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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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Spatial signatures of 12 independent components, derived from ICA applied to entropic transformation of MEG data concatenated across all subjects, and tasks.Regions identified include the visual cortex, the left and right motor cortices, cingulo-insula cortex, pre-motor cortex, the left and right fronto-parietal networks, the left and right temporo-parietal junction and the left and right insula cortices. This represents a unique method to parcellate the cortex, based upon the temporal signature of entropy. Images are displayed in radiological convention.
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pone.0120991.g004: Spatial signatures of 12 independent components, derived from ICA applied to entropic transformation of MEG data concatenated across all subjects, and tasks.Regions identified include the visual cortex, the left and right motor cortices, cingulo-insula cortex, pre-motor cortex, the left and right fronto-parietal networks, the left and right temporo-parietal junction and the left and right insula cortices. This represents a unique method to parcellate the cortex, based upon the temporal signature of entropy. Images are displayed in radiological convention.

Mentions: The RVE/ICA method yielded a spatial basis set, which is shown in Fig 4. The functional maps depict the spatial signatures of 12 temporally independent components which were selected for further analysis. Each of these maps represents a brain region (or collection of brain regions) that generates an independent temporal evolution of entropy. In this way, we allow spatial parcellation of the cortex, based upon its functional anatomy. The regions selected are well characterised and include visual cortex, motor cortex, cingulo-insula region, pre-motor cortex, the left and right fronto-parietal networks, the tempero-parietal junction and the left and right insula cortices. This spatial basis set was employed as a platform upon which all subsequent analyses were based. It is important to note that this approach offers significant advantages over voxel based metrics. Specifically, ICA allows us to decompose several thousand voxel timecourses into a smaller number of independent components, reducing multiple comparison problems that inevitably arise in subsequent statistical testing (see also discussion).


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)

Spatial signatures of 12 independent components, derived from ICA applied to entropic transformation of MEG data concatenated across all subjects, and tasks.Regions identified include the visual cortex, the left and right motor cortices, cingulo-insula cortex, pre-motor cortex, the left and right fronto-parietal networks, the left and right temporo-parietal junction and the left and right insula cortices. This represents a unique method to parcellate the cortex, based upon the temporal signature of entropy. Images are displayed in radiological convention.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0120991.g004: Spatial signatures of 12 independent components, derived from ICA applied to entropic transformation of MEG data concatenated across all subjects, and tasks.Regions identified include the visual cortex, the left and right motor cortices, cingulo-insula cortex, pre-motor cortex, the left and right fronto-parietal networks, the left and right temporo-parietal junction and the left and right insula cortices. This represents a unique method to parcellate the cortex, based upon the temporal signature of entropy. Images are displayed in radiological convention.
Mentions: The RVE/ICA method yielded a spatial basis set, which is shown in Fig 4. The functional maps depict the spatial signatures of 12 temporally independent components which were selected for further analysis. Each of these maps represents a brain region (or collection of brain regions) that generates an independent temporal evolution of entropy. In this way, we allow spatial parcellation of the cortex, based upon its functional anatomy. The regions selected are well characterised and include visual cortex, motor cortex, cingulo-insula region, pre-motor cortex, the left and right fronto-parietal networks, the tempero-parietal junction and the left and right insula cortices. This spatial basis set was employed as a platform upon which all subsequent analyses were based. It is important to note that this approach offers significant advantages over voxel based metrics. Specifically, ICA allows us to decompose several thousand voxel timecourses into a smaller number of independent components, reducing multiple comparison problems that inevitably arise in subsequent statistical testing (see also discussion).

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