Free water mapping predicts amyloid-β and cognition in older adults without dementia
Poster No:
205
Submission Type:
Abstract Submission
Authors:
Andrew Bender1, Pavithran Pattiam Giriprakash1, Zhengshi Yang1, Dietmar Cordes1,2, Jessica Caldwell1
Institutions:
1Cleveland Clinic, Las Vegas, NV, 2University of Colorado, Boulder, Boulder, CO
First Author:
Co-Author(s):
Introduction:
Alzheimer's disease (AD) is characterized by neuropathological features, including amyloid-β (Aβ) depositions. AD neuropathological processes can affect tissue microstructure, leading to increased intra-voxel proportions of extracellular cerebral spinal fluid (CSF), or 'free water.' Diffusion-weighted magnetic resonance imaging (dMRI) methods for quantifying free water are sensitive to neuropathological burden and cognitive performance (Jack et al., 2024; Maillard et al., 2019). Common dMRI free water mapping methods include dual-tensor free water elimination (DT-FWE; Pasternak et al., 2009) and neurite orientation dispersion diffusion imaging (NODDI; Zhang et al., 2012). In addition, multi-shell multi-tissue (MSMT) constrained spherical deconvolution (CSD; Dhollander et al., 2017, 2019; Jeurissen et al., 2014) also quantifies CSF as isotropic diffusion, but has yet to be validated against established free water measures. We tested the sensitivity of three dMRI free water mapping methods to Aβ levels and cognitive performance in a clinical aging cohort.
Methods:
Participants (N=122) were enrolled in the Center for Neurodegeneration and Translational Neuroscience and clinically characterized as cognitively unimpaired (CU; n=58) or mild cognitive impairment (MCI; n=64). All had complete data from florbetapir F18 amyloid PET, dMRI scanning, and cognitive tests of visual reproduction, executive function, working memory, and semantic fluency. MRI acquisition: Siemens Skyra 3T: slice thickness=1.5 mm, TR=5218ms, TE=100ms, b-values=0, 500, 1000, 2500s/mm2, diffusion directions=79 per shell. Image processing: We used ADNI PET processing methods to calculate standardized uptake value ratio (SUVR) measures of Aβ burden (Landau et al., 2012), rescaled to centiloids. DMRI preprocessing in FSL 6.0.3 and MRtrix3 (Tournier et al., 2019) corrected for noise, Gibbs ringing, imaging distortion, and eddy currents. Processing followed the MSMT-CSD fixel-based analysis pipeline to estimate multi-tissue basis functions, fit fiber orientation distributions (FOD), build a FOD template, and transform native, normalized FOD and CSF images to the template. We also fit 1) the NODDI model to derive the isotropic volume fraction (FISO), and 2) DT-FWE model (b=1000 s/mm2) to estimate free water fraction (FWF), and spatially transformed FISO and FWF images to the study template. Data analysis entailed voxel-based analyses (VBA) general linear models in mrclusterstats to assess total centiloid level as predictor of whole-brain differences for FWF, FISO, and MSMT-CSF. All models included covariates age, sex, handedness, and head size; all results used nonparametric correction for familywise error rate.
Results:
Higher centiloid level predicted greater FWF, FISO and MSMT-CSF (Fig. 1; all r≥0.51, p<.001). All three measures showed overlapping effects of Aβ in medial temporal lobe regions including bilateral entorhinal cortices, hippocampi, and amygdalae, left fornix crus and inferior temporo-occipital regions; however, FWF and MSMT-CSF effects were more widespread than FISO (Table 1). Aβ also positively predicted FWF and MSMF-CSF in bilateral basal forebrain, fornix body, retrosplenial cortex, posterior cingulum bundle, splenium of corpus callosum, stria terminalis, and precuneus. Only FWF was positively associated with Aβ in anterior thalamic radiation, and caudate nucleus. All three measures were negatively correlated with performance on neuropsychological tests of visual reproduction, working memory, category fluency, speeded processing, and executive function (Table 2).
·Figure 1
·Table 1 & 2
Conclusions:
In older adults without dementia, elevated Aβ predicts higher extracellular CSF in brain regions associated with AD neuropathology. Multiple free water models show similar associations to centiloid level and similar spatial patterns, but FISO was less sensitive than FWF and MSMT-CSF. These findings support the comparable validity of MSMT-CSF for dMRI free water mapping.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
ADULTS
Aging
Cortex
Modeling
Neurological
Sub-Cortical
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Alzheimer's disease
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dhollander, T., Raffelt, D., & Connelly, A. (2017). Towards interpretation of 3-tissue constrained spherical deconvolution results in pathology. In Proceedings of the International Society for Magnetic Resonance in Medicine, Vol. 25, p. 1815.
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Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A., & Sijbers, J. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 103, 411-426.
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Maillard, P., Fletcher, E., Singh, B., Martinez, O., Johnson, D. K., Olichney, J. M., Farias, S. T., & DeCarli, C. (2019). Cerebral white matter free water: A sensitive biomarker of cognition and function. Neurology, 92(19), e2221-e2231.
Pasternak, O., Sochen, N., Gur, Y., Intrator, N., & Assaf, Y. (2009). Free water elimination and mapping from diffusion MRI. Magnetic Resonance in Medicine, 62(3), 717-730.
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000-1016.