Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics

Mackay, Michael; Huo, Siyu; Kaiser, Marcus

doi:10.1371/journal.pcbi.1011349

Cited by 5 publications

(1 citation statement)

References 56 publications

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“…More recently, conduction delays between brain areas were associated with metastable oscillations on a macroscopic spatial scale ( Cabral et al, 2022 ). Many possible mechanisms of metastability have been proposed ( Kelso, 1996 ; Shanahan, 2008 ; Brinkman et al, 2022 ; Hancock et al, 2023 ; Mackay et al, 2023 ). However, the biophysical features of neuronal networks underlying metastability at the level of neurons have not been fully elucidated.…”

Section: Introductionmentioning

confidence: 99%

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Venkadesh,

Shaikh,

Shakeri

et al. 2024

Front. Netw. Physiol.

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Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons’ intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

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Section: Introductionmentioning

confidence: 99%

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Venkadesh,

Shaikh,

Shakeri

et al. 2024

Front. Netw. Physiol.

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Network structure and time delays shape synchronization patterns in brain network models

Pinder,

Nelson,

Crofts

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In this paper, we investigate synchronization patterns and coherence for a network of delayed Wilson–Cowan nodes. To capture information processing across different brain regions, our model incorporates two distinct delays: an intra-nodal delay that reflects the time signals take to travel within a cortical region due to local circuitry and an inter-nodal delay representing the longer communication times associated with white matter connections between brain areas. To investigate the role of network topology, we consider a range of toy network structures as well as the known (macro-scale) cortical structure of the Macaque monkey. We examine how global network dynamics are shaped by a combination of network configuration, coupling strength, and time delays. Our focus lies on two dynamic measures: synchrony and metastability, the latter reflecting the temporal variation of the former, both crucial for the brain’s real-time functionality. Our investigation identifies extensive regions within the system’s parameter space where the synchronized state exhibits transverse instabilities. These instabilities give rise to diverse dynamical behaviors contingent upon the network architecture and the interplay between coupling strength and time delay. While similar complex partially synchronized states existed for all network topologies considered, the cortical network demonstrated time-dependent behaviors, such as phase cluster dynamics, which were absent in the toy network architectures, and which are considered crucial in its ability to orchestrate complex information processing and behavior. Additionally, we illustrate how delays can regulate a cortical network with chaotic local dynamics, thus emphasizing the potential importance of delays in suppressing pathological spreading dynamics.

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Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework

Donaire,

Padilla,

Escrichs

et al. 2024

Cerebral Cortex

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This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain’s functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient’s brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

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Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics

Cited by 5 publications

References 56 publications

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Network structure and time delays shape synchronization patterns in brain network models

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework

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