Pawel Kudela

Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation

PURPOSE A neural network simulation with realistic cortical architecture has been used to study synchronized bursting as a seizure representation. This model has the property that bursting epochs arise and cease spontaneously, and bursting epochs can be induced by external stimulation. We have used this simulation to study the time-frequency properties of the evolving bursting activity, as well as effects due to network stimulation. METHODS The model represents a cortical region of 1.6 mm x 1.6mm, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. There are a total of 65,536 modeled single compartment neurons that operate according to a version of Hodgkin-Huxley dynamics. The intercellular wiring is based on histological studies and our previous modeling efforts. RESULTS The bursting phase is characterized by a flat frequency spectrum. Stimulation pulses are applied to this modeled network, with an electric field provided by a 1mm radius circular electrode represented mathematically in the simulation. A phase dependence to the post-stimulation quiescence is demonstrated, with local relative maxima in efficacy occurring before or during the network depolarization phase in the underlying activity. Brief periods of network insensitivity to stimulation are also demonstrated. The phase dependence was irregular and did not reach statistical significance when averaged over the full 2.5s of simulated bursting investigated. This result provides comparison with previous in vivo studies which have also demonstrated increased efficacy of stimulation when pulses are applied at the peak of the local field potential during cortical after discharges. The network bursting is synchronous when comparing the different neuron classes represented up to an uncertainty of 10 ms. Studies performed with an excitatory chandelier cell component demonstrated increased synchronous bursting in the model, as predicted from experimental work. CONCLUSIONS This large-scale multi-neuron neural network simulation reproduces many aspects of evolving cortical bursting behavior as well as the timing-dependent effects of electrical stimulation on that bursting.

External termination of recurrent bursting in a model of connected local neural sub-networks

Abstract Epileptic seizures are characterized by repetitive synchronous neuronal bursting activity. To study external influences on this activity, a simple model of a chain loop of neuronal networks has been developed with the goal of understanding how external stimuli can contribute to reduction or cessation of such abnormal recurrent bursting activity. Using this simulated loop model, one can demonstrate that an external stimulus that normally can initiate repetitive activity can terminate the bursting activity when applied to a chain loop of sub-networks, which has actively propagating bursts. The ability of external currents to terminate activity is dependent upon loop length and the timing of the applied external stimulus. Termination of propagation in long loops (40–64 sub-networks in this model) requires application of multiple simultaneous stimuli to different sub-networks.

A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone

OBJECTIVE The use of micro-electrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure onset zone (SOZ) localization. In this work, we used a multivariate autoregressive model to determine the evolution of seizure dynamics in the [Formula: see text] Hz high frequency band across micro-domains sampled by such micro-electrode arrays. We showed that a directed transfer function (DTF) can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with known propagation pattern. APPROACH We used seven complex partial seizures recorded from four patients undergoing intracranial monitoring for surgical evaluation to reconstruct the seizure propagation pattern over sliding windows using a DTF measure. MAIN RESULTS We showed that a DTF can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with a known propagation pattern. In general, depending on the location of the micro-electrode grid with respect to the clinical SOZ and the time from seizure onset, ictal propagation changed in directional characteristics over a 2-10 s time scale, with gross directionality limited to spatial dimensions of approximately [Formula: see text]. It was also seen that the strongest seizure patterns in the high frequency band and their sources over such micro-domains are more stable over time and across seizures bordering the clinically determined SOZ than inside. SIGNIFICANCE This type of propagation analysis might in future provide an additional tool to epileptologists for characterizing epileptogenic tissue. This will potentially help narrowing down resection zones without compromising essential brain functions as well as provide important information about targeting anti-epileptic stimulation devices.

Synaptic and Cellular Influences on the Composite EEG Signal During Seizures

We utilize neuronal network models to identify the potential effects of the synaptic, cellular, and membrane behaviors on the characteristics and the composites of epileptic EEG. In these network models the average membrane potential of neurons in a network is calculated while epileptiform activity in this network is simulated. Our results suggest that seizure activity may trigger changes in synaptic efficacy and alter membrane excitability. These alterations contribute to the pattern of frequency changes observed in simulated average membrane potential signal. We suggest that these factors may influence seizure dynamics and contribute to the ictal EEG pattern in humans

Computational Modeling of Subdural Cortical Stimulation: A Quantitative Spatiotemporal Analysis of Action Potential Initiation in a High-Density Multicompartment Model.

Computational modeling studies were performed to identify presynaptic elements of cortical neurons that are activated by subdural electrical stimulation.

Modeling Neural Adaptation in Auditory Cortex

Neural responses recorded from auditory cortex exhibit adaptation, a stimulus-specific decrease that occurs when the same sound is presented repeatedly. Stimulus-specific adaptation is thought to facilitate perception in noisy environments. Although adaptation is assumed to arise independently from cortex, this has been difficult to validate directly in vivo. In this study, we used a neural network model of auditory cortex with multicompartmental cell modeling to investigate cortical adaptation. We found that repetitive, non-adapted inputs to layer IV neurons in the model elicited frequency-specific decreases in simulated single neuron, population-level and local field potential (LFP) activity, consistent with stimulus-specific cortical adaptation. Simulated recordings of LFPs, generated solely by excitatory post-synaptic inputs and recorded from layers II/III in the model, showed similar waveform morphologies and stimulus probability effects as auditory evoked responses recorded from human cortex. We tested two proposed mechanisms of cortical adaptation, neural fatigue and neural sharpening, by varying the strength and type of inter- and intra-layer synaptic connections (excitatory, inhibitory). Model simulations showed that synaptic depression modeled in excitatory (AMPA) synapses was sufficient to elicit a reduction in neural firing rate, consistent with neural fatigue. However, introduction of lateral inhibition from local layer II/III interneurons resulted in a reduction in the number of responding neurons, but not their firing rates, consistent with neural sharpening. These modeling results demonstrate that adaptation can arise from multiple neural mechanisms in auditory cortex.

Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex

Within multiscale brain dynamics, the structure–function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level. We operationalized microscale damage to neurons as the formation of pores on the neuronal membrane based on research using blast paradigms, and in our model, pores were simulated by a change in membrane conductance. We then tracked changes in simulated electrical activity. Our model contained 585 simulated neurons, comprised of 14 types of cortical and thalamic neurons each with its own compartmental morphology and electrophysiological properties. Comparing the functional activity of neurons before and after simulated damage, we found that simulated pores in the membrane reduced both action potential generation and local field potential (LFP) power in the 1–40 Hz range of the power spectrum. Furthermore, the location of damage modulated the strength of these effects: pore formation on simulated axons reduced LFP power more strongly than did pore formation on the soma and the dendrites. These results indicate that even small amounts of cellular damage can negatively impact functional activity of larger scale oscillations, and our findings suggest that multiscale modeling provides a promising avenue to elucidate these relationships.

Determination of seizure propagation across microdomains using spectral measures of causality

The use of microelectrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure focus localization. In this work, we determine seizure propagation across microdomains sampled by such microelectrode arrays and compare the results using two widely used frequency domain measures of causality, namely the partial directed coherence and the directed direct transfer function. We show that these two measures produce very similar propagation patterns for simulated microelectrode activity over a relatively smaller number of channels. However as the number of channels increases, partial directed coherence produces better estimates of the actual propagation pattern. Additionally, we apply these two measures to determine seizure propagation over microelectrode arrays measured from a patient undergoing intracranial monitoring for seizure focus localization and find very similar patterns which also agree with a threshold based reconstruction during seizure onset.