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

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. Correspondence to: Pedro Doria Maia, Department of Applied Mathematics, University of Washington, Seattle, WA, 98195-2420, USA, E-mail: pedro.doria. maia@gmail.com Received: March 09, 2016; Accepted: March 24, 2016; Published: March 28, 2016 Introduction Neural plasticity plays a fundamental role in learning, memory and other executive functions. Although aging correlates with a natural decline in such cognitive abilities [1], notable deficits are usually associated with degenerative diseases or traumatic brain injuries (TBI). At present, limitations in biophysical measurements and neural recordings make it extremely difficult to extract the underlying mechanisms responsible for dysfunctions in neural networks, especially when circuits display intrinsically complex behavior and functional activity. Typically, the output patterns of such networks are associated with stimulus-induced behavior, thus suggesting that the neuronal networks are critical in data acquisition, processing and decision-making. In this manuscript, we build upon a recent model of neural plasticity and learning by Sussillo and Abbott [2] that captures key aspects of adaptive functional circuits in the brain. Specifically, we combine state-of-the-art biophysical observations about the role of focal axonal swellings in neurodegenerative diseases [3-5] and traumatic brain injury (TBI) [6,7] with their known impact on spike train encoding [8-10]. In doing so, we can quantity the cognitive deficits and compromised functional activity of the brain and the impact on its associated control (behavioral) protocols. Such a data-driven computational study is a first step to understanding on a network level the deleterious impact, in a quantifiable manner, of neurodegeneration and TBI. A recent mathematical model of neuronal networks with feedback and control pioneered by Sussillo and Abbott [1], known as the FORCE model, has shown that even chaotic networks can be trained to produce a variety of output patterns, suggesting that synaptic plasticity may be more powerful than generally appreciated. The FORCE model thus provides a quantitative framework for understanding neural plasticity, allowing for robust encoding of given stereotyped input stimulus to prescribed output neural activity. Importantly, the FORCE model suggests an underlying biological mechanism by which collections of neurons can organize their behavior into target functions in order to enact sophisticated control protocols associated with behavior and/or functional activity. In living organisms, these target functions encode important biological functions such as motor actions [11] or sensory information [12,13]. In fact, simpler organisms like the nematode exhibit complex motor behaviors by combining a small number of body-shape modes [11], thus suggesting how target functions can be used for simple tasks like locomotion. In this study, we simulate networks with these key properties but in the presence of injurious effects arising from FAS that are believed to occur in many neural pathologies and TBI. Specifically, we combine biologically derived FAS statistics with their impact on spike train encoding [8-10] in order to characterize damaged neuronal networks. We also develop new metrics to track anomalies in the collective neural activity and failures in their overall ability to learn and reproduce target output functions. Rudy S (2016) 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 Volume 2(2): 114-121 J Integr Syst Neurosci, 2016 doi: 10.15761/JSIN.1000120 More broadly, this study addresses the critically important effects of cognitive and behavioral deficits arising from FAS. FAS is a hallmark feature of TBI which is annually responsible for millions of hospitalizations, with at least 1.7 million cases in the United States alone [14,15]. Reports estimate that 57 million people worldwide experienced some form of TBI [16]. It targeted around 15% of all veterans of the Iraq and Afghanistan wars, with blast injuries being the signature wound of these conflicts [15,16]. Numerous studies show that even mild concussions, if induced repeatedly, can lead to permanent brain damage; the issue is constantly debated in the sports media, but especially in football [17]. The pathophysiology of TBI is heterogeneous [18,19,16]. However, all severities of TBI trigger axonal damage widespread over a large number of neurons [20]. Injured axons are thus a diagnostic marker for cognitive and behavioral deficits [3,21], both in animals and humans [22-25]. In extreme cases, axons are sheared or disconnected, leading to cell death. But even in mild TBI, injured axons undergo changes culminating in FAS [6,7,25-28]. FAS can lead to dramatic changes in axon diameters, potentially interrupting axonal transport [29,30] and/or significantly impairing the underlying spike train propagation responsible for encoding information in neural activity [8-10,31]. Interestingly, many leading neurodegenerative diseases, which affects orders of magnitude more people than TBI, also display FAS as a hallmark feature. Indeed, modeling the effect of FAS in neuronal networks can also shine new light in neuropathologies where they are implicated, such as Alzheimer’s disease [4,5], Creutzfeldt-Jakob’s disease [32], HIV dementia [33], Multiple Sclerosis [34,35] and Parkinson’s disease [36]. For both neurodegeneration and TBI, FAS leads to compromised network functionality and control in the FORCE model. Thus, a drop in network performance can be interpreted as a proxy for cognitive or behavioral deficits, opening possibilities for novel diagnostic techniques in these deleterious and widespread diseases. Ultimately, we provide a first study linking connective plasticity and pathological developments after brain trauma or neurodegeneration. Materials and methods The modeling of neuronal networks is one of the most vibrant fields of computational neuroscience [37-39]. In our simulations we use the FORCE learning protocol [1]. The rationale is that before learning, both network activity and its output are chaotic. The network is then trained, i.e. synaptic weights are adjusted to match its output to a given target function. Sussillo and Abbott [1] define the training of a neural network as a process through which parameters (typically synaptic strengths) are modified on the basis of output errors until a desired response is produced. Figure 1 illustrates the training process. After training, the network activity produces the coherent pattern periodically without requiring any additional weighting modification. Our contribution is a comprehensive study of how the effects attributed to FAS jeopardize the network functionality and training of the network. The network learning deficits have a clear biophysical interpretation, and anomalies in the injured output can be described quantitatively. Specifically, the normed distance between the target function and output of the network can be thought of as a measure for cognitive and/or behavioral deficit. Thus a trained and uninjured network would be able to match the target function whereas an injured (neurodegenerated) network would not accurately produce the desired target function. This connection will be explored further in what follows. Training networks with FORCE Training neural networks usually consists in applying a sequence of modifications that gradually improve the output. In FORCE learning [1], errors are maintained at small levels throughout the simulation. During the training period the learning scheme, rather than working to lower error, lowers the amount of training needed at each step. By the end of the training period, output weights have converged towards a value that recreates the periodic target function without further modification. During the testing phase of the FORCE learning simulation the target function is repeated periodically without any changes to the output weights. Networks initially exhibiting chaotic dynamics have been shown to achieve faster learning agreeing with several results which have shown that neural circuits exhibit chaotic dynamics for spontaneous spiking activity [40-43] FORCE learning has been shown to be successful in several contexts. Most notable, it allows an artificial neural network to reconstruct a periodic target function