Gennari, John HCrane, Paul KMadan, Raghav2026-04-202026-04-202026-04-202026Madan_washington_0250E_29037.pdfhttps://hdl.handle.net/1773/55423Thesis (Ph.D.)--University of Washington, 2026Alzheimer’s disease (AD) is characterized by the progressive accumulation of misfolded proteins, primarily tau neurofibrillary tangles, amyloid-β plaques, and TDP-43 inclusions, across various brain networks. Despite a century of histopathological insight, quantitative understanding of how these pathologies spatially unfold remains limited. Classical frameworks such as Braak and Thal staging distilled sparse regional observations into ordinal categories, yielding reproducible heuristics for disease progression but obscuring fine-scale gradients and inter-individual heterogeneity. Modern imaging and network-based diffusion models have extended these ideas to the living brain; however, they remain constrained by coarse parcellations, strong mechanistic assumptions, and the lack of direct calibration against histological ground truth. The field, therefore, lacks a rigorous, data-driven framework that can translate quantitative neuropathology into spatially continuous, anatomically interpretable maps across the brain. To address this gap, I developed NeuroPathPredict (NPP), a modular, open-source system that integrates quantitative histopathology with high-resolution neuroimaging and spatial statistics. NPP comprises three primary pipelines. (1) QNPtoVox transforms Halo-derived, tile-wise tau burden measurements into voxel-level maps co-registered to the MNI ICBM 2009b template via ex vivo MRI, preserving anatomical orientation and enabling cross-participant comparability. (2) The Integrated-Brain Information System (I-BIS) creates a multilayer “brain GIS” by unifying various structural, functional, and vascular atlases into a shared 0.5 mm volumetric grid. It produces thousands of biologically relevant covariates, such as distances, densities, and neighborhood features, which help contextualize each voxel regarding white-matter tracts, networks, and tissue types. (3) The NPP modeling framework combines these data using universal kriging with external drift, a geostatistical method that links anatomy-informed predictors with residual spatial autocorrelation to infer continuous tau fields from sparse observations, providing both predictions and spatially explicit results uncertainty. Applied to ten donors from the Adult Changes in Thought (ACT) study, NPP demonstrates that anatomically enriched models outperform non-spatial baselines and recover mesoscale gradients of tau burden consistent with known vulnerability patterns along association tracts and functional networks. The results show that spatial autocorrelation persists beyond measured covariates, validating the use of kriging in brain space and underscoring the value of integrated anatomical context. More broadly, NPP establishes a reproducible computational framework that transforms post-mortem histology into standardized, voxel-wise maps suitable for cross-modal validation with MRI and PET, for testing theories of selective vulnerability, and for modeling the spatial interplay of multiple pathologies. By integrating digital pathology, neuroimaging, and spatial statistics, this work enhances the ability to reconstruct, predict, and ultimately comprehend the brain-wide spatial dynamics of neurodegeneration.application/pdfen-USCC BYAlzheimer’sdigital histopathologyMRIspatial modelingTauuniversal krigingNeurosciencesStatisticsBioinformaticsTo Be AssignedThesis