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Cortical network properties revealed by SSVEP in anesthetized rats.

Xu P, Tian C, Zhang Y, Jing W, Wang Z, Liu T, Hu J, Tian Y, Xia Y, Yao D - Sci Rep (2013)

Bottom Line: Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood.In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat.All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

ABSTRACT
Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood. In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat. We examined the relationship between SSVEP amplitude and the network topological properties for different stimulation frequencies, the synergetic dynamic changes of the amplitude and topological properties in each rat, the network properties of the control state, and the individual difference of SSVEP network attributes existing among rats. All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

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The network properties varied with different network thresholds.The green line represents the control state, blue line represents the 8 Hz stimulus, black line represents the 44 Hz stimulus, and the red line represents the 84 Hz stimulus. The blue * indicates a significant difference (p<0.05) between the 8 Hz and 44 Hz stimulus networks. The black * indicates a significant difference (p<0.05) between 8 Hz network and 84 Hz network. The red * indicates a significant difference (p<0.05) between the 8 Hz stimulus and control state networks (p<0.05).
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f2: The network properties varied with different network thresholds.The green line represents the control state, blue line represents the 8 Hz stimulus, black line represents the 44 Hz stimulus, and the red line represents the 84 Hz stimulus. The blue * indicates a significant difference (p<0.05) between the 8 Hz and 44 Hz stimulus networks. The black * indicates a significant difference (p<0.05) between 8 Hz network and 84 Hz network. The red * indicates a significant difference (p<0.05) between the 8 Hz stimulus and control state networks (p<0.05).

Mentions: Based on the selected 3s-long EEG, the averaged network properties across 10 rats for the four conditions are shown in Figure 2 by varying the network binarization thresholds. Figure 2 demonstrates that the network corresponding to 8 Hz stimulus exhibit larger clustering coefficients (C), higher local efficiency (Le) and global efficiency (Ge), and shorter characteristic path length (L) compared to the networks at 44 Hz and 84 Hz stimuli. Further, the network properties of the 44 Hz and 84 Hz stimuli did not have obvious difference compared to that of control state network, while the 8 Hz stimulus showed a large difference in network properties from the control state. The paired t test reveal that, the four network properties of the 8 Hz stimulus were significantly different from those corresponding to the 44 Hz, 84 Hz, and control states for most of the tested thresholds.


Cortical network properties revealed by SSVEP in anesthetized rats.

Xu P, Tian C, Zhang Y, Jing W, Wang Z, Liu T, Hu J, Tian Y, Xia Y, Yao D - Sci Rep (2013)

The network properties varied with different network thresholds.The green line represents the control state, blue line represents the 8 Hz stimulus, black line represents the 44 Hz stimulus, and the red line represents the 84 Hz stimulus. The blue * indicates a significant difference (p<0.05) between the 8 Hz and 44 Hz stimulus networks. The black * indicates a significant difference (p<0.05) between 8 Hz network and 84 Hz network. The red * indicates a significant difference (p<0.05) between the 8 Hz stimulus and control state networks (p<0.05).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750539&req=5

f2: The network properties varied with different network thresholds.The green line represents the control state, blue line represents the 8 Hz stimulus, black line represents the 44 Hz stimulus, and the red line represents the 84 Hz stimulus. The blue * indicates a significant difference (p<0.05) between the 8 Hz and 44 Hz stimulus networks. The black * indicates a significant difference (p<0.05) between 8 Hz network and 84 Hz network. The red * indicates a significant difference (p<0.05) between the 8 Hz stimulus and control state networks (p<0.05).
Mentions: Based on the selected 3s-long EEG, the averaged network properties across 10 rats for the four conditions are shown in Figure 2 by varying the network binarization thresholds. Figure 2 demonstrates that the network corresponding to 8 Hz stimulus exhibit larger clustering coefficients (C), higher local efficiency (Le) and global efficiency (Ge), and shorter characteristic path length (L) compared to the networks at 44 Hz and 84 Hz stimuli. Further, the network properties of the 44 Hz and 84 Hz stimuli did not have obvious difference compared to that of control state network, while the 8 Hz stimulus showed a large difference in network properties from the control state. The paired t test reveal that, the four network properties of the 8 Hz stimulus were significantly different from those corresponding to the 44 Hz, 84 Hz, and control states for most of the tested thresholds.

Bottom Line: Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood.In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat.All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

ABSTRACT
Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood. In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat. We examined the relationship between SSVEP amplitude and the network topological properties for different stimulation frequencies, the synergetic dynamic changes of the amplitude and topological properties in each rat, the network properties of the control state, and the individual difference of SSVEP network attributes existing among rats. All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

Show MeSH