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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

A simulated time-series is shown in blue, the template points are shown in solid squares, and the shaded grey areas indicate the points that are within ± r (tolerance) of these template points.The unfilled squares indicate points that match the template points to form sequences of length m or m+1 (here m = 2). The template points are moved sequentially through the time-series, and the total number of matches of length m, and length m+1 are calculated.
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pone.0120991.g003: A simulated time-series is shown in blue, the template points are shown in solid squares, and the shaded grey areas indicate the points that are within ± r (tolerance) of these template points.The unfilled squares indicate points that match the template points to form sequences of length m or m+1 (here m = 2). The template points are moved sequentially through the time-series, and the total number of matches of length m, and length m+1 are calculated.

Mentions: Sample entropy (SampEn) [39] assesses the predictability of a timecourse. Starting at point k, m+1 points are selected as a template time series. Following this, the remainder of the time series is examined for matches to this template (all matches to the first m points, and all m+1 points are counted separately). This analysis is repeated, moving the initial starting point k, and counting the total number of template matches of length m and length m+1. Sample entropy is then defined as the natural logarithm of the ratio of these two numbers, and indicates the likelihood of predicting the subsequent data point from the first m data points in a timecourse (a schematic of this process is shown in Fig 3). There are two variables which affect the estimate of SampEn: m, which governs the length of the template, and the tolerance, r, which governs what qualifies as a match. Here, we employ m = 2 and r = 20% of the timecourse standard deviation [27].


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)

A simulated time-series is shown in blue, the template points are shown in solid squares, and the shaded grey areas indicate the points that are within ± r (tolerance) of these template points.The unfilled squares indicate points that match the template points to form sequences of length m or m+1 (here m = 2). The template points are moved sequentially through the time-series, and the total number of matches of length m, and length m+1 are calculated.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0120991.g003: A simulated time-series is shown in blue, the template points are shown in solid squares, and the shaded grey areas indicate the points that are within ± r (tolerance) of these template points.The unfilled squares indicate points that match the template points to form sequences of length m or m+1 (here m = 2). The template points are moved sequentially through the time-series, and the total number of matches of length m, and length m+1 are calculated.
Mentions: Sample entropy (SampEn) [39] assesses the predictability of a timecourse. Starting at point k, m+1 points are selected as a template time series. Following this, the remainder of the time series is examined for matches to this template (all matches to the first m points, and all m+1 points are counted separately). This analysis is repeated, moving the initial starting point k, and counting the total number of template matches of length m and length m+1. Sample entropy is then defined as the natural logarithm of the ratio of these two numbers, and indicates the likelihood of predicting the subsequent data point from the first m data points in a timecourse (a schematic of this process is shown in Fig 3). There are two variables which affect the estimate of SampEn: m, which governs the length of the template, and the tolerance, r, which governs what qualifies as a match. Here, we employ m = 2 and r = 20% of the timecourse standard deviation [27].

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