Accounting for systematic spatiotemporal variation improves connectome‐based models of tau spreading in human Alzheimer's disease: Person‐centered prediction of tau spreading in Alzheimer's disease and related disorders. (7th December 2020)

• Record Type: Journal Article
• Authors: Vogel, Jacob W.; Young, Alexandra L.; Oxtoby, Neil P.; Smith, Ruben; Ossenkoppele, Rik; Aksman, Leon M; Strandberg, Olof; La Joie, Renaud; Grothe, Michel J.; Rabinovici, Gil D.; Alexander, Daniel C.; Evans, Alan C.; Hansson, Oskar
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.040586
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Studies in cells, animals and humans have each provided evidence that, in an Alzheimer's disease (AD) context, tau may be spreading through the brain transneuronally. This information may be useful for modeling the spread of tau in humans. However, such models are confounded by the substantial heterogeneity in spreading patterns observed in AD. In this talk, I will discuss i) application of an epidemic spreading model (ESM) to tau‐PET data to model the spread of tau through the human connectome; ii) application of a spatiotemporal subtyping algorithm to identify separable tau‐spreading patterns; 3) how accounting for these heterogeneous patterns can improve connectome‐based models. Method: The ESM simulates diffusion of an agent through a system of connected brain regions measured with diffusion tractography. We compare simulations across 312 individuals to observed tau‐PET data. We next use the Subtype and Staging Inference (SuStaIn), an algorithm combining disease progression models with clustering, to identify spatiotemporal subtypes of tau spreading across 1143 individuals. We describe demographic, cognitive and genetic associations with each subtype, and evaluate their stability over time and across different radiotracers. Finally, we apply the ESM separately to each AD subtype to assess whether overall model accuracy is improved by accounting for individual subtype. Result: The best‐fitting ESM used the entorhinal cortex as the model epicenter, and explained 70% of the variance in the overall tau‐PET pattern across subjects. SuStaIn identified four spatiotemporal subtypes with differing tau‐PET patterns and phenotypic profiles: Limbic‐predominant, limbic‐sparing, posterior, and lateral‐temporal. These same four subtypes were observed in a separate cohort using a different radiotracer (similarity 0.7‐0.9). 86% of subjects exhibited the same subtype at follow‐up. The ESM selected a different epicenter for each subtype, and accounting for subtype‐specific variation resulted in a 17% improvement in model performance. Conclusion: Connectome‐based models explain the majority of spatial variation in tau‐PET patterns. However, a one‐size‐fits‐all approach fails to account for heterogeneity of tau‐spreading patterns. Four stable phenotypes were observed, which may be characterized by vulnerability of different cortico‐limbic networks. Accounting for this heterogeneity will be necessary for any predictive modeling of tau spreading moving forward.