Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques

Abstract

Slow waves (SWs) occur both during natural sleep and anesthesia and are universal 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. 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 "de novo" a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs in the frequency band [0.5, 4] Hz. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural 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. Comparison of preliminary experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).

Cortical propagation tracks functional recovery after stroke

“…sources and sinks) but do not deal explicitly with temporal order. Our methods used here contain some similarities but also crucial differences with a very recent analysis performed on cortical slow waves in anesthetized mice [ 38 ]. On the one hand, both algorithms apply exactly the same criteria of unicity and globality in the identification of the spatiotemporal patterns.…”

“…On PLOS COMPUTATIONAL BIOLOGY the other hand, the core of our global event detection is automated spike matching (via adaptive coincidence detection) while the wavehunt pipeline relies on an iterative procedure to cut the time series into distinctive waves. Apart from the different types of data, the two studies are also complementary in scope: while the focus of [38] lies on the excitability of the neuronal population, the dominant origin points and the velocity of the slow waves, we perform a thorough investigation of the spatial propagation pattern of each global event.…”

Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep

“…Finally, this work represents an additional contribution in understanding sleep mechanisms and functions, in line with the efforts we are carrying out in data analysis [ 53 , 54 ] and in large-scale simulations [ 55 ], aimed at bridging different elements in a multi-disciplinar approach. In particular, it hints to a careful balance between architectural abstraction and experimental observations as a valid methodology for the description of brain mechanisms and of their links with cognitive functions.…”

Analysis Pipeline for Extracting Features of Cortical Slow Oscillations

“…Moreover, several improvements are ongoing, focusing on the reliability and robustness of the analysis algorithms when increasing the number of channels (electrodes or pixels), as in recently developed multi-electrode arrays and in optical imaging data. The latter in particular is a case where we have already successfully applied our protocol (see Celotto et al, 2018, calcium-imaging data, GCaMP6f model, submitted for publication). In general, the approach we follow can be extended provided that single-channel records allow the reliable disentanglement of Up and Down states of the underlying cortical assemblies.…”