Sheheitli, H. | Search bioRxiv

bioRxiv2022

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

neuroscience↗

bioRxiv2022

The structured flow on the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to brains health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brains resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding. In addition, it shifts the focus from the single recordings towards brains capacity to generate certain dynamics characteristic of health and pathology.

neuroscience↗

bioRxiv2019

A mathematical model of ephaptic interactions in neuronal fiber pathways: could there be more than transmission along the tracts?

In the past several decades, there has been numerous experimental and modeling efforts to study ephaptic interactions in neuronal systems. While studies on the matter have looked at either axons of the peripheral nervous system or cortical neuronal structures, no attention has be given to the possibility of ephaptic interactions in the white matter tracts of the brain. Inspired by the highly organized and tightly packed geometry of axons in neuronal fiber pathways, we aim to theoretically investigate the potential effects of ephaptic interactions along these structures that are resilient to experimental probing. For that end, we use axonal cable theory to derive a minimal model of a sheet of N ephaptically coupled axons. We numerically solve the equations and explore the dynamics of the system as the ephaptic coupling parameter is varied. We demonstrate that ephaptic interactions can lead to local phase locking between impulses traveling along adjacent axons. As ephaptic coupling is increased, traveling impulses trigger new impulses along adjacent axons resulting in finite size traveling fronts. For strong enough coupling, impulses propagate laterally and backwards, resulting in complex spatio-temporal patterns. While it is common for large scale brain network models to assume the role of brain fiber pathways to be that of mere transmission of signals between different brain regions, our work calls for a closer re-examination of the validity of such a view. The results suggest that in the presence of significant ephaptic interactions the brain fiber tracts can act as a dynamic active medium.\n\nAuthor summaryStarting from local circuit theory and the Fitzhugh-Nagumo cable model of an axon, we derive a system of nonlinear coupled partial differential equations (PDEs) to model a sheet of N ephaptically coupled axons. We also put forward a continuous limit approximation that transforms the model into a field equation in the form of a two-dimensional PDE that allows for the extension of the model to a 3D domain. We numerically solve the equations and explore the dynamic responses as the ephaptic coupling strength is varied. We observe that ephaptic interaction allows for phase locking of adjacent impulses and coordination of subthreshold dynamics. In addition, when strong enough, ephaptic interaction can lead to the generation of new impulses along the axons as well as lateral and backward propagation in the form of traveling fronts and complex spatio-temporal patterns. The transition between different dynamic regimes happens abruptly at critical values of the parameter. We also compare the dynamics of the two models and find good qualitative correspondence in certain parameter regimes. The results put into question the validity of assuming the role of fiber pathways to be that of mere interneuronal transmission and calls for further investigation of the matter.

neuroscience↗