Kuchenbuch, M. | Search bioRxiv

bioRxiv2025

Fully Automated EEG Source Imaging Using Structured Sparsity for Single and Multiple Synchronous Epileptic Activities

Accurate localization of epileptic zones from High-Resolution ElectroEncephaloGraphy (HR-EEG) data can be challenging, especially when multiple synchronous zones are involved, and is highly dependent on the chosen EEG Source Imaging (ESI) method. Since a given scalp-level electrical pattern can result from multiple source configurations, ESI methods address this ill-posed inverse problem by imposing constraints on the structure of underlying sources. Here, we present an efficient approach that imposes sparsity on both the source-level activity and its spatial gradient. Unlike other methods that generally require a heuristic choice of a regularization parameter that balances between data fidelity and constraints, our method iteratively adjusts the parameter value based on the noise level in a fully automated way. The performance of the new method is evaluated across different scenarios of realistic synthetic HR-EEG data, including unifocal and synchronous multifocal cortical epileptic activity. Based on multiple performance indices, we demonstrate that the proposed approach outperforms traditional methods in accurately reconstructing epileptic sources. We also show that the method reduces polarity artifacts responsible for ghost sources and spatial discontinuities. Its ability to recover homogeneous, well-delineated regions of activity is further confirmed using real EEG data capturing a typical absence seizure.

neuroscience↗

bioRxiv2023

Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to Low-Voltage-Fast onset seizures in Dravet syndrome.

Epilepsies have numerous specific mechanisms. Understanding neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing targeted therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials and hippocampal single-unit neuronal activities in Scn1a+/- knock-out and Scn1aRH/+ knock-in DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal period) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients EEG, which are consistent with the dysfunctions that we observed in single neurons. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Spectral features may be used as predictive seizure biomarker. Significance statementDravet syndrome (DS) is caused by mutations of the NaV1.1 sodium channel (SCN1A gene) leading to hypoexcitability of GABAergic interneurons. We found that most of the seizures in both DS patients and mouse models have low-voltage-fast onset, which is instead thought to be generated by hyperactivity of GABAergic neurons. We disclosed a disorganization in the temporal pattern of the firing of single interneurons before the seizure (preictal period), and a rebound hyperactivity at seizure onset. Consistently, the electrographic signal showed a decrease of fast oscillations in the preictal period. Thus, perturbed interneurons preictal activity, consistent with the main mechanism of DS, leads to their hyperactivity at seizure onset and induces specific electrographic signatures that may be exploited for seizure prediction.

neuroscience↗