Spectrally and temporally resolved estimation of neural signal diversity
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Quantifying the complexity of neural activity has provided fundamental insights into cognition,
consciousness, and clinical conditions. However, the most widely used approach to estimate the
complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that
substantially restrict its domain of applicability. In this article we leverage the information-theoretic
foundations of LZ to overcome these limitations by introducing a complexity estimator based on
state-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having a
performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial
advantages: 1) CSER offers a principled decomposition into spectral components, which allows us
to rigorously investigate the relationship between complexity and spectral power; and 2) CSER
provides a temporal resolution two orders of magnitude better than LZ, which allows complexity
analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG
datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver
of complexity changes across states of consciousness; and reveals early entropy increases that
precede 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 of
applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal
structure of the signal complexity associated with cognitive processes and conscious states.
consciousness, and clinical conditions. However, the most widely used approach to estimate the
complexity of neural dynamics, Lempel-Ziv complexity (LZ), has fundamental limitations that
substantially restrict its domain of applicability. In this article we leverage the information-theoretic
foundations of LZ to overcome these limitations by introducing a complexity estimator based on
state-space models — which we dub Complexity via State-space Entropy Rate (CSER). While having a
performance equivalent to LZ in discriminating states of consciousness, CSER boasts two crucial
advantages: 1) CSER offers a principled decomposition into spectral components, which allows us
to rigorously investigate the relationship between complexity and spectral power; and 2) CSER
provides a temporal resolution two orders of magnitude better than LZ, which allows complexity
analyses of e.g. event-locked neural signals. As a proof of principle, we use MEG, EEG and ECoG
datasets of humans and monkeys to show that CSER identifies the gamma band as the main driver
of complexity changes across states of consciousness; and reveals early entropy increases that
precede 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 of
applicability, CSER opens the door to novel investigations on the fine-grained spectral and temporal
structure of the signal complexity associated with cognitive processes and conscious states.
Date Acceptance
2023-04-18
Citation
eLife
ISSN
2050-084X
Publisher
eLife Sciences Publications Ltd
Journal / Book Title
eLife
License URL
Publication Status
Accepted