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Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia.

Schartner M, Seth A, Noirhomme Q, Boly M, Bruno MA, Laureys S, Barrett A - PLoS ONE (2015)

Bottom Line: Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions).In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation.We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels.

View Article: PubMed Central - PubMed

Affiliation: Sackler Centre for Consciousness Science, Department of Informatics, University of Sussex, Brighton, United Kingdom.

ABSTRACT
Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct 'flavours' of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

No MeSH data available.


Schematic of the computation of Lempel-Ziv complexity LZc.a) xi is the activity of the ith EEG electrode, i.e. the ith channel, and ai is the (Hilbert) amplitude of xi. b) bi is binarized ai, using the mean activity of ai as binarisation threshold. c) After binarisation of all n channels, successive n × 1 dimensional observations are concatenated to obtain one binary sequence s in which patterns are searched and listed into a dictionary of binary words via a Lempel-Ziv algorithm. d) Lempel-Ziv complexity LZc is proportional to the size of this dictionary.
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pone.0133532.g003: Schematic of the computation of Lempel-Ziv complexity LZc.a) xi is the activity of the ith EEG electrode, i.e. the ith channel, and ai is the (Hilbert) amplitude of xi. b) bi is binarized ai, using the mean activity of ai as binarisation threshold. c) After binarisation of all n channels, successive n × 1 dimensional observations are concatenated to obtain one binary sequence s in which patterns are searched and listed into a dictionary of binary words via a Lempel-Ziv algorithm. d) Lempel-Ziv complexity LZc is proportional to the size of this dictionary.

Mentions: For a given segment of data, Lempel-Ziv complexity quantifies complexity by counting the number of distinct patterns of activity in the data. It can be thought of as being proportional to the size of a computer file containing the data, after applying a compression algorithm. Computing the Lempel-Ziv compressibility of data requires a binarization of the multidimensional time series. Casali et al [21] computed this measure on event-related, as opposed to resting data, and so used a threshold relative to pre-stimulus activity (baseline) to define the binarization. Here our threshold was based on the instantaneous amplitude of the Hilbert transform, i.e. the absolute value of the analytic signal of the channel’s time series. The threshold Ti for the ith channel was chosen as the mean of the absolute value of the analytic signal of the ith channel. The data segment is then treated as a binary matrix, with rows corresponding to channels (time series) and columns corresponding to time (observations). A Lempel-Ziv compression algorithm obtains a list of words (binary subsequences that appear at least once) in the data matrix, as sketched in Fig 3. The Lempel-Ziv complexity is then proportional to the number of binary words. The greater the degree of randomness, the greater the number of different sub-sequences that will be present, and thus the higher the Lempel-Ziv complexity.


Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia.

Schartner M, Seth A, Noirhomme Q, Boly M, Bruno MA, Laureys S, Barrett A - PLoS ONE (2015)

Schematic of the computation of Lempel-Ziv complexity LZc.a) xi is the activity of the ith EEG electrode, i.e. the ith channel, and ai is the (Hilbert) amplitude of xi. b) bi is binarized ai, using the mean activity of ai as binarisation threshold. c) After binarisation of all n channels, successive n × 1 dimensional observations are concatenated to obtain one binary sequence s in which patterns are searched and listed into a dictionary of binary words via a Lempel-Ziv algorithm. d) Lempel-Ziv complexity LZc is proportional to the size of this dictionary.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4529106&req=5

pone.0133532.g003: Schematic of the computation of Lempel-Ziv complexity LZc.a) xi is the activity of the ith EEG electrode, i.e. the ith channel, and ai is the (Hilbert) amplitude of xi. b) bi is binarized ai, using the mean activity of ai as binarisation threshold. c) After binarisation of all n channels, successive n × 1 dimensional observations are concatenated to obtain one binary sequence s in which patterns are searched and listed into a dictionary of binary words via a Lempel-Ziv algorithm. d) Lempel-Ziv complexity LZc is proportional to the size of this dictionary.
Mentions: For a given segment of data, Lempel-Ziv complexity quantifies complexity by counting the number of distinct patterns of activity in the data. It can be thought of as being proportional to the size of a computer file containing the data, after applying a compression algorithm. Computing the Lempel-Ziv compressibility of data requires a binarization of the multidimensional time series. Casali et al [21] computed this measure on event-related, as opposed to resting data, and so used a threshold relative to pre-stimulus activity (baseline) to define the binarization. Here our threshold was based on the instantaneous amplitude of the Hilbert transform, i.e. the absolute value of the analytic signal of the channel’s time series. The threshold Ti for the ith channel was chosen as the mean of the absolute value of the analytic signal of the ith channel. The data segment is then treated as a binary matrix, with rows corresponding to channels (time series) and columns corresponding to time (observations). A Lempel-Ziv compression algorithm obtains a list of words (binary subsequences that appear at least once) in the data matrix, as sketched in Fig 3. The Lempel-Ziv complexity is then proportional to the number of binary words. The greater the degree of randomness, the greater the number of different sub-sequences that will be present, and thus the higher the Lempel-Ziv complexity.

Bottom Line: Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions).In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation.We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels.

View Article: PubMed Central - PubMed

Affiliation: Sackler Centre for Consciousness Science, Department of Informatics, University of Sussex, Brighton, United Kingdom.

ABSTRACT
Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct 'flavours' of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.

No MeSH data available.