Limits...
Noise during rest enables the exploration of the brain's dynamic repertoire.

Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK - PLoS Comput. Biol. (2008)

Bottom Line: Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network.The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging.The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Neuroscience Group, Institut des Sciences du Mouvement, Marseille, France. Anandamohan.GHOSH@univmed.fr

ABSTRACT
Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Show MeSH

Related in: MedlinePlus

Identification of relevant nodes in resting state network.(A) Temporal evolution of resting state network nodes. Time series at bottom depicts the window of interest for areas PCS (white) and PFCORB (yellow) and markers correspond to instance at which activity is shown on the cortical surface. (B) Fourier power spectra of PCS (white) and PFCORB (yellow) show dominant peak at ∼10 Hz. (C) Time series of neural activity at PCS. (D) Time series of neural activity at PFCORB. Time evolution in (A) is shown for the window marked by vertical lines, 340–580 ms.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2551736&req=5

pcbi-1000196-g005: Identification of relevant nodes in resting state network.(A) Temporal evolution of resting state network nodes. Time series at bottom depicts the window of interest for areas PCS (white) and PFCORB (yellow) and markers correspond to instance at which activity is shown on the cortical surface. (B) Fourier power spectra of PCS (white) and PFCORB (yellow) show dominant peak at ∼10 Hz. (C) Time series of neural activity at PCS. (D) Time series of neural activity at PFCORB. Time evolution in (A) is shown for the window marked by vertical lines, 340–580 ms.

Mentions: The nodes of both networks ψk(i) are ordered according to power (see Figure 4C). We find that prefrontal, parietal and cingulate cortices rank highest in this ordering scheme and hence contribute most to the two network patterns present during the transient of the instability. We confirm our findings by performing a complete computational network simulation with noise just below the critical boundary and verify that these sub-networks are most commonly present during the transient oscillations of rest state activity. Exemplary time series for the network nodes in the presence of noise are shown in Figure 4D and resemble the characteristic transient and spindle-like time courses with dominant frequencies of 8–12 Hz known from real human resting EEG [30]. To illustrate the spatiotemporal nature of the network dynamics during such an intermittent spindle, we plot a sequence of activation patterns in a cortical surface-based coordinate system for 240 ms in Figure 5.


Noise during rest enables the exploration of the brain's dynamic repertoire.

Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK - PLoS Comput. Biol. (2008)

Identification of relevant nodes in resting state network.(A) Temporal evolution of resting state network nodes. Time series at bottom depicts the window of interest for areas PCS (white) and PFCORB (yellow) and markers correspond to instance at which activity is shown on the cortical surface. (B) Fourier power spectra of PCS (white) and PFCORB (yellow) show dominant peak at ∼10 Hz. (C) Time series of neural activity at PCS. (D) Time series of neural activity at PFCORB. Time evolution in (A) is shown for the window marked by vertical lines, 340–580 ms.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2551736&req=5

pcbi-1000196-g005: Identification of relevant nodes in resting state network.(A) Temporal evolution of resting state network nodes. Time series at bottom depicts the window of interest for areas PCS (white) and PFCORB (yellow) and markers correspond to instance at which activity is shown on the cortical surface. (B) Fourier power spectra of PCS (white) and PFCORB (yellow) show dominant peak at ∼10 Hz. (C) Time series of neural activity at PCS. (D) Time series of neural activity at PFCORB. Time evolution in (A) is shown for the window marked by vertical lines, 340–580 ms.
Mentions: The nodes of both networks ψk(i) are ordered according to power (see Figure 4C). We find that prefrontal, parietal and cingulate cortices rank highest in this ordering scheme and hence contribute most to the two network patterns present during the transient of the instability. We confirm our findings by performing a complete computational network simulation with noise just below the critical boundary and verify that these sub-networks are most commonly present during the transient oscillations of rest state activity. Exemplary time series for the network nodes in the presence of noise are shown in Figure 4D and resemble the characteristic transient and spindle-like time courses with dominant frequencies of 8–12 Hz known from real human resting EEG [30]. To illustrate the spatiotemporal nature of the network dynamics during such an intermittent spindle, we plot a sequence of activation patterns in a cortical surface-based coordinate system for 240 ms in Figure 5.

Bottom Line: Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network.The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging.The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Neuroscience Group, Institut des Sciences du Mouvement, Marseille, France. Anandamohan.GHOSH@univmed.fr

ABSTRACT
Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Show MeSH
Related in: MedlinePlus