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Self-Regulated Dynamical Criticality in Human ECoG.

Solovey G, Miller KJ, Ojemann JG, Magnasco MO, Cecchi GA - Front Integr Neurosci (2012)

Bottom Line: Mounting experimental and theoretical results indicate that neural systems are poised near a critical state.In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution.Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Columbia University New York, NY, USA.

ABSTRACT
Mounting experimental and theoretical results indicate that neural systems are poised near a critical state. In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution. Electrocorticography (ECoG) provides a unique window into human brain activity: each electrode records, with high temporal resolution, the activity resulting from the sum of the local field potentials of ∼10(5) neurons. We show that the human brain ECoG recordings display features of self-regulated dynamical criticality: dynamical modes of activation drift around the critical stability threshold, moving in and out of the unstable region and equilibrating the global dynamical state at a very fast time scale. Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

No MeSH data available.


The number of unstable modes is larger in cue-off than in cue-on periods for all subjects. (A) Relative difference (%) between the mean number of unstable modes in cue-on and cue-off periods for all subjects. The blue bars correspond to the% of unstable modes in the cue-off condition and superimposed red bars correspond to the cue-on condition. (B) Difference between the % of unstable nodes in cue-off and cue-on conditions. This quantity is always positive, therefore there is always more unstable modes in cue-off than in cue-on periods. (C) The differences in the distribution of unstable modes is significant in 8 of 11 subjects. (p < 0.05, Kolmogorov–Smirnov test, blue line: p = 0.05).
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Figure 5: The number of unstable modes is larger in cue-off than in cue-on periods for all subjects. (A) Relative difference (%) between the mean number of unstable modes in cue-on and cue-off periods for all subjects. The blue bars correspond to the% of unstable modes in the cue-off condition and superimposed red bars correspond to the cue-on condition. (B) Difference between the % of unstable nodes in cue-off and cue-on conditions. This quantity is always positive, therefore there is always more unstable modes in cue-off than in cue-on periods. (C) The differences in the distribution of unstable modes is significant in 8 of 11 subjects. (p < 0.05, Kolmogorov–Smirnov test, blue line: p = 0.05).

Mentions: The results illustrated in Figures 1 and 2, and Figures 3 and 4, provide evidence that human ECoG potentials are dynamical and statistically critical, respectively. Figures 5 and 6 illustrate how our analysis can be used to distinguish between rest and task related activity during a finger-movement task.


Self-Regulated Dynamical Criticality in Human ECoG.

Solovey G, Miller KJ, Ojemann JG, Magnasco MO, Cecchi GA - Front Integr Neurosci (2012)

The number of unstable modes is larger in cue-off than in cue-on periods for all subjects. (A) Relative difference (%) between the mean number of unstable modes in cue-on and cue-off periods for all subjects. The blue bars correspond to the% of unstable modes in the cue-off condition and superimposed red bars correspond to the cue-on condition. (B) Difference between the % of unstable nodes in cue-off and cue-on conditions. This quantity is always positive, therefore there is always more unstable modes in cue-off than in cue-on periods. (C) The differences in the distribution of unstable modes is significant in 8 of 11 subjects. (p < 0.05, Kolmogorov–Smirnov test, blue line: p = 0.05).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3400079&req=5

Figure 5: The number of unstable modes is larger in cue-off than in cue-on periods for all subjects. (A) Relative difference (%) between the mean number of unstable modes in cue-on and cue-off periods for all subjects. The blue bars correspond to the% of unstable modes in the cue-off condition and superimposed red bars correspond to the cue-on condition. (B) Difference between the % of unstable nodes in cue-off and cue-on conditions. This quantity is always positive, therefore there is always more unstable modes in cue-off than in cue-on periods. (C) The differences in the distribution of unstable modes is significant in 8 of 11 subjects. (p < 0.05, Kolmogorov–Smirnov test, blue line: p = 0.05).
Mentions: The results illustrated in Figures 1 and 2, and Figures 3 and 4, provide evidence that human ECoG potentials are dynamical and statistically critical, respectively. Figures 5 and 6 illustrate how our analysis can be used to distinguish between rest and task related activity during a finger-movement task.

Bottom Line: Mounting experimental and theoretical results indicate that neural systems are poised near a critical state.In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution.Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Columbia University New York, NY, USA.

ABSTRACT
Mounting experimental and theoretical results indicate that neural systems are poised near a critical state. In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution. Electrocorticography (ECoG) provides a unique window into human brain activity: each electrode records, with high temporal resolution, the activity resulting from the sum of the local field potentials of ∼10(5) neurons. We show that the human brain ECoG recordings display features of self-regulated dynamical criticality: dynamical modes of activation drift around the critical stability threshold, moving in and out of the unstable region and equilibrating the global dynamical state at a very fast time scale. Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

No MeSH data available.