Interstitial fluid dynamics assessed by diffusion MRI may predict Aβ positivity conversion
Poster No:
189
Submission Type:
Abstract Submission
Authors:
Kaito Takabayashi1, Koji Kamagata1, Christina Andica1,2,3, Rui Zou1,2, Wataru Uchida3, Takafumi Kitagawa1,2, Sen Guo1, Junko Kikuta1, Akifumi Hagiwara1, Shigeki Aoki1,2,3
Institutions:
1Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan, 2Department of Data Science, Juntendo University Graduate School of Medicine, Tokyo, Japan, 3Faculty of Health Data Science, Juntendo University, Chiba, Japan
First Author:
Kaito Takabayashi
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Co-Author(s):
Christina Andica
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Rui Zou
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Takafumi Kitagawa
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Akifumi Hagiwara
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Shigeki Aoki
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Introduction:
The accumulation of extracellular amyloid-β (Aβ), a pathologic hallmark of Alzheimer's disease (AD), begins more than 15 years before the onset of dementia (Sperling et al., 2013). Meanwhile, anti-amyloid therapies aimed at reducing Aβ accumulation and preventing cognitive decline have recently become widespread, but their therapeutic efficacy remains limited. This may be because interventions were implemented at a late stage of the disease. It may be clinically beneficial to identify patients with subthreshold Aβ levels that may change to Aβ (+) in the future and provide them with anti-amyloid therapy.
Recently, it has been suggested that abnormalities in brain interstitial fluid (ISF) dynamics are involved in Aβ accumulation. Estimation of the interstitial water content in the brain parenchyma, calculated as the volume fraction of free-water (FW) in a voxel with diffusion MRI, has become possible (Pasternak et al., 2009). It has also been reported that FW in white matter (WM) is associated with Aβ level in patients with AD (Kamagata et al., 2022). However, the association between ISF dynamics and Aβ accumulation in Aβ (−) healthy individuals remains poorly understood.
Therefore, the current study aimed to investigate whether FW can identify individuals who will convert to Aβ (+) and can predict future Aβ accumulation in Aβ (−) healthy individuals.
Recently, it has been suggested that abnormalities in brain interstitial fluid (ISF) dynamics are involved in Aβ accumulation. Estimation of the interstitial water content in the brain parenchyma, calculated as the volume fraction of free-water (FW) in a voxel with diffusion MRI, has become possible (Pasternak et al., 2009). It has also been reported that FW in white matter (WM) is associated with Aβ level in patients with AD (Kamagata et al., 2022). However, the association between ISF dynamics and Aβ accumulation in Aβ (−) healthy individuals remains poorly understood.
Therefore, the current study aimed to investigate whether FW can identify individuals who will convert to Aβ (+) and can predict future Aβ accumulation in Aβ (−) healthy individuals.
Methods:
The data used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative-GO, 2 cohort. We selected Aβ (−) cognitively normal subjects with baseline MRI scan and longitudinal 18F-florbetapir (AV45) PET scans with a total follow-up duration longer than 6.4 years. This cutoff for follow-up duration was defined based on the reported mean follow-up time for the transition from Aβ (−) to Aβ (+) (Jagust et al., 2021). Finally, we defined Aβ (−) subjects who converted to Aβ (+) within follow-up durations as converters and subjects who remained Aβ (−) as non-converters (Table 1). Amyloid positivity on AV45 PET was defined as above the global standardized uptake value ratio (SUVR) threshold of 1.11 (Landau et al., 2014).
Diffusion-weighted images (DWI; b-value = 1,000 s/mm2) were preprocessed with MRtrix3. FW map was estimated by preprocessed DWI to a bi-tensor model using the Freewater Estimator running Interpolated Initialization algorithm (Parker et al., 2020). Then, the mean cerebral FW (FW-WM) was calculated excluding WM lesions.
FW-WM was compared between groups using a general linear model while controlling for age, sex, years of education, APOE ε4 status, intracranial volume, scanning site, and follow-up duration. In addition, we applied multiple linear regression (MLR) to model the annual rate of change of global SUVR (dependent variable) and baseline FW-WM (independent variable), including the same covariates plus baseline global SUVR as covariates. A p-value <0.05 was considered statistically significant.
Diffusion-weighted images (DWI; b-value = 1,000 s/mm2) were preprocessed with MRtrix3. FW map was estimated by preprocessed DWI to a bi-tensor model using the Freewater Estimator running Interpolated Initialization algorithm (Parker et al., 2020). Then, the mean cerebral FW (FW-WM) was calculated excluding WM lesions.
FW-WM was compared between groups using a general linear model while controlling for age, sex, years of education, APOE ε4 status, intracranial volume, scanning site, and follow-up duration. In addition, we applied multiple linear regression (MLR) to model the annual rate of change of global SUVR (dependent variable) and baseline FW-WM (independent variable), including the same covariates plus baseline global SUVR as covariates. A p-value <0.05 was considered statistically significant.
Results:
A total of 35 subjects were enrolled in the present study: 17 converters and 18 non-converters. There were no significant differences in age; sex; years of education; APOE ε4 carriers; follow-up durations; or baseline global SUVR between groups.
Compared with non-converters, the converters had significantly higher baseline FW-WM (p = 0.029, Cohen's d = 0.58; Figure 1A). In the MLR analysis, higher FW-WM was significantly associated with the higher annual rate of change of global SUVR (standardized β = 0.44, p = 0.025; Figure 1B).
Compared with non-converters, the converters had significantly higher baseline FW-WM (p = 0.029, Cohen's d = 0.58; Figure 1A). In the MLR analysis, higher FW-WM was significantly associated with the higher annual rate of change of global SUVR (standardized β = 0.44, p = 0.025; Figure 1B).
Conclusions:
FW has been reported to correlate with brain clearance function measurements calculated by MRI with intrathecal contrast administration (Li et al., 2024), and elevated FW may indicate decreased Aβ efflux capacity due to abnormal ISF dynamics. Therefore, our findings suggest that evaluation of FW-WM might be useful for detecting Aβ converters and that abnormalities in ISF dynamics may be associated with the Aβ accumulation in Aβ (−) healthy individuals.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Novel Imaging Acquisition Methods:
Diffusion MRI 2
Keywords:
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Interstitial fluid dynamics
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Kamagata, K (2022). Association of MRI Indices of Glymphatic System With Amyloid Deposition and Cognition in Mild Cognitive Impairment and Alzheimer Disease. Neurology, 99(24), e2648-e2660.
Landau, S. M (2014). Amyloid PET imaging in Alzheimer's disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging, 41(7), 1398-1407.
Li, Y (2024). Novel functional network-level glymphatic clearance associated with network connectivity in human. OHBM2024 annual meeting.
Parker, D (2020). Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data. PLoS One, 15(5), e0233645.
Pasternak, O (2009). Free water elimination and mapping from diffusion MRI. Magn Reson Med, 62(3), 717-730.
Sperling, R. A (2013). Preclinical Alzheimer disease-the challenges ahead. Nat Rev Neurol, 9(1), 54-58.