042259.
[7] Karim, H. T., Aizenstein, H. J., Mizuno, A., Ly, M., Andreescu, C., Wu, M., ... & Son, S. J. (2022). Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction. Molecular Psychiatry, 27(12), 5235-5243.
[8] Yeh, F. C., & Tseng, W. Y. I. (2011). NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage, 58(1), 91-99.