In this section
@article{Martinez:2023,
author = {Martinez, Mediano P and Rosas, FE and Luppi, AI and Noreika, V and Seth, AK and Carhart-Harris, RL and Barnett, L and Bor, D},
journal = {eLife},
title = {Spectrally and temporally resolved estimation of neural signal diversity},
year = {2023}
}
TY - JOUR
AB - Quantifying the complexity of neural activity has provided fundamental insights into cognition,consciousness, and clinical conditions. However, the most widely used approach to estimate thecomplexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations thatsubstantially restrict its domain of applicability. In this article we leverage the information-theoreticfoundations of LZ to overcome these limitations by introducing a complexity estimator based onstate-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having aperformance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucialadvantages: 1) CSER offers a principled decomposition into spectral components, which allows usto rigorously investigate the relationship between complexity and spectral power; and 2) CSERprovides a temporal resolution two orders of magnitude better than LZ, which allows complexityanalyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoGdatasets of humans and monkeys to show that CSER identifies the gamma band as the main driverof complexity changes across states of consciousness; and reveals early entropy increases thatprecede the standard ERP in an auditory mismatch negativity paradigm by approximately 20ms.Overall, by overcoming the main limitations of LZ and substantially extending its range ofapplicability, CSER opens the door to novel investigations on the fine-grained spectral and temporalstructure of the signal complexity associated with cognitive processes and conscious states.
AU - Martinez,Mediano P
AU - Rosas,FE
AU - Luppi,AI
AU - Noreika,V
AU - Seth,AK
AU - Carhart-Harris,RL
AU - Barnett,L
AU - Bor,D
PY - 2023///
SN - 2050-084X
TI - Spectrally and temporally resolved estimation of neural signal diversity
T2 - eLife
ER -
