Cognitive and behavioral deficits arising from neurodegeneration and traumatic brain injury: a model for the underlying role of focal axonal swellings in neuronal networks with plasticity

Abstract

Neurodegenerative diseases and traumatic brain injury, whose hallmark features are the presence of focal axonal swellings (FAS), are leading causes of cognitive dysfunction. By leveraging biophysical observations of FAS statistics, we develop a theoretical model of functional neural network activity driven by adaptive changes from plasticity. Based upon the FORCE model of Sussillo and Abbott [1], our innovations highlight the role of plasticity in overcoming injuries and degeneration of neurons in a network architecture. We provide a quantitative measure, on a network level, of cognitive deficits arising from injury. We demonstrate that plasticity is capable of overcoming mild injuries while failing to compensate for more severe injuries. Such injuries are characterized by their underlying effect on spike trains propagating through the neurons in a network architecture. Specifically, spike trains can be filtered in firing rate, or blocked under more severe FAS. The level of injury dictates the FORCE model's ability to produce a desired output functionality (and associated behavior) and allows for quantitative metrics for accessing cognitive and behavioral deficits. Thus a direct link between FAS in neural networks and compromised functional response can be established. The theoretical framework developed is a promising computational framework for providing a deeper understanding of the cognitive deficits arising in, for instance, Alzheimer's, Parkinson's, Multiple-Sclerosis, and TBI.

Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics

“…However, we focus here on a Hopfield associate memory model in order to illustrate the concepts of how an auxiliary network can be trained with the Hopfield network. Damage to axons and neuronal connections typically impairs the network's collective functionality after a critical threshold value [ 54 ]. In the worst-case scenarios, focal axonal swellings, axotomies, or cell death could block the information encoded in spike trains and effectively zero out weights in the connectivity matrix [ 23 – 25 ].…”

“…Both sources of dysfunction exhibit significant presence of focal axonal swellings [ 16 – 22 ]. Axonal injuries hinder the information encoded in spike trains [ 23 – 25 ], thus leading to potentially severe functional deficits [ 26 – 32 ]. Challenging our understanding of the impact of axonal swellings is our inability to access small-scale injuries with noninvasive methods, the overall complexity of neuronal pathologies, and our limited knowledge of how networks process biological signals [ 33 ].…”

Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases

“…Despite the insights gained with animal studies [8,73,92] and in vitro experiments [28,29,52,51], there are still many open questions and opportunities for translational studies [33]. In particular, there have been only a modest number of theoretical and numerical works regarding blast-induced shockwaves [69], pathological effects on neuronal signaling [43,45] or network dysfunction [74]. This work aims to contribute with the latter, linking commonly found axonal injuries to a network's impaired decision-making capabilities.…”

A Computational Model for Pain Processing in the Dorsal Horn Following Axonal Damage to Receptor Fibers

“…While there are multiple mechanisms associated with axonal damage (such as focal axonal swellings or demyelination), they invariably alter the usual transmission of spike trains along neuronal fibers. Maia et al [ 14 ] assembled a list of phenomenological input/output rules describing commonly-observed forms of spike train distortions, which led to several studies simulating the addition of an injured-neuron population to functional networks [ 18 , 19 , 20 , 21 , 22 , 23 ].…”