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Complexity measures in magnetoencephalography: measuring "disorder" in schizophrenia.

Brookes MJ, Hall EL, Robson SE, Price D, Palaniyappan L, Liddle EB, Liddle PF, Robinson SE, Morris PG - PLoS ONE (2015)

Bottom Line: These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal.We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary.We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network.

View Article: PubMed Central - PubMed

Affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.

ABSTRACT
This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).

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Related in: MedlinePlus

Schematic diagram detailing the calculation of Rank Vector Entropy.
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pone.0120991.g002: Schematic diagram detailing the calculation of Rank Vector Entropy.

Mentions: Rank Vector Entropy (RVE) is a computationally efficient and high signal to noise algorithm recently introduced by Robinson et al. [25]. It combines symbolic entropy with a leaky integrator in order to transform timecourses of electrophysiological activity into a timecourse showing the temporal evolution of entropy. Note that RVE is nonparametric and is independent of source amplitude. The procedure is depicted in Fig 2.


Complexity measures in magnetoencephalography: measuring "disorder" in schizophrenia.

Brookes MJ, Hall EL, Robson SE, Price D, Palaniyappan L, Liddle EB, Liddle PF, Robinson SE, Morris PG - PLoS ONE (2015)

Schematic diagram detailing the calculation of Rank Vector Entropy.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401778&req=5

pone.0120991.g002: Schematic diagram detailing the calculation of Rank Vector Entropy.
Mentions: Rank Vector Entropy (RVE) is a computationally efficient and high signal to noise algorithm recently introduced by Robinson et al. [25]. It combines symbolic entropy with a leaky integrator in order to transform timecourses of electrophysiological activity into a timecourse showing the temporal evolution of entropy. Note that RVE is nonparametric and is independent of source amplitude. The procedure is depicted in Fig 2.

Bottom Line: These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal.We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary.We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network.

View Article: PubMed Central - PubMed

Affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.

ABSTRACT
This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).

Show MeSH
Related in: MedlinePlus