arXiv subjects

Gerold Baier

Publications and source records attributed to Gerold Baier.

3 recordsLinked to original sources

arXiv2020

Band power modulation through intracranial EEG stimulation and its cross-session consistency

Background: Direct electrical stimulation of the brain through intracranial electrodes is currently used to probe the epileptic brain as part of pre-surgical evaluation, and it is also being considered for therapeutic treatments through neuromodulation. It is still unknown, however, how consistent intracranial direct electrical stimulation responses are across sessions, to allow effective neuromodulation design. Objective: To investigate the cross-session consistency of the electrophysiological effect of electrical stimulation delivered through intracranial EEG. Methods: We analysed data from 79 epilepsy patients implanted with intracranial EEG who underwent brain stimulation as part of a memory experiment. We quantified the effect of stimulation in terms of band power modulation and compared this effect from session to session. As a reference, we applied the same measures during baseline periods. Results: In most sessions, the effect of stimulation on band power could not be distinguished from baseline fluctuations of band power. Stimulation effect was also not consistent across sessions; only a third of the session pairs had a higher consistency than the baseline standards. Cross-session consistency is mainly associated with the strength of positive stimulation effects, and it also tends to be higher when the baseline conditions are more similar between sessions. Conclusion: These findings can inform our practices for designing neuromodulation with greater efficacy when using direct electrical brain stimulation as a therapeutic treatment.

q-bio.NC↗

arXiv2017

Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients

Electro-cortical activity in patients with epilepsy may show abnormal rhythmic transients in response to stimulation. Even when using the same stimulation parameters in the same patient, wide variability in the duration of transient response has been reported. These transients have long been considered important for the mapping of the excitability levels in the epileptic brain but their dynamic mechanism is still not well understood. To understand the occurrence of abnormal transients dynamically, we use a thalamo-cortical neural population model of epileptic spike-wave activity and study the interaction between slow and fast subsystems. In a reduced version of the thalamo-cortical model, slow wave oscillations arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region of bistability between a high amplitude oscillatory rhythm and the background state. In vicinity of the bistability in parameter space, the model has excitable dynamics, showing prolonged rhythmic transients in response to suprathreshold pulse stimulation. We analyse the state space geometry of the bistable and excitable states, and find that the rhythmic transient arises when the impending FoC bifurcation deforms the state space and creates an area of locally reduced attraction to the fixed point. This area essentially allows trajectories to dwell there before escaping to the stable steady state, thus creating rhythmic transients. In the full thalamo-cortical model, we find a similar FoC bifurcation structure. Based on the analysis, we propose an explanation of why stimulation induced epileptiform activity may vary between trials, and predict how the variability could be related to ongoing oscillatory background activity.

q-bio.NC↗

arXiv2004

The Nonlinear Dynamic Conversion of Analog Signals into Excitation Patterns

Local periodic perturbations induce frequency-dependent propagation waves in an excitable spatio-temporally chaotic system. We show how segments of noise-contaminated and chaotic perturbations induce characteristic sequences of excitations in the model system. Using a set of tuned excitable systems, it is possible to characterize signals by their spectral composition of excitation pattern. As an example we analyze an epileptic spike-and-wave time series.

nlin.CD↗