A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease

Abstract

Alzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid- β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid- β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer’s patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid- β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid- β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid- β and tau protein.

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading

“…Another interesting scenario involves several heterodimer species on the same network. These species may interact [35] and even affect the underlying oscillator dynamics differently [41]. The latter case could be realized by having two heterodimer species evolving, where one speeds up (negative c ) and the other slows down (positive c ) the intrinsic frequencies of the oscillators.…”

“…In these models, the instantaneous oscillator frequencies are commonly interpreted as neuronal firing rates, which is a common metric for neuronal activity. Only recently have the two aspects of slow disease progression and fast neural dynamics been captured in a single modeling framework; see for example [40, 41].…”

A multiscale closed-loop neurotoxicity model of Alzheimer’s disease progression explains functional connectivity alterations

“…The proteinopathy dynamics for i ∈ [1, N] were adapted from Alexandersen et al (2023) to include the effect of hyperactivity on the enhanced production of Aβ (Cirrito et al, 2005; Yamamoto et al, 2015; Kamenetz et al, 2003; Busche and Hyman, 2020; Stargardt et al, 2015) and on the biased prion-like propagation of TAUt to hyperactive regions (Rodriguez et al, 2020); see Table 2 for a description of parameters): Where prod, clear and trans stand for rates of protein production, clearance, and transformation from a healthy isoform to a toxic one. ρ stands for a diffusion constant, and L ij for the laplacian diffusion term: where δ ij stands for the kronecher delta (i.e.…”

“…The impact of protein concentration on the brain network model is established through two sets of transfer functions. First, a set of damage functions that are used to translate the concentration of toxic proteins into damage variables (Alexandersen et al, 2023): Second, a set of equations that translate the damage just mentioned into NMM parameter changes. We adapted these equations to the JR NMM.…”

“…In recent years, significant computational modelling efforts have employed brain network models (BNMs) to investigate neural activity and its relationship to several aspects of the disease (Stefanovski et al, 2021, 2019; Alexandersen et al, 2023; van Nifterick et al, 2022; Ranasinghe et al, 2022). These models reproduce neural activity by using sets of second-order differential equations known as neural mass models (NMMs) that are interconnected through empirically-derived structural connectivity (SC) networks based on tractography.…”