The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model

Multi-state metastability in neuroimaging signals reflects the brain's flexibility to transition between network configurations in response to changing environments or tasks. We modeled these dynamics with a Kuramoto network of 90 nodes oscillating at an intrinsic frequency of 40 Hz, interconnected using human brain structural connectivity strengths and delays. We simulated this model for 30 min to generate multi-state metastability. We identified global coupling and delay parameters that maximize spectral entropy, a proxy for multi-state metastability. At this operational point, multiple frequency-specific coherent sub-networks spontaneously emerge across oscillatory modes, persisting for periods between 140 and 4300 ms, reflecting flexible and sustained dynamic states. The topography of these sub-networks aligns with empirical resting-state neuroimaging data. Additionally, periodic components of the EEG spectra from young healthy participants correlate with maximal multi-state metastability, while dynamics away from this point correlate with sleep and anesthesia spectra. Our findings suggest that multi-state metastable functional dynamics observed in empirical data emerge from specific interactions of structural topography and connection delays, providing a platform to study mechanisms underlying flexible dynamics of cognition.

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Neuronal oscillations are ubiquitous in brain activity at all scales and their synchronization dynamics are essential for information processing in neuronal systems. The underlying synaptic mechanisms, while mainly based on GABA- and glutamatergic neurotransmission, are influenced by neuromodulatory systems that have highly variable densities of neurotransmitter receptors and transporters across the cortical mantle. How they constrain the network structures of interacting oscillations has remained a central unaddressed question. We asked here whether the receptor and transporter densities covary with the frequency-specific neuroanatomical patterns of inter-areal phase synchrony (PS) and amplitude correlation (AC) networks in resting-state magnetoencephalography (MEG) data. Network centrality in delta and gamma frequencies covaried positively with GABA-, NMDA-, dopaminergic-, and most serotonergic receptor and transporter densities while covariance was negative in alpha and beta bands. These results show that local receptor microarchitecture shapes macro-scale oscillation networks in spectrally specific patterns.

Brain anatomy and dynamics: A commentary on "Does the brain behave like a (complex) network? I. Dynamics" by Papo and Buldú (2024)

Papo and Buldú [1] ask whether the brain truly acts as a network, or whether it is a convenient coincidence that it can be described with the tools of complex network theory, and the emerging field of network neuroscience. After a broad ranging discussion of networkness they explore some of the ways in which the combination of brain structure and dynamics can indeed better be understood as realising a complex network that subserves brain function. To complement and bolster this perspective, which is informed largely from a physics viewpoint, we direct the reader to additional tools, approaches and insights available from applied mathematics that may further help address some of the many remaining open challenges in this field.