P052 Geometry and dynamics in sinusoidally perturbed cortical networks
Martina Cortada*1,2, Joana Covelo1, Maria V. Sanchez-Vives1,3
1Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Carrer de Rosselló, 149-153, 08036 Barcelona, Spain
2Facultat de Física, Universitat de Barcelona (UB), Carrer de Martí i Franquès, 1-11, 08028 Barcelona,
Spain
3Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys, 23,
08010 Barcelona, Spain
*Email: cortada@recerca.clinic.cat
Introduction
Cerebral cortex networks exemplify complex coupled systems where collective behaviorsgiverise to emergent properties.This study explores how electric fieldsinusoidal modulationimpactcortical networksexhibitingself-sustained slow oscillations (SOs), characterized by alternating neuronal silence (Down states) and activity (Up states) around 1 Hz[1,2].SOs, describedas thecorticaldefaultactivity pattern[3],arecrucial formemory consolidation,plasticityandhomeostasis[4].
Here, we aimed to understand SOs and how to control them. Specifically, how the amplitudeand frequency of sinusoidal electric fields shape emergent network states and the transitions across them?
Methods
Wevariedfrequencies and amplitudesof sinusoidal fieldson cortical networksexhibitingspontaneous SOs. These networks form a coupled system where intrinsic oscillations interact with an external periodic force. To characterize their response, we define a suitably reduced phase space in which trajectoriesemergefrom the interaction between the perturbation and the network’s activity. These trajectories are constructed by segmenting the network response into single-cycle epochs corresponding to the perturbation, mapping each oscillatory response into a structured, low-dimensional representation. The system’s behavior is then analyzed through the evolution of these trajectories within this phase space, using geometric and topological approaches.
Results
When sinusoidally perturbed, these networksexhibitdistinct qualitative behaviors shaped by the interplay between intrinsic oscillations and external driving forces. By examining thetrajectoriesrepresentingthis interplay, we found that the Euclidean distance between their start and end points distinguishes different dynamical regimes, including phase and frequencylocking, quasi-periodicityand desynchronization.
Beyond trajectory closure, the intricate patterns of these curves across stimulation conditionsindicatethe existence of multiple stable or metastable regimes, suggesting that external forcing can drive transitions between distinct attractor-like states in cortical dynamics.
Discussion
Through this analysis, we have explored how perturbations shape network responses across the parameter space. Our findings suggest that cortical networks encode these effects through the geometric structure of their dynamical trajectories, revealing patterns of entrainment and stability under electric field modulation. This framework deepens our understanding of coupled neural oscillators and how they can be controlled, which has important implications for neuromodulation strategies in clinical contexts.
Acknowledgements
Funded by INFRASLOWPID2023-152918OB-I00 funded by MICIU / AEI / 10.13039/501100011033/FEDER. Co-funded byEuropean Union (ERC, NEMESIS, project number 101071900)andDepartamentdeRecercaiUniversitatsde laGeneralitatde Catalunya (AGAUR 2021-SGR-01165), supported by FEDER.
References
[1] M.V. Sanchez-Vives.Current Opinion in Physiology, vol. 15, 2020, pp. 217–223.
[2] M.Torao-Angosto et al.Frontiers in Systems Neuroscience, vol. 15, 2021.
[3] M.V. Sanchez-Vives and M. Mattia.ArchivesItaliennesdeBiologie, vol. 158, no. 1, 2020, pp. 59–65.doi:10.12871/000398292020112.
[4] J.M. Krueger et al.Sleep Medicine Reviews, vol. 28, 2016, pp. 46–54.
