Looking forward to present you my work here: https://meet.google.com/epq-nghf-xue
Abstract: Alessandra Camassa, Andrea Galluzzi, Miguel Dasilva, Beatriz Rebollo, Maurizio Mattia, Maria V. Sanchez-Vives
The cerebral cortex as a structured network is able to spontaneously express different types of dynamics that are continuously changing over time according to the ongoing brain state. Transitions across brain states correlate with changes in network excitability and functional connectivity giving rise to a wide repertoire of spatiotemporal patterns of neuronal activity [1]. The quasi-periodic occurrence of travelling waves - namely slow-wave activity (SWA) - characterizes the cortical networks under unconscious brain states. The spatiotemporal patterns generated under SWA are shaped by the structure and excitability of the underlying network [2,3]. Thus, the emergent wavefronts portray the characteristics of the dynamical regime under which they have been spawn. Here we aimed to develop novel analytical methods to capture wave propagation features and to identify the universal fingerprints of the cortical network activity generated by different preparations all spontaneously expressing SWA, in order to gain a deeper understanding of functional mechanisms underlying the cortical network organization. To do so, we studied the spatiotemporal dynamics of the cortex under SWA in three different frameworks: _in vivo_ , performing extracellular recordings of cortical activity in deeply anesthetized mice with a superficial multielectrode array; _in vitro_ , recording the electrophysiological signals from cortical slices cut from ferret visual cortex; _in silico_ , in a simulated multimodular network of spiking neurons [2,4]. We studied network dynamics by characterizing the spatiotemporal patterns of propagation of the activation wavefronts developing a phase-based method that allow an accurate reconstruction of the waves travelling across the cortex both in experimental and simulated data [5]. We complemented the study of network dynamics with the computation of network synchronization over time, evaluating the variability of ongoing synchrony fluctuations that entail dynamically changing states, in our case Up and Down states of SWA. Finally, we evaluated the dynamical richness of the cortical activity by estimating the dimensionality of the system dynamics over time. We adopted an approach drawn from experimental fluid dynamics in physics [6]. Applying an empirical eigenfunction approach by means of the algorithm of Singular Value Decomposition (SVD) it is possible to quantify the instantaneous energy of the system and its effective dimension, and to study the evolution of the system dimension over time as well as its dependence on the structure and on the dynamical state of the system. In this way, we were able to compare the mechanistic underpinning of SWA when the intact cortex is functionally disconnected ( _in vivo_ under deep anesthesia) and when it is anatomically disconnected fromt he rest of the brain ( _in vitro_ in cortical slices), and finally exploiting the model, to emphasize the universal nature of this slow rhythm highlighting both the differences and similarities between experimental conditions.
Acknowledgements
Founded by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3) and by MINECO grant BFU2017-85048-R.
References
[1] Stitt et al., _Sci. Rep._ **7(1)** , 1 (2017).
