Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).

Analysis and model of cortical slow waves acquired with optical techniques / Celotto, M.; De Luca, C.; Muratore, P.; Resta, F.; Mascaro, A. L. A.; Pavone, F. S.; De Bonis, G.; Paolucci, P. S.. - In: METHODS AND PROTOCOLS. - ISSN 2409-9279. - 3:1(2020). [10.3390/mps3010014]

Analysis and model of cortical slow waves acquired with optical techniques

De Luca C.
Co-primo
;
Muratore P.
Co-primo
;
2020

Abstract

Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
2020
data analysis methods; GCamP6f; in vivo imaging; slow wave activity; spatio-temporal dynamics; toy-model simulation; wide-field microscopy
01 Pubblicazione su rivista::01a Articolo in rivista
Analysis and model of cortical slow waves acquired with optical techniques / Celotto, M.; De Luca, C.; Muratore, P.; Resta, F.; Mascaro, A. L. A.; Pavone, F. S.; De Bonis, G.; Paolucci, P. S.. - In: METHODS AND PROTOCOLS. - ISSN 2409-9279. - 3:1(2020). [10.3390/mps3010014]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1487438
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