Biology subjects

Grayden, D.

Publications and source records attributed to Grayden, D..

2 recordsLinked to original sources

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↗

bioRxiv2022

A novel closed-loop control algorithm to disrupt pathological neuronal oscillations - implementation and validation in vitro

Adaptive neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinsons disease and epilepsy. However, standard stimulation protocols mostly rely on continuous open-loop stimulation. We implement here, for the first time in neuronal populations, two different Delayed Feedback Control (DFC) algorithms and assess their efficacy in disrupting unwanted neuronal oscillations. DFC is a well-established closed-loop control technique but its use in neuromodulation has been limited so far to models and computational studies. Leveraging on the high spatiotemporal monitoring capabilities of specialized in vitro platforms, we show that standard DFC in fact worsens the neuronal population oscillatory behaviour and promotes faster bursting, which was never reported in silico. Alternatively, we present adaptive DFC (aDFC) that monitors ongoing oscillation periodicity and self-tunes accordingly. aDFC disrupts collective neuronal oscillations and decreases network synchrony. Furthermore, we show that the intrinsic population dynamics have a strong impact in the susceptibility of networks to neuromodulation. Experimental data was complemented with computer simulations to show how this network controllability might be determined by specific network properties. Overall, these results support aDFC as a better candidate for therapeutic neurostimulation and provide new insights regarding the controllability of neuronal systems.

neuroscience↗