Biology subjects

Alexandersen, C. G.

Publications and source records attributed to Alexandersen, C. G..

5 recordsLinked to original sources

bioRxiv2026

Rise-and-fall dynamics reveal a molecular and cellular vulnerability axis in prion-like α-synuclein propagation

The spread of misfolded proteins through neuronal circuits is a defining feature of neurodegenerative disease, yet the dynamics underlying this process remain poorly understood. Most studies rely on sparsely sampled datasets that capture spatial patterns of pathology but not their temporal evolution. Here, we analyze longitudinal histopathology measurements of -synuclein pathology across hundreds of brain regions in a mouse model of Parkinsons disease. Network-based dynamical modeling shows that regional pathology does not simply accumulate but instead follows rise-and-fall trajectories across the brain. The inferred parameter landscape reveals a one-dimensional vulnerability axis along which regions with stronger rise-and-fall dynamics have elevated energy metabolism and greater monoaminergic neuronal composition. This vulnerability axis replicates in an independent histopathological dataset, indicating that its dominant transcriptomic structure is preserved. Together, these results suggest that regional vulnerability collapses onto a low-dimensional molecular and cellular axis defined by rise-and-fall dynamics.

neuroscience↗

bioRxiv2023

Pseudo-craniotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Brain tumors can induce pathological changes in neuronal dynamics both on a local and global level. Here, we use a whole-brain modeling approach to investigate these pathological alterations in neuronal activity. By fitting a Hopf whole-brain model to empirical functional connectivity, we demonstrate that phase correlations are largely determined by the ratio of interregional coupling strength and intraregional excitability. Furthermore, we observe considerable differences in interregional-versus-intraregional dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, we show that local tumor pathology induces shifts in the global brain dynamics by promoting the contribution of interregional interactions. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

neuroscience↗

bioRxiv2023

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimers disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feed-back loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimers disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

A mean-field to capture asynchronous irregular dynamics of conductance-based networks of adaptive quadratic integrate-and-fire neuron models

Mean-field models are a class of models used in computational neuroscience to study the behaviour of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behaviour of "mean field" variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semi-analytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

bioinformatics↗

bioRxiv2022

A mechanistic model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease

Alzheimers disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-{beta} and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-{beta} and tau affect neurons at the cellular scale. On a larger scale, Alzheimers 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-{beta} and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-{beta} 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-{beta} and tau protein.

neuroscience↗