Analysis Pipeline for Extracting Features of Cortical Slow Oscillations

Abstract

Cortical slow oscillations (≲1 Hz) are an emergent property of the cortical network that integrate connectivity and physiological features. This rhythm, highly revealing of the characteristics of the underlying dynamics, is a hallmark of low complexity brain states like sleep, and represents a default activity pattern. Here, we present a methodological approach for quantifying the spatial and temporal properties of this emergent activity. We improved and enriched a robust analysis procedure that has already been successfully applied to both in vitro and in vivo data acquisitions. We tested the new tools of the methodology by analyzing the electrocorticography (ECoG) traces recorded from a custom 32-channel multi-electrode array in wild-type isoflurane-anesthetized mice. The enhanced analysis pipeline, named SWAP (Slow Wave Analysis Pipeline), detects Up and Down states, enables the characterization of the spatial dependency of their statistical properties, and supports the comparison of different subjects. The SWAP is implemented in a data-independent way, allowing its application to other data sets (acquired from different subjects, or with different recording tools), as well as to the outcome of numerical simulations. By using the SWAP, we report statistically significant differences in the observed slow oscillations (SO) across cortical areas and cortical sites. Computing cortical maps by interpolating the features of SO acquired at the electrode positions, we give evidence of gradients at the global scale along an oblique axis directed from fronto-lateral toward occipito-medial regions, further highlighting some heterogeneity within cortical areas. The results obtained using the SWAP will be essential for producing data-driven brain simulations. A spatial characterization of slow oscillations will also trigger a discussion on the role of, and the interplay between, the different regions in the cortex, improving our understanding of the mechanisms of generation and propagation of delta rhythms and, more generally, of cortical properties.

Modulation of cortical slow oscillatory rhythm by GABAB receptors: an in vitro experimental and computational study

“…2010; De Bonis et al . 2019). Singled‐out sets of Up and Down state durations from each recording were used to estimate the different parameters reported in the study.…”

“…Up and Down states were singled out by setting a threshold in the log(MUA) time series. The threshold was set to 60% of the interval between the peaks in the bimodal distributions of log(MUA) corresponding to Up and Down states (Reig et al 2010;Sanchez-Vives et al 2010;De Bonis et al 2019). Singled-out sets of Up and Down state durations from each recording were used to estimate the different parameters reported in the study.…”

Detection of neuronal OFF periods as low amplitude neural activity segments

“…More sophisticated methods for detecting ON/OFF periods in continuous neuronal activity signals can broadly be classified as ‘threshold-crossing’ or ‘predictive’ algorithms. ‘Threshold-crossing’ algorithms work by processing the data until a bimodal distribution is obtained upon which a threshold is applied to separate data between ON and OFF periods (Mukovski et al, 2007, De Bonis et al, 2019, Dasilva et al, 2021). ‘Predictive’ algorithms assume bimodality and assign data to one of either state based on the probability of a predictive model fitted to the data (Seamari et al, 2007; MacFarland et al, 2011; Jercog et al, 2017).…”

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

“…Also, we are extending the model to multi-layers and multi-area descriptions. This work represents an additional contribution to understanding sleep mechanisms and functions, in line with the efforts we are carrying out in data analysis 43,44 and in large-scale simulations 45 , aimed at bridging different elements in a multi-disciplinar approach.…”