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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Syst. Neurosci. | doi: 10.3389/fnsys.2019.00070

Analysis pipeline for extracting features ofcortical slow oscillations

  • 1National Institute of Nuclear Physics (INFN), Italy
  • 2August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Spain
  • 3National Institute of Health (ISS), Italy

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 Waves 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 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 towards occipito-medial regions, further highlighting some heterogeneity within cortical areas.
The results obtained using 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. ​

Keywords: slow-wave activity, slow oscillations, analysis pipeline, software tools, cortical areas, Multi-Electrode Arrays (MEAs), Multi-Unit Activity (MUA), Anesthetized mice

Received: 08 Mar 2019; Accepted: 05 Nov 2019.

Copyright: © 2019 De Bonis, Dasilva, Pazienti, Sanchez-Vives, Mattia and Paolucci. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dr. Giulia De Bonis, National Institute of Nuclear Physics (INFN), Rome, Italy,
Prof. Maria V. Sanchez-Vives, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, 08036, Catalonia, Spain,