Neural dynamics and seizure correlations: Insights from neural mass models in a Tetanus Toxin rat model of epilepsy.

This study focuses on the use of a neural mass model to investigate potential relationships between functional connectivity and seizure frequency in epilepsy. We fitted a three-layer neural mass model of a cortical column to intracranial EEG (iEEG) data from a Tetanus Toxin rat model of epilepsy, which also included responses to periodic electrical stimulation. Our results show that some of the connectivity weights between different neural populations correlate significantly with the number of seizures each day, offering valuable insights into the dynamics of neural circuits during epileptogenesis. We also simulated single-pulse electrical stimulation of the neuronal populations to observe their responses after the connectivity weights were optimized to fit background (non-seizure) EEG data. The recovery time, defined as the time from stimulation until the membrane potential returns to baseline, was measured as a representation of the critical slowing down phenomenon observed in nonlinear systems operating near a bifurcation boundary. The results revealed that recovery times in the responses of the computational model fitted to the EEG data were longer during 5 min periods preceding seizures compared to 1 hr before seizures in four out of six rats. Analysis of the iEEG recorded in response to electrical stimulation revealed results similar to the computational model in four out of six rats. This study supports the potential use of this computational model as a model-based biomarker for seizure prediction when direct electrical stimulation to the brain is not feasible.

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column.

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their "paradoxical response", where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation-inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation-inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points.

Model Parameter Estimation As Features to Predict the Duration of Epileptic Seizures From Onset.

The durations of epileptic seizures are linked to severity and risk for patients. It is unclear if the spatiotemporal evolution of a seizure has any relationship with its duration. Understanding such mechanisms may help reveal treatments for reducing the duration of a seizure. Here, we present a novel method to predict whether a seizure is going to be short or long at its onset using features that can be interpreted in the parameter space of a brain model. The parameters of a Jansen-Rit neural mass model were tracked given intracranial electroencephalography (iEEG) signals, and were processed as time series features using MINIROCKET. By analysing 2954 seizures from 10 patients, patient-specific classifiers were built to predict if a seizure would be short or long given 7 s of iEEG at seizure onset. The method achieved an area under the receiver operating characteristic curve (AUC) greater than 0.6 for five of 10 patients. The behaviour in the parameter space has shown different mechanisms are associated with short/long seizures.Clinical relevance-This shows that it is possible to classify whether a seizure will be short or long based on its early characteristics. Timely interventions and treatments can be applied if the duration of the seizures can be predicted.

Brain model state space reconstruction using an LSTM neural network.

Objective. Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network.Approach. An LSTM filter was trained on simulated EEG data generated by a NMM using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input.Main results. Test results using simulated data yielded correlations withRsquared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures.Significance. Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any NMM and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

Preferential modulation of individual retinal ganglion cells by electrical stimulation.

Objective.Retinal prostheses have had limited success in vision restoration through electrical stimulation of surviving retinal ganglion cells (RGCs) in the degenerated retina. This is partly due to non-preferential stimulation of all RGCs near a single stimulating electrode, which include cells that conflict in their response properties and their contribution to visiual processing. Our study proposes a stimulation strategy to preferentially stimulate individual RGCs based on their temporal electrical receptive fields (tERFs).Approach.We recorded the responses of RGCs using whole-cell patch clamping and demonstrated the stimulation strategy, first using intracellular stimulation, then via extracellular stimulation.Main results. We successfully reconstructed the tERFs according to the RGC response to Gaussian white noise current stimulation. The characteristics of the tERFs were extracted and compared based on the morphological and light response types of the cells. By re-delivering stimulation trains that were composed of the tERFs obtained from different cells, we could preferentially stimulate individual RGCs as the cells showed lower activation thresholds to their own tERFs.Significance.This proposed stimulation strategy implemented in the next generation of recording and stimulating retinal prostheses may improve the quality of artificial vision.

A new algorithm for drift compensation in multi-unit recordings of action potentials in peripheral autonomic nerves over time.

BACKGROUND: Peripheral autonomic nerves control visceral organs and convey information regarding their functional states and are, therefore, potential targets for new therapeutic and diagnostic approaches. Conventionally recorded multi-unit nerve activity in vivo undergoes slow differential drift of signal and noise amplitudes, making accurate monitoring of nerve activity for more than tens of minutes problematic. NEW METHOD: We describe an on-line drift compensation algorithm that utilizes recursive least-squares to estimate the relative change in spike amplitude due to changes in the nerve-electrode interface over time. RESULTS: We tested and refined our approach using simulated data and in vivo recordings from nerves supplying the small intestine under control conditions and in response to gut inflammation over several hours. The algorithm is robust to changes in recording conditions and signal-to-noise ratio and applicable to both single and multi-unit recordings. In uncompensated records, drift prevented "spike families" and single units from being discriminated accurately over hours. After rescaling, these were successfully tracked throughout recordings (up to 3 h). COMPARISON WITH EXISTING METHODS: Existing methods are subjective or compensate for drift using spatial information and spike shape data which is not practical in multi-unit peripheral nerve recordings. In contrast, this method is objective and applicable to data from a single differential multi-unit recording. In comparisons using simulated data the algorithm performed as well as or better than existing methods. CONCLUSIONS: Results suggest our drift compensation algorithm is widely applicable and robust, though conservative, when differentiating prolonged responses from drift in signal. Extracellular nerve recordings; drift compensation; chronic nerve recordings; closed-loop; multi-unit activity; spike discrimination; recursive least squares; real-time.

Hybrid diamond/ carbon fiber microelectrodes enable multimodal electrical/chemical neural interfacing.

Implantable medical devices are now in regular use to treat or ameliorate medical conditions, including movement disorders, chronic pain, cardiac arrhythmias, and hearing or vision loss. Aside from offering alternatives to pharmaceuticals, one major advantage of device therapy is the potential to monitor treatment efficacy, disease progression, and perhaps begin to uncover elusive mechanisms of diseases pathology. In an ideal system, neural stimulation, neural recording, and electrochemical sensing would be conducted by the same electrode in the same anatomical region. Carbon fiber (CF) microelectrodes are the appropriate size to achieve this goal and have shown excellent performance, in vivo. Their electrochemical properties, however, are not suitable for neural stimulation and electrochemical sensing. Here, we present a method to deposit high surface area conducting diamond on CF microelectrodes. This unique hybrid microelectrode is capable of recording single-neuron action potentials, delivering effective electrical stimulation pulses, and exhibits excellent electrochemical dopamine detection. Such electrodes are needed for the next generation of miniaturized, closed-loop implants that can self-tune therapies by monitoring both electrophysiological and biochemical biomarkers.