Scalable Modeling of Nonlinear Network Dynamics in Neurodegenerative Disease

TL;DR

This paper introduces COMIND, a scalable connectome-based model for simulating nonlinear neurodegenerative disease progression, effectively handling heterogeneity and large biomarker sets in neuroimaging data.

Contribution

The paper presents a novel, scalable model combining diffusion and logistic concepts with structural connectivity to improve neurodegenerative disease modeling.

Findings

Model guarantees monotonic disease trajectories.

Effective on simulated and real Parkinson's data.

Handles large biomarker sets without reduction.

Abstract

Mechanistic models of progressive neurodegeneration offer great potential utility for clinical use and novel treatment development. Toward this end, several connectome-informed models of neuroimaging biomarkers have been proposed. However, these models typically do not scale well beyond a small number of biomarkers due to heterogeneity in individual disease trajectories and a large number of parameters. To address this, we introduce the Connectome-based Monotonic Inference of Neurodegenerative Dynamics (COMIND). The model combines concepts from diffusion and logistic models with structural brain connectivity. This guarantees monotonic disease trajectories while maintaining a limited number of parameters to improve scalability. We evaluate our model on simulated data as well as on the Parkinson's Progressive Markers Initiative (PPMI) data. Our model generalizes to anatomical imaging representations from a standard brain atlas without the need to reduce biomarker number.