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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

The relationship between neural oscillatory amplitude and signal entropy in the visual, motor and insula cortices.Note the general trend that entropy exhibits a negative correlation with alpha and beta oscillations and a positive correlation with gamma oscillations. Inset images show the spatial maps of the regions used. The mean correlation across subjects is shown and error bars show standard deviation across subjects.
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pone.0120991.g006: The relationship between neural oscillatory amplitude and signal entropy in the visual, motor and insula cortices.Note the general trend that entropy exhibits a negative correlation with alpha and beta oscillations and a positive correlation with gamma oscillations. Inset images show the spatial maps of the regions used. The mean correlation across subjects is shown and error bars show standard deviation across subjects.

Mentions: The majority of MEG experiments are processed via exposition of the time-frequency signature of neural oscillations. Comparing entropic timecourses to the spectrograms in Fig 5, it is clear that a relationship exists, with decreases in oscillatory amplitude in the alpha and beta band accompanied by a transient increase in entropy. Given that oscillations and entropy are generated from the same underlying beamformer projected timecourses, coupled with the fact that oscillations are highly predictable and therefore low entropy, this would be expected. However, it proves instructive to probe this relationship further by comparing the time-courses of change in oscillatory amplitude across the frequency spectrum, to the entropic time-courses. Such comparison reveals a non-linear relationship, summarised in Fig 6, which shows temporal correlation between the Hilbert envelope of oscillations and the entropy timecourses. Note that the relationship exhibits a general trend with a negative correlation between entropy and alpha and beta band oscillatory amplitude, and a positive correlation at higher frequency. Note also a weak negative correlation in the delta and theta range. An effect of signal to noise ratio (SNR) is apparent: in general, the SNR of alpha and beta band oscillations is higher than that of gamma oscillations. This probably explains the higher (absolute) correlation between entropy and alpha and beta oscillations, compared to gamma oscillations. Most importantly, we note that that no simple one-to-one relationship exists between the entropic timecourse and oscillatory power in any one frequency band.


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)

The relationship between neural oscillatory amplitude and signal entropy in the visual, motor and insula cortices.Note the general trend that entropy exhibits a negative correlation with alpha and beta oscillations and a positive correlation with gamma oscillations. Inset images show the spatial maps of the regions used. The mean correlation across subjects is shown and error bars show standard deviation across subjects.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0120991.g006: The relationship between neural oscillatory amplitude and signal entropy in the visual, motor and insula cortices.Note the general trend that entropy exhibits a negative correlation with alpha and beta oscillations and a positive correlation with gamma oscillations. Inset images show the spatial maps of the regions used. The mean correlation across subjects is shown and error bars show standard deviation across subjects.
Mentions: The majority of MEG experiments are processed via exposition of the time-frequency signature of neural oscillations. Comparing entropic timecourses to the spectrograms in Fig 5, it is clear that a relationship exists, with decreases in oscillatory amplitude in the alpha and beta band accompanied by a transient increase in entropy. Given that oscillations and entropy are generated from the same underlying beamformer projected timecourses, coupled with the fact that oscillations are highly predictable and therefore low entropy, this would be expected. However, it proves instructive to probe this relationship further by comparing the time-courses of change in oscillatory amplitude across the frequency spectrum, to the entropic time-courses. Such comparison reveals a non-linear relationship, summarised in Fig 6, which shows temporal correlation between the Hilbert envelope of oscillations and the entropy timecourses. Note that the relationship exhibits a general trend with a negative correlation between entropy and alpha and beta band oscillatory amplitude, and a positive correlation at higher frequency. Note also a weak negative correlation in the delta and theta range. An effect of signal to noise ratio (SNR) is apparent: in general, the SNR of alpha and beta band oscillations is higher than that of gamma oscillations. This probably explains the higher (absolute) correlation between entropy and alpha and beta oscillations, compared to gamma oscillations. Most importantly, we note that that no simple one-to-one relationship exists between the entropic timecourse and oscillatory power in any one frequency band.

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