Search bioRxiv⌕ Search

Biology subjects

Zubler, F.

Publications and source records attributed to Zubler, F..

2 recordsLinked to original sources
bioRxiv2025

Sleep-Modulated Cross-Frequency Coupling Between δ Phase and β-γ Bistability: A System-Level Modulation of Epileptic Activity

ObjectiveWhile slow waves in {delta} (0.5-4 Hz) characterize NREM sleep, in patients with sleep-related epilepsy, seizures most frequently emerge during NREM stage 2, known to be promoted by {delta}-band instability. Meanwhile, the epileptogenic zone (EZ) shows localized bistability in {beta}-{gamma} band (15-200 Hz) neuronal oscillations--indicating a catastrophic shift toward seizure. We aim to clarify the mechanistic link between {delta}-band synchrony and {beta}-{gamma} band bistability in epilepsy. MethodsWe studied a cohort of fourteen patients with Sleep Hypermotor Epilepsy (22.3 {+/-} 10.8 years old; 7 males). 7-9-hour stereo-EEG sleep recordings were segmented into 10-minute of uninterrupted, interictal N2 and N3 epochs, and phase synchrony, phase-amplitude coupling (PAC), and bistability were assessed. Canonical correlation was examined to answer whether PAC links {delta}-phase to {beta}-{gamma} bistability. ResultsCompared to non-EZ, the EZ exhibited larger 15-200 Hz bistability along with stronger 2-8 Hz and 15-100 Hz synchrony throughout N2 and N3. Compared to N3, N2 showed stronger PAC between 2-30 Hz phases in the non-EZ and 5-150 Hz amplitudes in the EZ. Canonical correlations between {delta}-phase modulated PAC and both bistability and synchrony were identified during N2 (r = 0.86 and 0.82) and N3 (r = 0.84 and 0.80), with the strongest contributors being 2-4 Hz synchrony and bistability in 2-4 Hz and 15-200 Hz bands. Correlations between interictal spikes and canonical covariates of bistability and PAC (r2 = 0.62 for N2 and 0.56 for N3) validated their relevance to epileptogenicity. Significance{delta}-band synchrony and {beta}-{gamma} band bistability are not isolated epileptogenic mechanisms but likely act synergistically, playing a pivotal role in seizure generation through the coupling of {delta} phases and {beta}-{gamma} amplitudes across large networks, with significant contributions from non-epileptogenic tissues. Key pointsO_LIStrong {beta}-{gamma} bistability in neuronal oscillations localizes the EZ throughout N2 and N3 sleep. C_LIO_LIElevated {delta}-band phase synchrony characterizes the EZ and its functional neighbors throughout N2 and N3 sleep. C_LIO_LIa-band synchrony modulates local {beta}-{gamma} bistability through PAC, with significant contributions from non-EZ tissues. C_LI

neuroscience↗
bioRxiv2018

A Gene Regulatory Model of Cortical Neurogenesis

Sparse data describing mouse cortical neurogenesis were used to derive a model gene regulatory network (GRN) that is then able to control the quantitative cellular dynamics of the observed neurogenesis. Derivation of the network begins by estimating from the biological data a set of cell states and transition probabilities necessary to explain neurogenesis. We show that the stochastic transition between states can be implemented by the dynamics of a GRN comprising only 36 abstract genes. Finally, we demonstrate using detailed physical simulations of cell mitosis, and differentiation that this GRN is able to steer a population of neuroepithelial precursors through mitotic expansion and differentiation to form the quantitatively correct complex multicellular architectures of mouse cortical areas 3 and 6. We find that the same GRN is able to generate both areas though modulation of only one gene, suggesting that arealization of the cortical sheet may require only simple improvisations on a fundamental gene network. We conclude that even sparse phenotypic and cell lineage data can be used to infer fundamental properties of neurogenesis and its organization.\n\n1. HighlightsO_LIEstimation of the cell states and transition probabilities of neurogenesis from experimental data.\nC_LIO_LIDesign of an abstract gene regulatory network (GRN) whose dynamics implement cell states and their stochastic transitions.\nC_LIO_LIDetailed simulation of GRN-guided neurogenesis for mouse cortical areas 3 and 6.\nC_LIO_LIDifferent dynamics of neurogenesis of distinct cortical areas arise through modulation of only a single gene.\nC_LI\n\n2. In briefPfister et al. show how sparse phenotypic and cell lineage data can be used to infer a small abstract gene regulatory network (GRN), which, when inserted into model precursor cells, is able to control in a distributed manner the quantitative cellular dynamics of neocortical neurogenesis.

neuroscience↗