  Cortical slow oscillations are an emergent property of the cortical network,
a hallmark of low complexity brain states like sleep, and represent 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 on spatial characterization of slow oscillations will be essential for
producing data-driven brain simulations and for triggering 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.
