Plasticity impairment alters community structure but permits successful pattern separation in a hippocampal network model.

Patients who suffer from traumatic brain injury (TBI) often complain of learning and memory problems. Their symptoms are principally mediated by the hippocampus and the ability to adapt to stimulus, also known as neural plasticity. Therefore, one plausible injury mechanism is plasticity impairment, which currently lacks comprehensive investigation across TBI research. For these studies, we used a computational network model of the hippocampus that includes the dentate gyrus, CA3, and CA1 with neuron-scale resolution. We simulated mild injury through weakened spike-timing-dependent plasticity (STDP), which modulates synaptic weights according to causal spike timing. In preliminary work, we found functional deficits consisting of decreased firing rate and broadband power in areas CA3 and CA1 after STDP impairment. To address structural changes with these studies, we applied modularity analysis to evaluate how STDP impairment modifies community structure in the hippocampal network. We also studied the emergent function of network-based learning and found that impaired networks could acquire conditioned responses after training, but the magnitude of the response was significantly lower. Furthermore, we examined pattern separation, a prerequisite of learning, by entraining two overlapping patterns. Contrary to our initial hypothesis, impaired networks did not exhibit deficits in pattern separation with either population- or rate-based coding. Collectively, these results demonstrate how a mechanism of injury that operates at the synapse regulates circuit function.

Plasticity impairment exposes CA3 vulnerability in a hippocampal network model of mild traumatic brain injury.

Proper function of the hippocampus is critical for executing cognitive tasks such as learning and memory. Traumatic brain injury (TBI) and other neurological disorders are commonly associated with cognitive deficits and hippocampal dysfunction. Although there are many existing models of individual subregions of the hippocampus, few models attempt to integrate the primary areas into one system. In this work, we developed a computational model of the hippocampus, including the dentate gyrus, CA3, and CA1. The subregions are represented as an interconnected neuronal network, incorporating well-characterized ex vivo slice electrophysiology into the functional neuron models and well-documented anatomical connections into the network structure. In addition, since plasticity is foundational to the role of the hippocampus in learning and memory as well as necessary for studying adaptation to injury, we implemented spike-timing-dependent plasticity among the synaptic connections. Our model mimics key features of hippocampal activity, including signal frequencies in the theta and gamma bands and phase-amplitude coupling in area CA1. We also studied the effects of spike-timing-dependent plasticity impairment, a potential consequence of TBI, in our model and found that impairment decreases broadband power in CA3 and CA1 and reduces phase coherence between these two subregions, yet phase-amplitude coupling in CA1 remains intact. Altogether, our work demonstrates characteristic hippocampal activity with a scaled network model of spiking neurons and reveals the sensitive balance of plasticity mechanisms in the circuit through one manifestation of mild traumatic injury.

NMDA Receptor Alterations After Mild Traumatic Brain Injury Induce Deficits in Memory Acquisition and Recall.

Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disruptions on local circuitry after mTBI are poorly understood. Our group recently reported how mTBI in neuronal networks affects the functional wiring of neural circuits and how neuronal inactivation influences the synchrony of coupled microcircuits. Here, we utilized a computational neural network model to investigate the circuit-level effects of N-methyl D-aspartate receptor dysfunction. The initial increase in activity in injured neurons spreads to downstream neurons, but this increase was partially reduced by restructuring the network with spike-timing-dependent plasticity. As a model of network-based learning, we also investigated how injury alters pattern acquisition, recall, and maintenance of a conditioned response to stimulus. Although pattern acquisition and maintenance were impaired in injured networks, the greatest deficits arose in recall of previously trained patterns. These results demonstrate how one specific mechanism of cellular-level damage in mTBI affects the overall function of a neural network and point to the importance of reversing cellular-level changes to recover important properties of learning and memory in a microcircuit.

Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks.

Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

Neuronal Degeneration Impairs Rhythms Between Connected Microcircuits.

Synchronization of neural activity across brain regions is critical to processes that include perception, learning, and memory. After traumatic brain injury (TBI), neuronal degeneration is one possible effect and can alter communication between neural circuits. Consequently, synchronization between neurons may change and can contribute to both lasting changes in functional brain networks and cognitive impairment in patients. However, fundamental principles relating exactly how TBI at the cellular scale affects synchronization of mesoscale circuits are not well understood. In this work, we use computational networks of Izhikevich integrate-and-fire neurons to study synchronized, oscillatory activity between clusters of neurons, which also adapt according to spike-timing-dependent plasticity (STDP). We study how the connections within and between these neuronal clusters change as unidirectional connections form between the two neuronal populations. In turn, we examine how neuronal deletion, intended to mimic the temporary or permanent loss of neurons in the mesoscale circuit, affects these dynamics. We determine synchronization of two neuronal circuits requires very modest connectivity between these populations; approximately 10% of neurons projecting from one circuit to another circuit will result in high synchronization. In addition, we find that synchronization level inversely affects the strength of connection between neuronal microcircuits - moderately synchronized microcircuits develop stronger intercluster connections than do highly synchronized circuits. Finally, we find that highly synchronized circuits are largely protected against the effects of neuronal deletion but may display changes in frequency properties across circuits with targeted neuronal loss. Together, our results suggest that strongly and weakly connected regions differ in their inherent resilience to damage and may serve different roles in a larger network.