The Influence of Slow Calcium-Activated Potassium Channels on Epileptiform Activity in a Neuronal Model of Pyramidal Cells

An imbalance between excitation and inhibition can play an important role in the generation of epileptiform activity. Experimental evidence indicates that alterations of either synaptic activity or intrinsic membrane properties may contribute to this imbalance. The slow Ca2+ - activated K+ currents (sIAHP) limit neuronal firing rate and excitability and are therefore of great interest for their potential role in epileptogenesis. The sIAHP is found in both excitatory and inhibitory neurons, and its effect on these neurons can influence the network behavior. Simulations show that the increased excitability caused by reduction of inhibition by the sIAHP for inhibitory interneuron generates recurrent bursting activity

The Effect of Changes in the Inhibitory Interneuron Connectivity on the Pattern of Bursting Behavior in a Pyramidal Cell Model

Inhibitory interneurons play crucial roles in the regulation of patterns of activity in the hippocampus, and some types are thought to be vulnerable in epilepsy. The connections between excitatory and inhibitory synapses are important for generation of bursting activity in pyramidal neurons. The present study investigates the influences of changes in the connectivity of interneurons on the patterns of bursting in several excitatory connections using a multicompartmental pyramidal cell model. Simulations show that bursting activity depends upon changes in the connectivity of the inhibitory interneuron, and the location of the inhibitory synapses on excitatory neurons

Inhibition Modifies the Effects of Slow Calcium-Activated Potassium Channels on Epileptiform Activity in a Neuronal Network Model

Generation of epileptiform activity typically results from a change in the balance between network excitation and inhibition. Experimental evidence indicates that alterations of either synaptic activity or intrinsic membrane properties can produce increased network excitation. The slow Ca2+-activated K+ currents (sI AHP) are important modulators of neuronal firing rate and excitability and have important established and potential roles in epileptogenesis. While the effects of changes in sI AHP on individual neuronal excitability are readily studied and well established, the effects of such changes on network behavior are less well known. The experiments here utilize a defined small network model of multicompartment pyramidal cells and an inhibitory interneuron to study the effects of changes in sI AHP on network behavior. The benefits of this model system include the ability to observe activity in all cells in a network and the effects of interactions of multiple simultaneous influences. In the model with no inhibitory interneuron, increasing sI AHP results in progressively decreasing burst activity. Adding an inhibitory interneuron changes the observed effects; at modest inhibitory strengths, increasing sI AHP in all network neurons actually results in increased network bursting (except at very high values). The duration of the burst activity is influenced by the length of delay in a feedback loop, with longer loops resulting in more prolonged bursting. These observations illustrate that the study of potential antiepileptogenic membrane effects must be extended to realistic networks. Network inhibition can dramatically alter the observations seen in pure excitatory networks

The Influences of GABAA and GABAB Inhibition in Bursting Activity in a Model of Pyramidal Cells

This work provides information from the AES Proceedings on epilepsy

A Topological Deep Learning Framework for Neural Spike Decoding

The brain's spatial orientation system uses different neuron ensembles to aid in environment-based navigation. One of the ways brains encode spatial information is through grid cells, layers of decked neurons that overlay to provide environment-based navigation. These neurons fire in ensembles where several neurons fire at once to activate a single grid. We want to capture this firing structure and use it to decode grid cell data. Understanding, representing, and decoding these neural structures require models that encompass higher order connectivity than traditional graph-based models may provide. To that end, in this work, we develop a topological deep learning framework for neural spike train decoding. Our framework combines unsupervised simplicial complex discovery with the power of deep learning via a new architecture we develop herein called a simplicial convolutional recurrent neural network (SCRNN). Simplicial complexes, topological spaces that use not only vertices and edges but also higher-dimensional objects, naturally generalize graphs and capture more than just pairwise relationships. Additionally, this approach does not require prior knowledge of the neural activity beyond spike counts, which removes the need for similarity measurements. The effectiveness and versatility of the SCRNN is demonstrated on head direction data to test its performance and then applied to grid cell datasets with the task to automatically predict trajectories

Language Mapping in Multilingual Patients: Electrocorticography and Cortical Stimulation During Naming

Multilingual patients pose a unique challenge when planning epilepsy surgery near language cortex because the cortical representations of each language may be distinct. These distinctions may not be evident with routine electrocortical stimulation mapping (ESM). Electrocorticography (ECoG) has recently been used to detect task-related spectral perturbations associated with functional brain activation. We hypothesized that using broadband high gamma augmentation (HGA, 60–150 Hz) as an index of cortical activation, ECoG would complement ESM in discriminating the cortical representations of first (L1) and second (L2) languages. We studied four adult patients for whom English was a second language, in whom subdural electrodes (a total of 358) were implanted to guide epilepsy surgery. Patients underwent ECoG recordings and ESM while performing the same visual object naming task in L1 and L2. In three of four patients, ECoG found sites activated during naming in one language but not the other. These language-specific sites were not identified using ESM. In addition, ECoG HGA was observed at more sites during L2 versus L1 naming in two patients, suggesting that L2 processing required additional cortical resources compared to L1 processing in these individuals. Post-operative language deficits were identified in three patients (one in L2 only). These deficits were predicted by ECoG spectral mapping but not by ESM. These results suggest that pre-surgical mapping should include evaluation of all utilized languages to avoid post-operative functional deficits. Finally, this study suggests that ECoG spectral mapping may potentially complement the results of ESM of language

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

From wavelets to adaptive approximations: time-frequency parametrization of EEG

This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals

On the methodological unification in electroencephalography

BACKGROUND: This paper presents results of a pursuit of a repeatable and objective methodology of analysis of the electroencephalographic (EEG) time series. METHODS: Adaptive time-frequency approximations of EEG are discussed in the light of the available experimental and theoretical evidence, and applicability in various experimental and clinical setups. RESULTS: Four lemmas and three conjectures support the following conclusion. CONCLUSION: Adaptive time-frequency approximations of signals unify most of the univariate computational approaches to EEG analysis, and offer compatibility with its traditional (visual) analysis, used in clinical applications