@article{lom21,
author = {Lombardi F, and Shriki O, and Herrmann H. J., and de ArcangelisL,},
pages = {657-666},
title = {Long-range Temporal Correlations in the Broadband Resting state Activity of the Human Brain revealed by Neuronal Avalanches},
volume = {461},
note = {Articolo in rivista},
issn = {0925-2312},
journal = {NEUROCOMPUTING},
doi = {10.1016/j.neucom.2020.05.126},
year = {2021},
abstract = {Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of
characteristic size. Such evidence has been interpreted in the context of criticality and associated with
the normal functioning of the brain. A distinctive attribute of systems at criticality is the presence of
long-range correlations. Thus, to verify the hypothesis that the brain operates close to a critical point
and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance
to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused
on the analysis of narrow-band electroencephalography (EEG) and magnetoencephalography (MEG) signal
amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations.
However, brain activity is a broadband phenomenon, and a significant piece of information useful
to precisely discriminate between normal (critical) and pathological behavior (non-critical), may be
encoded in the broadband spatio-temporal cortical dynamics. Here we propose to characterize the temporal
correlations in the broadband brain activity through the lens of neuronal avalanches. To this end,
we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche
sequences, and study their temporal correlations. We demonstrate that the broadband restingstate
brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings,
with similar values of the scaling exponents. Importantly, although we observe that the avalanche
size distribution depends on scale parameters, scaling exponents characterizing long-range correlations
are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating
that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches
constitute a fundamental feature of neural systems with universal characteristics, the proposed
approach may serve as a general, systems- and experiment-independent procedure to infer the existence
of underlying long-range correlations in extended neural systems, and identify pathological behaviors in
the complex spatio-temporal interplay of cortical rhythms.}
}