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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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Entropic time-courses and time-frequency spectrograms are given for the visual, left motor, left fronto-parietal and left insula regions.The upper panel shows results for the Sternberg task and the lower panel shows results for the RM task. For entropic timecourses, the blue line shows change in entropy from baseline level; the shaded region shows standard error across subjects. Time-frequency spectrograms show deviation from resting state oscillatory amplitude, with red and blue showing increased and decreased oscillatory amplitude respectively. Note that both tasks elicit transient changes in signal entropy. Note also the differences in temporal profile of entropy across brain regions.
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pone.0120991.g005: Entropic time-courses and time-frequency spectrograms are given for the visual, left motor, left fronto-parietal and left insula regions.The upper panel shows results for the Sternberg task and the lower panel shows results for the RM task. For entropic timecourses, the blue line shows change in entropy from baseline level; the shaded region shows standard error across subjects. Time-frequency spectrograms show deviation from resting state oscillatory amplitude, with red and blue showing increased and decreased oscillatory amplitude respectively. Note that both tasks elicit transient changes in signal entropy. Note also the differences in temporal profile of entropy across brain regions.

Mentions: Fig 5 shows entropic time-courses and time-frequency spectrograms for 4 of the 12 regions identified in Fig 4. In the entropy timecourses, the zero-line (black solid line) represents resting level entropy (computed within a rest block), and so timecourses show transient change in entropy induced by the task, relative to baseline. Similarly, the time frequency spectrograms are also plotted relative to baseline, with blue representing decreased oscillatory power with respect to rest and red/yellow representing increased oscillatory power.


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)

Entropic time-courses and time-frequency spectrograms are given for the visual, left motor, left fronto-parietal and left insula regions.The upper panel shows results for the Sternberg task and the lower panel shows results for the RM task. For entropic timecourses, the blue line shows change in entropy from baseline level; the shaded region shows standard error across subjects. Time-frequency spectrograms show deviation from resting state oscillatory amplitude, with red and blue showing increased and decreased oscillatory amplitude respectively. Note that both tasks elicit transient changes in signal entropy. Note also the differences in temporal profile of entropy across brain regions.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0120991.g005: Entropic time-courses and time-frequency spectrograms are given for the visual, left motor, left fronto-parietal and left insula regions.The upper panel shows results for the Sternberg task and the lower panel shows results for the RM task. For entropic timecourses, the blue line shows change in entropy from baseline level; the shaded region shows standard error across subjects. Time-frequency spectrograms show deviation from resting state oscillatory amplitude, with red and blue showing increased and decreased oscillatory amplitude respectively. Note that both tasks elicit transient changes in signal entropy. Note also the differences in temporal profile of entropy across brain regions.
Mentions: Fig 5 shows entropic time-courses and time-frequency spectrograms for 4 of the 12 regions identified in Fig 4. In the entropy timecourses, the zero-line (black solid line) represents resting level entropy (computed within a rest block), and so timecourses show transient change in entropy induced by the task, relative to baseline. Similarly, the time frequency spectrograms are also plotted relative to baseline, with blue representing decreased oscillatory power with respect to rest and red/yellow representing increased oscillatory power.

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