Biology subjects

Siebenhuehner, F.

Publications and source records attributed to Siebenhuehner, F..

3 recordsLinked to original sources

bioRxiv2025

Spectral patterns of MEG oscillatory coupling emerge from meta-stable dynamics with small coupling delays

Functional connectivity (FC) is a fundamental mechanism of neural communication, connecting distinct oscillating populations. Oscillatory networks exhibit heterogeneity across frequencies and coupling modes whose origins are not well understood, but have been suggested to involve a complex interplay of critical-like dynamics and structure-function coupling. We here utilized structural connectivity (SC) to tune a whole-brain computational model of delay-coupled damped oscillators near a Hopf bifurcation to match oscillations and FC as observed in resting-state magnetoencephalography (MEG) data. We assessed two forms of oscillation-based FC from empirical and model data, namely phase synchronization (PS) and amplitude coupling (AC). We found that both oscillations and FC best matched with empirical observations in a meta-stable regime which was characterized by small delays, realistic oscillation lifetimes, and FC with intermediate strength and high variability. How well MEG FC patterns were matched by the model varied between frequency bands and best fits were observed for high-alpha and beta-band networks. These fits could partially, but not fully, be explained by correlations with SC, implicating that both structure-function coupling and critical-like metastable dynamics underlie empirical FC, and the contributions of these mechanisms varies between different frequency bands in MEG data.

neuroscience↗

bioRxiv2024

Node centrality in MEG resting-state networks covaries with neurotransmitter receptor and transporter density

Neuronal oscillations are central mechanisms in the regulation of neuronal processing and communication (Deco et al., 2011; Fries, 2015; S. Palva & Palva, 2012; Siegel et al., 2012; Singer, 1999), but the relationship between the emergent inter-areal synchronization of oscillations and their underlying synaptic and neuromodulatory mechanisms - the dynome - has remained poorly understood (Kopell et al., 2014). While oscillations are largely generated by fast synaptic neurotransmission among pyramidal cells and interneurons (Traub et al., 2004), these microcircuits are subject to slower neuromodulation in a frequency- and region-specific manner (Batista-Brito et al., 2018; Roopun et al., 2010). While the efferent connections, receptor densities, and neurotransmitter reuptake regulation of neuromodulatory systems are highly heterogeneous across the cortical mantle (Avery & Krichmar, 2017; Deco et al., 2017; Hansen et al., 2022), it has remained unresolved how this variability shapes inter-areal connectivity of neuronal oscillations. Here, we used source-reconstructed human magnetoencephalography (MEG) data to assess how the centrality of brain areas (nodes) in large-scale networks of phase synchrony and amplitude correlations covaries with neurotransmitter receptor and transporter densities. Node centrality strongly covaried with receptor and transporter densities in a coupling- and frequency-specific manner both at the level of individual receptors and transporters and that of principal components. In delta, theta, and gamma frequencies, node centrality in phase-synchronization networks covaried positively, and in high-alpha and beta bands negatively, with dopaminergic, GABA, NMDA, muscarinic, and most serotonergic receptor densities. In amplitude-correlation networks, node centrality in delta and gamma bands covaried positively, and in theta to beta bands negatively, with most receptor and transporter densities. These results establish the contribution of neurotransmitter receptor and transporter densities for shaping connectivity of neuronal oscillations in the human brain and demonstrate a link between coupling of neuronal oscillations with the underlying biological details.

neuroscience↗

bioRxiv2019

Resting-state cross-frequency coupling networks in human electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that inter-areal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at meso-scale resolution from stereo-electroencephalography (SEEG) and at macro-scale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from non-sinusoidal signals ubiquitous in neuronal activity. We show that genuine inter-areal CFC is present in human resting-state activity in both MEG and SEEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for inter-areal CFS and PAC being two distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

neuroscience↗