Biology subjects

Chaggar, P.

Publications and source records attributed to Chaggar, P..

5 recordsLinked to original sources

bioRxiv2026

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

neuroscience↗

bioRxiv2025

Patient-specific functional brain architecture explains cortical patterns of tau PET in Alzheimer's disease

The spatial distribution of tau pathology, a key correlate of neurodegeneration and cognitive decline in Alzheimer's disease (AD), varies markedly across individuals. While tau is thought to spread along brain networks, the role of inter-individual variability in accounting for these patterns remains underexplored. Using resting-state fMRI and tau-PET from 805 BioFINDER participants across the AD continuum, with replication in ADNI (n=361) and A4 (n=336), we studied whether subject-specific functional connectivity (FC) profiles enhance the characterization of tau deposition patterns. A hybrid approach integrating individual and group-average FC explained individual tau-PET topographies better than either FC representation alone, particularly in symptomatic individuals and at finer spatial resolutions. Hybrid FC also better captured individual tau topographies than canonical tau-PET maps derived from cohort-level data. These effects were specific to tau and not similarly observed for {beta}-amyloid, and the explanatory advantage of FC-based models increased with spatial granularity. Furthermore, baseline hybrid FC explained follow-up tau-PET topography better than template FC, suggesting that individualized baseline connectivity contains information about future tau-PET progression. The main FC model-comparison findings replicated in ADNI and A4. Collectively, these findings show that individual functional brain architecture is associated with heterogeneity in tau-PET topography. While not establishing a causal propagation mechanism, our findings are consistent with network-spread models. This work advances the methodological characterization of tau-PET heterogeneity in AD and highlights functional connectivity as a potentially informative marker of individual tau-PET trajectories.

neuroscience↗

bioRxiv2025

Neuronal activity and amyloid-beta cause tau seeding in the entorhinal cortex in Alzheimer's disease

The entorhinal cortex is the earliest site of tau pathology in both Alzheimers disease and primary age-related tauopathy, yet the mechanisms underlying this selective vulnerability remain poorly understood. Here, we use a computational model integrating neuronal activity and amyloid-{beta} deposition with interneuronal tau transport to predict regional susceptibility to tau seeding. Using fluorodeoxyglucose PET as a measure of neuronal activity, we show that brain-wide activity patterns drive tau accumulation in the medial temporal lobe, independent of amyloid status. Incorporating amyloid PET, we further show that amyloid-{beta} selectively amplifies tau seeding in the entorhinal cortex, aligning with its early involvement in Alzheimers disease. These predictions are supported by cross-subject correlation analysis, which reveals a significant association between model-derived seeding concentrations and empirical tau deposition. Our findings suggest that neuronal activity patterns shape the early landscape of tau pathology, while amyloid-{beta} deposition creates a unique vulnerability in the entorhinal cortex, potentially triggering the pathological cascade that defines Alzheimers disease.

neuroscience↗

bioRxiv2023

Personalised Regional Modelling Predicts Tau Progression in the Human Brain

Aggregation of the hyperphosphorylated tau protein is a central driver of Alzheimers disease, and its accumulation exhibits a rich spatio-temporal pattern that unfolds during the course of the disease, sequentially progressing through the brain across axonal connections. It is unclear how this spatio-temporal process is orchestrated - namely, to what extent the spread of pathologic tau is governed by transport between brain regions, local production or both. To address this, we develop a mechanistic model from tau PET data to describe tau dynamics along the Alzheimers disease timeline. Our analysis reveals longitudinal changes in production and transport dynamics on two independent cohorts, with subjects in early stage of the disease exhibiting transport-dominated spread, consistent with an initial spread of pathologic tau seeds, and subjects in late stage disease (Braak stage 3/4 onwards) characterised primarily by local production of tau. Furthermore, we demonstrate that the model can accurately predict subject-specific longitudinal tau accumulation at a regional level, potentially providing a new clinical tool to monitor and classify patient disease progression. TeaserA mechanistic model reveals tau protein dynamics in Alzheimers, showing stage-specific shifts in transport and local production.

neuroscience↗

bioRxiv2021

Predicting brain atrophy from tau pathology: A summary of clinical findings and their translation into personalized models

For more than 25 years, the amyloid hypothesis-the paradigm that amyloid is the primary cause of Alzheimers disease-has dominated the Alzheimers community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimers disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the the Alzheimers Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimers patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis.

biophysics↗