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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 topology for the four conditions.(a) The network topology difference between the three frequency stimuli networks and the control state network. The actual anatomic coordinates of these nodes are illustrated in Figure 7; (b) The network topology difference between the 8 Hz network and the other two frequency stimuli networks. Based on two paired t-tests, the red line denotes the edges with a statistically significant increase (p<0.05) in linkage between the compared two networks, and the thickness of the edge indicates the strength of the increase. The green line denotes a significant decrease (p<0.05) in strength, while the blue lines denote no statistical difference between the two compared networks. The size of green circle represents the Hub coefficients of each node.
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f3: The network topology for the four conditions.(a) The network topology difference between the three frequency stimuli networks and the control state network. The actual anatomic coordinates of these nodes are illustrated in Figure 7; (b) The network topology difference between the 8 Hz network and the other two frequency stimuli networks. Based on two paired t-tests, the red line denotes the edges with a statistically significant increase (p<0.05) in linkage between the compared two networks, and the thickness of the edge indicates the strength of the increase. The green line denotes a significant decrease (p<0.05) in strength, while the blue lines denote no statistical difference between the two compared networks. The size of green circle represents the Hub coefficients of each node.

Mentions: The topology differences between the three stimuli frequencies and the control state across 10 rats are shown in Figure 3(a), where the paired t test is performed to investigate the linkage difference for each edge. The network topology changes shown in Figure 3(a) demonstrate that the network corresponding to the 8 Hz stimulus had a very strong increase in linkage strength between the frontal area and the parietal lobe compared to the control state network, while the 44 Hz and 84 Hz networks had no obvious difference compared to the control state. The differences in network topologies among the three stimuli conditions are further shown in Figure 3(b), where the 8 Hz network also exhibits a more dense linkage between the frontal and occipital areas, and between the parietal and occipital lobes, compared to the other two stimuli conditions. The hub coefficients shown in Figures 3(a) and (b) show that the parietal lobe plays an important role in SSVEP generation, which is consistent with previous results using scalp EEG23. The relatively shorter L, larger Ge, Le and C indicate that the network corresponding to the 8 Hz stimulus has more effective information processing ability in both local and global regions. We assume this might be the main reason why 8 Hz stimulus can evoke the strong SSVEP while the other two frequencies cannot.


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 topology for the four conditions.(a) The network topology difference between the three frequency stimuli networks and the control state network. The actual anatomic coordinates of these nodes are illustrated in Figure 7; (b) The network topology difference between the 8 Hz network and the other two frequency stimuli networks. Based on two paired t-tests, the red line denotes the edges with a statistically significant increase (p<0.05) in linkage between the compared two networks, and the thickness of the edge indicates the strength of the increase. The green line denotes a significant decrease (p<0.05) in strength, while the blue lines denote no statistical difference between the two compared networks. The size of green circle represents the Hub coefficients of each node.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: The network topology for the four conditions.(a) The network topology difference between the three frequency stimuli networks and the control state network. The actual anatomic coordinates of these nodes are illustrated in Figure 7; (b) The network topology difference between the 8 Hz network and the other two frequency stimuli networks. Based on two paired t-tests, the red line denotes the edges with a statistically significant increase (p<0.05) in linkage between the compared two networks, and the thickness of the edge indicates the strength of the increase. The green line denotes a significant decrease (p<0.05) in strength, while the blue lines denote no statistical difference between the two compared networks. The size of green circle represents the Hub coefficients of each node.
Mentions: The topology differences between the three stimuli frequencies and the control state across 10 rats are shown in Figure 3(a), where the paired t test is performed to investigate the linkage difference for each edge. The network topology changes shown in Figure 3(a) demonstrate that the network corresponding to the 8 Hz stimulus had a very strong increase in linkage strength between the frontal area and the parietal lobe compared to the control state network, while the 44 Hz and 84 Hz networks had no obvious difference compared to the control state. The differences in network topologies among the three stimuli conditions are further shown in Figure 3(b), where the 8 Hz network also exhibits a more dense linkage between the frontal and occipital areas, and between the parietal and occipital lobes, compared to the other two stimuli conditions. The hub coefficients shown in Figures 3(a) and (b) show that the parietal lobe plays an important role in SSVEP generation, which is consistent with previous results using scalp EEG23. The relatively shorter L, larger Ge, Le and C indicate that the network corresponding to the 8 Hz stimulus has more effective information processing ability in both local and global regions. We assume this might be the main reason why 8 Hz stimulus can evoke the strong SSVEP while the other two frequencies cannot.

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