Common time-frequency analysis of local field potential and pyramidal cell activity in seizure-like events of the rat hippocampus

Phase-amplitude coupling detection and analysis of human 2-dimensional neural cultures in multi-well microelectrode array in vitro

BACKGROUND

Human induced pluripotent stem cell (hiPSC)- derived neurons offer the possibility of studying human-specific neuronal behaviors in physiologic and pathologic states in vitro. It is unclear whether cultured neurons can achieve the fundamental network behaviors required to process information in the brain. Investigating neuronal oscillations and their interactions, as occurs in cross-frequency coupling (CFC), addresses this question.

NEW METHODS

We examined whether networks of two-dimensional (2D) cultured hiPSC-derived cortical neurons grown with hiPSC-derived astrocytes on microelectrode array plates recapitulate the CFC that is present in vivo. We employed the modulation index method for detecting phase-amplitude coupling (PAC) and used offline spike sorting to analyze the contribution of single neuron spiking to network behavior.

RESULTS

We found that PAC is present, the degree of PAC is specific to network structure, and it is modulated by external stimulation with bicuculline administration. Modulation of PAC is not driven by single neurons, but by network-level interactions.

COMPARISON WITH EXISTING METHODS

PAC has been demonstrated in multiple regions of the human cortex as well as in organoids. This is the first report of analysis demonstrating the presence of coupling in 2D cultures.

CONCLUSION

CFC in the form of PAC analysis explores communication and integration between groups of neurons and dynamical changes across networks. In vitro PAC analysis has the potential to elucidate the underlying mechanisms as well as capture the effects of chemical, electrical, or ultrasound stimulation; providing insight into modulation of neural networks to treat nervous system disorders in vivo.

Phase-Amplitude Coupling Detection and Analysis of Human 2-Dimensional Neural Cultures in Multi-well Microelectrode Array in Vitro

Human induced pluripotent stem cell (hiPSC) - derived neurons offer the possibility of studying human-specific neuronal behaviors in physiologic and pathologic states in vitro . However, it is unclear whether these cultured neurons can achieve the fundamental network behaviors that are required to process information in the human brain. Investigating neuronal oscillations and their interactions, as occurs in cross-frequency coupling (CFC), is potentially a relevant approach. Microelectrode array culture plates provide a controlled framework to study populations of hiPSC-derived cortical neurons (hiPSC-CNs) and their electrical activity. Here, we examined whether networks of two-dimensional cultured hiPSC-CNs recapitulate the CFC that is present in networks in vivo . We analyzed the electrical activity recorded from hiPSC-CNs grown in culture with hiPSC-derived astrocytes. We employed the modulation index method for detecting phase-amplitude coupling (PAC) and used an offline spike sorting method to analyze the contribution of a single neuron's spiking activities to network behavior. Our analysis demonstrates that the degree of PAC is specific to network structure and is modulated by external stimulation, such as bicuculine administration. Additionally, the shift in PAC is not driven by a single neuron's properties but by network-level interactions. CFC analysis in the form of PAC explores communication and integration between groups of nearby neurons and dynamical changes across the entire network. In vitro , it has the potential to capture the effects of chemical agents and electrical or ultrasound stimulation on these interactions and may provide valuable information for the modulation of neural networks to treat nervous system disorders in vivo .

Significance

Phase amplitude coupling (PAC) analysis demonstrates that the complex interactions that occur between neurons and network oscillations in the human brain, in vivo , are present in 2-dimensional human cultures. This coupling is implicated in normal cognitive function as well as disease states. Its presence in vitro suggests that PAC is a fundamental property of neural networks. These findings offer the possibility of a model to understand the mechanisms and of PAC more completely and ultimately allow us to understand how it can be modulated in vivo to treat neurologic disease.

Electrophysiology as a Tool to Decipher the Network Mechanism of Visceral Pain in Functional Gastrointestinal Disorders

Abdominal pain, including visceral pain, is prevalent in functional gastrointestinal (GI) disorders (FGIDs), affecting the overall quality of a patient's life. Neural circuits in the brain encode, store, and transfer pain information across brain regions. Ascending pain signals actively shape brain dynamics; in turn, the descending system responds to the pain through neuronal inhibition. Pain processing mechanisms in patients are currently mainly studied with neuroimaging techniques; however, these techniques have a relatively poor temporal resolution. A high temporal resolution method is warranted to decode the dynamics of the pain processing mechanisms. Here, we reviewed crucial brain regions that exhibited pain-modulatory effects in an ascending and descending manner. Moreover, we discussed a uniquely well-suited method, namely extracellular electrophysiology, that captures natural language from the brain with high spatiotemporal resolution. This approach allows parallel recording of large populations of neurons in interconnected brain areas and permits the monitoring of neuronal firing patterns and comparative characterization of the brain oscillations. In addition, we discussed the contribution of these oscillations to pain states. In summary, using innovative, state-of-the-art methods, the large-scale recordings of multiple neurons will guide us to better understanding of pain mechanisms in FGIDs.

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised two-state hidden Markov model (HMM). γ-Distributions of their durations indicated that both are predictable. Furthermore, omitting 4 Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-aminopyridine (4-AP)-induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12 Hz, inhibitory currents dominantly phase locked at 4 Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.

Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations.

Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain

OBJECTIVE

One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80 Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties.

METHODS

This study deals with a four-unit neuroglial cellular network model where each unit incorporates pyramidal cells, interneurons, and astrocytes. Three different pathways of hyperexcitability generation-Na - ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel-were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration, and CFC were then measured and analyzed.

RESULTS

Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz).

CONCLUSION

Longer duration SEDs exhibit CFC features similar to those reported by our team.

SIGNIFICANCE

First, Identifying the exponential relationship between network excitability and SED durations; second, highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium); and third, elucidation of the biophysical basis for CFC coupling features.

Evaluation of local field potential signals in decoding of visual attention

In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.