Biophysical Modeling of Alzheimer's Disease
Poster No:
240
Submission Type:
Abstract Submission
Authors:
Robin Sandell1, Daren Ma2, Justin Torok3, Srikantan Nagarajan1, Kamalini Ranasinghe1, Ashish Raj2
Institutions:
1University of California, San Francisco, San Francisco, CA, 2University of California San Francisco, San Francisco, CA, 3UCSF, San Francisco, CA
First Author:
Co-Author(s):
Introduction:
Alzheimer's Disease (AD) is characterized by tau protein neurofibrillary tangles which co-locate with atrophy and cognitive decline as they spread on the white matter tracts of the brain. Understanding individuals' spatiotemporal tau spread is critical for developing precision tau-targeting treatments. The Braak stages accurately capture population level spread patterns but not individual variability. Two previous approaches to study tau progression include statistical Event Based Models (EBMs) and biophysical Network Diffusion Models (NDMs). EBMs assign subjects to stages on a disease continuum, thus enabling a longitudinal understanding of disease progression from cross-sectional data; however, they lack mechanistic insight. Network Diffusion Models (NDM) capture underlying mechanisms of pathology but currently lack the longitudinal data needed to fit parameters. In this study we leverage the strengths of both approaches to create predictive individualized models of tau spread patterns.
Methods:
The study analyzed 650 subjects (64 AD, 196 MCI, 390 controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Tau-PET images were normalized by cerebellar uptake and averaged within Desikan-Killany atlas regions. Structural connectivity networks were constructed using dMRI data from 418 Human Connectome Project subjects. An event-based model was used to statistically stage subjects based on their MRI volumes, tau-PET, and cognitive scores. We interpolated longitudinal tau trajectories from subjects' stage assignments and tau maps. The extended NDM developed by our lab maps tau's diffusion and growth on the structural connectome, mediated by two rate parameters and initiated with tau's origin, the initial seed vector. We tried optimizing eNDM with three different techniques: optimizing parameters and seed vector to fit cohort-level EBM-derived tau trajectories, optimizing individual rate parameters, and optimizing individual seed vectors.
·Project Flow Chart
Results:
The EBM successfully staged subjects across 16 stages, showing clear diagnostic category separation. Network diffusion model optimization with individualized seeds (mean R=0.85; AIC = 9,032) far outperformed the other two techniques (mean R=0.52, 0.54; AIC= 123,535, 153, 290) and benchmarks from prior studies (Vogel, 2021). The model's predictions were successfully validated against longitudinal data from 297 subjects (mean R=0.81). Further analysis of subjects' tau trajectories showed that variation across subjects decreases over time in both model prediction and empirical data, converging from diverse seed patterns to common distributions as AD progresses. A k-means clustering analysis also identified two distinct tau seeding patterns: entorhinal-dominant (typical AD) and diffuse temporal.
·Results Summary
Conclusions:
Our novel hybrid approach successfully combines statistical and biophysical models to enable individualized tau spread prediction. The higher variation of tau patterning across subjects at disease onset contradicts the current "center-out" hypothesis of tau spread and indicates that AD heterogeneity may originate at tau's origin instead of spread dynamics. This implies early interventions should be tailored to unique seed patterns rather than uniformly targeting tau spread mechanisms. The identification of entorhinal-dominant and diffuse seeding archetypes demonstrates the utility of our method to uncover such patterns. Our study improves upon previous work by lending biophysical relevance to purely statistical approaches and enabling individual rather than group-level analysis. Our novel method could be applied to any neurodegenerative disease charactered by protein spread, opening new avenues for exploring heterogeneity across subjects and guiding personalized treatment strategies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
PET Modeling and Analysis
Novel Imaging Acquisition Methods:
PET
Keywords:
Aging
Computational Neuroscience
Data analysis
Degenerative Disease
Modeling
Open Data
Positron Emission Tomography (PET)
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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