P306 Computational investigation of wave propagation in a desynchronized network
Lluc Tresserras Pujadas*1, Leonardo Dalla Porta1, Maria V. Sanchez-Vives1,2
1Systems Neuroscience, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
2ICREA, Passeig Lluís Companys, Barcelona, Spain
*Email: tresserrasi@recerca.clinic.cat
Introduction
The cerebral cortex exhibits a rich repertoire of spontaneous spatiotemporal patterns of activity that strongly depend on the dynamical regime of the brain. Specifically, its dynamics can range from highly synchronized states (e.g., slow wave sleep), characterized by the presence of slow oscillations (SO), to more asynchronous patterns (e.g., awake states). However, under certain specific conditions, slow waves can spontaneously emerge and propagate within awake cortical network such as in cases of sleep deprivation [1], lapses of attention [2], or brain lesions [3]. Although recent studies have described this phenomenon, the mechanisms facilitating slow wave percolation on desynchronized cortical areas remain poorly understood.
Methods
To investigate this question, we employed a biophysical realistic two-dimensional computational model simulating desynchronized activity characteristic of awake states [4]. By inducing slow oscillations in a localized cortical area, we investigated how slow waves percolate into neighboring awake regions. Specifically, we examined how changes in excitatory/inhibitory balance and structural connectivity of the network can enhance or reducethe percolation of slow waves into desynchronized areas. To quantify slow wave propagation in the desynchronized network, we analyzed evoked network activity using different percolation metrics, such as the range of activation and shared information across the network.
Results
Our results indicate that increasing the proportion of long-range postsynaptic connections in excitatory neurons enhances global synchronization, facilitating the propagation of SO activity into desynchronized regions. We also examined the impact of inhibition on slow wave propagation by modulating the excitatory/inhibitory balance in the SO activity region of the network. Reducing inhibition increasedcortical excitabilityand local synchronization within the SO region, thereby enhancing the spread of slow oscillations within the desynchronized network.
Discussion
In summary, we showed that increasing the proportion of long-range excitatory connections enhances global synchronization, while reducing inhibition promotes local synchronization and neuronal excitability, both facilitating the spread of slow oscillations into desynchronized areas. These findings are further supported with the use of different percolation metrics reinforcing the idea that structural and functional properties of the network play a crucial role in determining cortical vulnerability to slow wave percolation. Together, our results are a first step in mechanistically understanding the dynamical changes that occur in the lesioned brain and their underlying mechanisms, offering a path to the development of future therapeutic strategies for neurologic disorder.
Acknowledgements
Funded by PID2020-112947RB-I00 financed by MCIN/ AEI /10.13039/501100011033 and by European Union (ERC, NEMESIS, project number 101071900) to MVSV and PRE2021-101156 financed by the Spanish Ministry of Science and Innovation.
References
[1]Vyazovskiy, V. V., et al. (2011).Local sleep in awake rats.Nature,472, 443-447.
[2]Andrillon, T., et al. (2021). Predicting lapses of attention with sleep-like slow waves.Nat Commun,12, 3657.
[3]Massimini, M., et al. (2024). Sleep-like cortical dynamics during wakefulness and their network effects following brain injury.Nat Commun,15, 7207.
[4]Barbero-Castillo, A., et al.(2021). Impact of GABAAand GABABinhibition on cortical dynamics and perturbational complexity during synchronous and desynchronized states.J Neurosci,41, 5029-5044.
