Biology subjects

Zarei Eskikand, P.

Publications and source records attributed to Zarei Eskikand, P..

7 recordsLinked to original sources

bioRxiv2025

Forecasting Seizure Duration from Neural Connectivity Patterns in a Neural Mass Model

Understanding seizure duration is crucial for predicting seizure evolution and developing targeted therapeutic strategies. This study investigates how changes in neural connectivity patterns estimated in a Neural Mass Model (NMM) relate to seizure duration and whether these parameters can be used to forecast seizure duration. We applied a biologically plausible NMM to intracranial EEG recordings from a Tetanus Toxin rat model of epilepsy. Model parameters, representing synaptic connectivity strengths between excitatory and inhibitory neuronal populations across different cortical layers, were estimated using the Unscented Kalman Filter. A Random Forest classifier was trained to predict seizure duration (short vs. long) based on these connectivity parameters. We assessed the classification performance using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Our findings reveal that stronger excitatory-to-excitatory connectivity before seizure onset was associated with longer seizure durations. In contrast, inhibitory-to-inhibitory and excitatory-to-inhibitory connectivity strengths decreased before long seizures. The Random Forest classifier achieved an AUC of 0.91 when using both preictal (1 minute before seizure onset) and early ictal (first 5 seconds of the seizures) parameters, demonstrating excellent predictive power. Classification using only preictal parameters resulted in an AUC of 0.70. Our study highlights the critical role of preictal neural connectivity in determining seizure duration. The findings suggest that network excitability and inhibitory control before seizure onset influence how long seizures persist. These insights provide a foundation for personalized, time-based therapeutic interventions and seizure management strategies, ultimately improving the quality of life of patients with epilepsy.

pathology↗

bioRxiv2024

Cycles in Seizure Duration and Their Underlying Dynamics in the Tetanus Toxin Rat Model

Seizure duration, a characteristic of epilepsy that is understudied in relation in its relationship with rhythmic cycles, provides critical insights into the severity and temporal dynamics of seizures. This study investigates the rhythmic patterns of seizure duration in the Tetanus Toxin (TT) rat model of epilepsy, which is a well-established platform enabling long-term, stable recordings and observation of seizure emergence and remission. Our analysis shows significant cyclical patterns in seizure durations, with periods ranging from 4 to 8 days across rats. The synchronization index and circular-linear correlations revealed phase-locked relationships between seizure durations and cycles, suggesting non-random, predictable temporal dynamics. Further analyses examined the relationship between seizure durations, inter-seizure intervals, and dominant EEG power. The relationship between inter-seizure intervals and seizure duration was modest, suggesting little to no temporal dependency. In contrast, seizure duration showed stronger associations with EEG power in dominant frequency bands, potentially reflecting fluctuations in neural excitability. The findings highlight that seizure durations exhibit predictable rhythms, which could transform seizure prediction and enable time-based intervention strategies, ultimately improving epilepsy management and patient outcomes. These insights lay the groundwork for personalized, rhythm-aware therapeutic approaches.

neuroscience↗

bioRxiv2024

Dynamic Multiday Seizure Cycles in a Tetanus Toxin Rat Model of Epilepsy: Evolving Rhythms and Implications for Prediction

Epilepsy is characterized by recurrent, unpredictable seizures that impose significant challenges in daily management and treatment. One emerging area of interest is the identification of seizure cycles, including multiday patterns, which may offer insights into seizure prediction and treatment optimization. This study investigated multiday seizure cycles in a Tetanus Toxin (TT) rat model of epilepsy. Six TT-injected rats were observed over a 40-day period, with continuous EEG monitoring to record seizure events. Wavelet transform analysis revealed significant multiday cycles in seizure occurrences, with periods ranging from 4 to 7 days across different rats. Synchronization Index (SI) analysis demonstrated variable phase locking, with some rats showing strong synchronization of seizures with specific phases of the cycle. Importantly, the study revealed that these seizure cycles are dynamic and evolve over time, with some rats exhibiting shifts in cycle periods during the recording period. This suggests that the underlying neural mechanisms driving these cycles may change as the epileptic state progresses. The identification of stable and evolving multiday rhythms in seizure activity, independent of external factors, highlights a potential intrinsic biological basis for seizure timing. These findings offer promising avenues for improving seizure forecasting and designing personalized, timing-based therapeutic interventions in epilepsy. Future research should explore the underlying neural mechanisms and clinical applications of multiday seizure cycles.

neuroscience↗

bioRxiv2024

Balancing Prior Knowledge and Sensory Data in a Predictive Coding Model: Insights into Coherent Motion Detection in Schizophrenia

This study introduces a biologically plausible computational model based on the predictive coding algorithm, providing insights into motion detection processes and potential deficiencies in schizophrenia. The model decomposes motion structures into individual and shared sources, highlighting a critical role of surround suppression in detecting global motion. This biologically plausible model sheds light on how the brain extracts the structure of motion and comprehends shared or coherent motion within the visual field. The results obtained from random dot stimuli underscore the delicate balance between sensory data and prior knowledge in coherent motion detection. Model testing across varying noise levels reveals longer convergence times with higher noise, consistent with psychophysical experiments showing that response duration (e.g., reaction time or decision-making time) also increases with noise levels. The model suggests that an excessive emphasis on prior knowledge extends the convergence time in motion detection. Conversely, for faster convergence, the model requires a certain level of prior knowledge to prevent excessive disturbance due to noise. These findings contribute to potential explanations for motion detection deficiencies observed in schizophrenia.

neuroscience↗

bioRxiv2024

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.

neuroscience↗

bioRxiv2022

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. Author summaryStrong feedback inhibition prevents neural networks from becoming unstable. Inhibition Stabilized Networks (ISNs) have strong inhibitory connections combined with high levels of unstable excitatory recurrent connections. In the absence of strong inhibitory feedback, ISNs become unstable. ISNs demonstrate a paradoxical effect: perturbing inhibitory neurons in an ISN by increasing their excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we developed a neural mass model of a cortical column based on neurophysiological data. The model shows a gradual change in inhibitory stabilization across different layers in the cortex where layer 2/3 is less inhibitory stabilized and shows no paradoxical effect in contrast to layer 4 and layer 5, which operate in the ISN regime and show paradoxical responses to perturbation.

neuroscience↗

bioRxiv2021

Computational simulations and Ca2+ imaging reveal that slow synaptic depolarizations (slow EPSPs) inhibit fast EPSP evoked action potentials for most of their time course in enteric neurons

Transmission between neurons in the extensive enteric neural networks of the gut involves synaptic potentials with vastly different time courses and underlying conductances. Most enteric neurons exhibit fast excitatory post-synaptic potentials (EPSPs) lasting 20-50 ms, but many also exhibit slow EPSPs that last up to 100 s. When large enough, slow EPSPs excite action potentials at the start of the slow depolarization, but how they affect action potentials evoked by fast EPSPs is unknown. Furthermore, two other sources of synaptic depolarization probably occur in enteric circuits, activated via GABAA or GABAC receptors; how these interact with other synaptic depolarizations is also unclear. We built a compartmental model of enteric neurons incorporating realistic voltage-dependent ion channels, then simulated fast EPSPs, slow EPSPs and GABAA or GABAC ligand-gated Cl- channels to explore these interactions. Model predictions were tested by imaging Ca2+ transients in myenteric neurons ex vivo as an indicator of their activity during synaptic interactions. The model could mimic firing of myenteric neurons in mouse colon evoked by depolarizing current during intracellular recording and the fast and slow EPSPs in these neurons. Subthreshold fast EPSPs evoked spikes during the rising phase of a slow EPSP, but suprathreshold fast EPSPs could not evoke spikes later in a slow EPSP. This predicted inhibition was confirmed by Ca2+ imaging in which stimuli that evoke slow EPSPs suppressed activity evoked by fast EPSPs in many myenteric neurons. The model also predicted that synchronous activation of GABAA receptors and fast EPSPs potentiated firing evoked by the latter, while synchronous activation of GABAC receptors with fast EPSPs, potentiated firing and then suppressed it. The results reveal that so-called slow EPSPs have a biphasic effect being likely to suppress fast EPSP evoked firing over very long periods, perhaps accounting for prolonged quiescent periods seen in enteric motor patterns. Author SummaryThe gastrointestinal tract is the only organ with an extensive semi-autonomous nervous system that generates complex contraction patterns independently. Communication between neurons in this "enteric" nervous system is via depolarizing synaptic events with dramatically different time courses including fast synaptic potentials lasting around 20-50 ms and slow depolarizing synaptic potentials lasting for 10 - 120 s. Most neurons have both. We explored how slow synaptic depolarizations affect generation of action potentials by fast synaptic potentials using computational simulation of small networks of neurons implemented as compartmental models with realistic membrane ion channels. We found that slow synaptic depolarizations have biphasic effects; they initially make fast synaptic potentials more likely to trigger action potentials, but then actually prevent action potential generation by fast synaptic potentials with the inhibition lasting several 10s of seconds. We confirmed the inhibitory effects of the slow synaptic depolarizations using live Ca imaging of enteric neurons from mouse colon in isolated tissue. Our results identify a novel form of synaptic inhibition in the enteric nervous system of the gut, which may account for the vastly differing time courses between signalling in individual gut neurons and rhythmic contractile patterns that often repeat at more than 60 s intervals.

neuroscience↗