A Noninvasive Approach to Measure Dysfunction in the Brain Waste Clearance System
Poster No:
1306
Submission Type:
Abstract Submission
Authors:
Dimuthu Hemachandra1, Eric T. Peterson1,3, Eva M. Müller-Oehring1,3, Sara Lorkiewicz1, Kevin Zheng1, Kyan Younes1, Joseph Winer1, Elizabeth C. Mormino1, Tilman Schulte3,4, Kathleen L. Poston1,2
Institutions:
1Dept. of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA; 2Dept. of Neurosurgery, Stanford University School of Medicine, Stanford, CA; 3Biosciences Division, SRI International, Menlo Park, CA; 4Dept. of Psychology, Palo Alto University, Palo Alto, CA
First Author:
Co-Author(s):
Introduction:
Abnormal accumulation of specific proteins, such as β-amyloid (Aβ), alpha-synuclein (α-syn) and tau, are the hallmark of age-related neurodegenerative diseases. A mechanism thought to underlie many neurodegenerative disorders is dysfunction in protein clearance mechanisms (Sundaram et al., 2019; Tarasoff-Conway et al., 2015). The exact mechanisms of how solutes and macromolecules are removed from brain tissue remains controversial (Smith & Verkman, 2018); There are limited means of non-invasively evaluating the brain waste clearance system in vivo in human brains (Kaur et al., 2020). Recent advances in diffusion MRI, specifically the modeling of intravoxel incoherent motion (IVIM), could offer potential insights into glymphatic flow in the brain (Federau et al., 2014)[Fig.1A]. Critically, research establishing a relationship between IVIM metrics and glymphatic dysfunction related to protein accumulation is lacking. To address this, we conducted an innovative investigation using simultaneous PET-MR scans to evaluate how MR diffusion measures of glymphatic flow relate to Aβ levels obtained through PET. We hypothesized that IVIM metrics will correlate with protein accumulation indicated by Aβ.
Methods:
Data from 78 subjects (n=50 with positive Aβ status using a centiloid value cutoff of 18) with 3 Tesla diffusion MRI scans (b values: 0, 25, 50, 100, 250, 500, 1000 s/mm2) were processed using an in-house developed pipeline (Dimuthu et al., 2024.). We utilized the IAR_LU_biexp fitting model, part of the IVIM task force 2.4 (Fan et al., 2024), within our pipeline to calculate three key IVIM metrics: D (true diffusion), D* (a parameter accounting for perfusion), and f (perfusion fraction). T1-weighted images were utilized to parcellate the brain into 98 regions of interest (ROIs) employing a deep learning-based algorithm available in FreeSurfer (Billot et al., 2023). These ROIs were then used to mask the IVIM images, allowing the calculation of mean values for each ROI, resulting in a total of 294 features per subject. Additionally, we calculated 18F-florbetaben Aβ-PET centiloids, a measure of cortical Aβ burden. An initial random forest-based model was trained using leave-one-out cross validation to identify the most informative IVIM features for predicting the centiloid status. Selected important features were then evaluated using their correlation with centiloids. A second model was trained with only D features to compare with the previous model including D, D* and f to evaluate how perfusion related metrics contributed to the model performance.
Results:
The initial analysis of feature importance revealed that D in the precuneus was identified as the most significant predictor. Top 10 features included a mix of D, D*, and f from several cortical and subcortical areas, highlighting their relevance in the modeling process [Fig.1B]. Notably, the D measures from the right precuneus exhibited a moderate, yet a significant correlation with centiloid scores (R = 0.53, p*<0.05). Furthermore, the comparison of model performances through the receiver operating characteristic curve demonstrated a 9% improvement in predictive performance when incorporating the perfusion-related metrics D* and f into the model, as shown in Figure 2.
Conclusions:
The trained model identified D, D*, and f from several cortical and subcortical areas the most informative IVIM features when predicting the centiloid status. Notably, higher D values were significantly correlated with higher centiloids within the precuneus, one of the first brain regions affected in Alzheimer's disease (Palmqvist et al., 2017). Additionally, the improvement in model performance when including D* and f features demonstrated that, using perfusion related IVIM metrics are truly effective in detecting Aβ related abnormalities. This affiliation underscores the potential of IVIM metrics as valuable biomarkers for assessing dysfunction within the waste clearance system of the brain.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Methods Development
PET Modeling and Analysis
Keywords:
Cerebral Blood Flow
Computing
Degenerative Disease
Experimental Design
Machine Learning
MRI
Other - Brain Waste Clearance
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hemachandra, D., Peterson, E. (2024). IVIM_fit. Github. Retrieved December 17, 2024, from https://github.com/PostonLab/IVIM_fit
Fan, H., Mutsaerts, H. J. M. M., Anazodo, U., Arteaga, D., Baas, K. P. A., Buchanan, C., Camargo, A., Keil, V. C., Lin, Z., Lindner, T., Hirschler, L., Hu, J., Padrela, B. E., Taghvaei, M., Thomas, D. L., Dolui, S., & Petr, J. (2024). ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): ASL pipeline inventory. Magnetic Resonance in Medicine, 91(5), 1787–1802.
Federau, C., O’Brien, K., Meuli, R., Hagmann, P., & Maeder, P. (2014). Measuring brain perfusion with intravoxel incoherent motion (IVIM): initial clinical experience: Brain IVIM: Initial Clinical Experience. Journal of Magnetic Resonance Imaging, 39(3), 624–632.
Kaur, J., Davoodi-Bojd, E., Fahmy, L. M., Zhang, L., Ding, G., Hu, J., Zhang, Z., Chopp, M., & Jiang, Q. (2020). Magnetic resonance imaging and modeling of the glymphatic system. Diagnostics (Basel, Switzerland), 10(6), 344.
Palmqvist, S., Schöll, M., Strandberg, O., Mattsson, N., Stomrud, E., Zetterberg, H., Blennow, K., Landau, S., Jagust, W., & Hansson, O. (2017). Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nature Communications, 8(1), 1214.
Smith, A. J., & Verkman, A. S. (2018). The “glymphatic” mechanism for solute clearance in Alzheimer’s disease: game changer or unproven speculation? FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 32(2), 543–551.
Sundaram, S., Hughes, R. L., Peterson, E., Müller-Oehring, E. M., Brontë-Stewart, H. M., Poston, K. L., Faerman, A., Bhowmick, C., & Schulte, T. (2019). Establishing a framework for neuropathological correlates and glymphatic system functioning in Parkinson’s disease. Neuroscience and Biobehavioral Reviews, 103, 305–315.
Tarasoff-Conway, J. M., Carare, R. O., Osorio, R. S., Glodzik, L., Butler, T., Fieremans, E., Axel, L., Rusinek, H., Nicholson, C., Zlokovic, B. V., Frangione, B., Blennow, K., Ménard, J., Zetterberg, H., Wisniewski, T., & de Leon, M. J. (2015). Clearance systems in the brain-implications for Alzheimer disease. Nature Reviews