Mapping the Impact of Amyloid, Tau, and APOE Variants on White Matter Tracts in Alzheimer’s Disease
Poster No:
164
Submission Type:
Abstract Submission
Authors:
Bramsh Chandio1, Julio Villalon-Reina1, Talia Nir1, Sophia Thomopoulos2, Yixue Feng1, Sebastian Benavidez1, Robert Reid3, Clifford Jack3, Michael Weiner4, Neda Jahanshad5, Jaroslaw Harezlak6, Eleftherios Garyfallidis7, Paul Thompson2
Institutions:
1University of Southern California, Marina Del Rey, CA, 2University of Southern California, Los Angeles, CA, 3Mayo Clinic, Rochester, MN, 4University of California, San Francisco, San Francisco, CA, 5University of Southern California,, Marina del Rey, CA, 6Indiana University Bloomington, Bloomington, IN, 7Indiana University, Bloomington, IN
First Author:
Co-Author(s):
Introduction:
Amyloid-beta (Aβ) plaques and tau neurofibrillary tangles are hallmark pathologies of Alzheimer's disease (AD), driving neurodegeneration and cognitive decline[8]. These protein accumulations disrupt neuronal integrity, impacting gray and white matter structures. White matter tracts, essential for efficient brain communication, are particularly vulnerable, with disruptions contributing to cognitive impairment in AD. The APOE gene, the strongest common genetic risk factor for late-onset AD, further modulates disease progression through its ε4, ε2, and ε3 alleles, which influence amyloid-beta deposition, tau aggregation, and neuroinflammation[7]. The ε4 allele accelerates neurodegeneration, while ε2 is protective, and ε3 is the neutral reference. We investigate the impact of amyloid, tau, and APOE alleles on white matter tracts using Bundle Analytics (BUAN)[1], a tractometry framework enabling precise, localized assessments of microstructural abnormalities along the tracts.
Methods:
We analyzed 3D brain diffusion MRI (dMRI) data from 730 participants in the Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI3)[10], comprising individuals aged 55-95, 349M/381F, 214 with mild cognitive impairment, 69 with dementia, and 447 cognitively healthy controls, 245 Aβ+, 351 Aβ-, 168 tau+, and 401 tau-, and 310 ε3ε3, 48 ε3ε2/ε2ε2, 358 ε3ε3/ε3ε2/ε2ε2, 192 ε3ε4/ε4ε4, and 203 ε3ε4/ε2ε4/ε4ε4. Aβ status (+ or -) was determined by either mean 18F-florbetapir (Aβ+ defined as >1.11)[5] or florbetaben (Aβ+ defined as >1.20)[6] PET cortical SUVR uptake, normalized using a whole cerebellum reference region. Tau+ was defined as a tau SUVR > 1.23. dMRI were pre-processed using the ADNI3 dMRI protocol[9]. We applied RUMBA[2] and probabilistic particle filtering tracking[3] to generate whole-brain tractograms using DIPY[4]. We applied BUAN tractometry[1] to extract 34 bundles per subject and quantify microstructural properties along individual white matter tracts, using diffusion tensor imaging (DTI) metrics: mean, axial, radial diffusivity (MD, AxD, RD), and fractional anisotropy (FA). Statistical analyses employed linear mixed models to assess associations between each biomarker and tract microstructure, with age and sex modeled as fixed effects and scanner and subject effects as random terms. False discovery rate correction was applied to control for multiple comparisons across white matter tracts.
Results:
Figure 1 reveals localized amyloid- and tau-specific alterations in the white matter microstructure of the individual tracts. Amyloid positivity was linked to significant increases in diffusivity metrics, particularly in regions such as the cingulum, corpus callosum, and extreme capsule, suggesting amyloid's impact on myelin and axonal damage. Tau pathology exhibited stronger associations with MD near cortical areas, especially in the frontopontine tract, inferior longitudinal fasciculus, and optic radiation, supporting the idea that tau spreads through cortical pathways, disrupting cognitive networks. The high sensitivity of MD relative to other diffusion metrics highlights the potential of dMRI in early AD detection.
Figure 2 visualizes the localized effects of ε4 and ε2. APOE genotype associations show the impact of genetic risk factors on white matter vulnerability: ε4 carriers exhibit more pronounced microstructural alteration in tracts, including the corticospinal and inferior longitudinal fasciculus, than ε2 carriers. This pattern is consistent with APOE ε4's known impact on amyloid clearance and inflammatory processes. These findings emphasize that genetic factors affect white matter microstructure differentially in pathways critical to cognition.
Figure 2 visualizes the localized effects of ε4 and ε2. APOE genotype associations show the impact of genetic risk factors on white matter vulnerability: ε4 carriers exhibit more pronounced microstructural alteration in tracts, including the corticospinal and inferior longitudinal fasciculus, than ε2 carriers. This pattern is consistent with APOE ε4's known impact on amyloid clearance and inflammatory processes. These findings emphasize that genetic factors affect white matter microstructure differentially in pathways critical to cognition.
Conclusions:
BUAN tractometry and diffusion MRI are powerful tools for studying how amyloid, tau, and APOE genotype variations affect white matter tracts in AD, offering insights into disease mechanisms, early detection, and personalized therapies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Computational Neuroscience
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Alzheimer’s Disease, Amyloid, Tau, APOE Gene, Tractometry, BUAN
1|2Indicates the priority used for review
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[2] Canales-Rodríguez, E. J., et al. (2015). Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization. PloS ONE, 10(10), e0138910.
[3] Girard, G., et al. (2014). Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage, 98, 266-278.
[4] Garyfallidis, E., et al. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 8, 8.
[5] Landau, S. M., et al. (2013). Amyloid-β imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods. Journal of Nuclear Medicine, 54(1), 70-77.
[6] Landau, S. M., et al. (2012). Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Annals of Neurology, 72(4), 578-586.
[7] Raulin, A. C., et al. (2022). ApoE in Alzheimer’s disease: pathophysiology and therapeutic strategies. Molecular Neurodegeneration, 17(1), 72.
[8] Sepulcre, J., et al. (2018). Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nature Medicine, 24(12), 1910-1918.
[9] Thomopoulos, S. I.,et al. (2021, December). Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches. In 17th International Symposium on Medical Information Processing and Analysis (Vol. 12088, pp. 166-179). SPIE.
[10] Zavaliangos-Petropulu, A., et al. (2019). Diffusion MRI indices and their relation to cognitive impairment in brain aging: the updated multi-protocol approach in ADNI3. Frontiers in Neuroinformatics, 13, 2.