Limits...
Hyper-brain networks support romantic kissing in humans.

Müller V, Lindenberger U - PLoS ONE (2014)

Bottom Line: Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies.Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains.We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior.

View Article: PubMed Central - PubMed

Affiliation: Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.

ABSTRACT
Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies. Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains. Specifically, we compare the electroencephalograms (EEG) of 15 heterosexual couples during romantic kissing to kissing one's own hand, and to kissing one another while performing silent arithmetic. Using graph-theory methods, we identify theta-alpha hyper-brain networks, with alpha serving a cleaving or pacemaker function. Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand. In both partner-oriented kissing conditions, greater strength and shorter path length for 5-Hz oscillation nodes correlated reliably with greater partner-oriented kissing satisfaction. This correlation was especially strong for inter-brain connections in both partner-oriented kissing conditions but not during kissing one's own hand. Kissing quality assessed after the kissing with silent arithmetic correlated reliably with intra-brain strength of 10-Hz oscillation nodes during both romantic kissing and kissing with silent arithmetic. We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior.

No MeSH data available.


Hyper-brain network properties under the three kissing conditions.A: Changes in hyper-brain network costs dependent on the coupling threshold. B: Changes in global efficiency (Eglob) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. C: Changes in local efficiency (Eloc) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. D: Changes in characteristic path length (CPL) in the hyper-brain and random networks dependent on the coupling threshold. The CPL of regular networks was always equal infinity and is, therefore, not presented in the diagram. E: Changes in the clustering coefficient (CC) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. F: Changes in the small-worldness coefficient (σ) in the hyper-brain network dependent on the coupling threshold. G: Changes in the small-worldness coefficient (ω) in the hyper-brain network dependent on the coupling threshold. RK = romantic kissing, K-SA = kissing while performing silent arithmetic, and HK = hand kissing. Hyper-brain network: red line; regular network: blue line; and random network: green line.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4222975&req=5

pone-0112080-g003: Hyper-brain network properties under the three kissing conditions.A: Changes in hyper-brain network costs dependent on the coupling threshold. B: Changes in global efficiency (Eglob) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. C: Changes in local efficiency (Eloc) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. D: Changes in characteristic path length (CPL) in the hyper-brain and random networks dependent on the coupling threshold. The CPL of regular networks was always equal infinity and is, therefore, not presented in the diagram. E: Changes in the clustering coefficient (CC) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. F: Changes in the small-worldness coefficient (σ) in the hyper-brain network dependent on the coupling threshold. G: Changes in the small-worldness coefficient (ω) in the hyper-brain network dependent on the coupling threshold. RK = romantic kissing, K-SA = kissing while performing silent arithmetic, and HK = hand kissing. Hyper-brain network: red line; regular network: blue line; and random network: green line.

Mentions: The real and control networks for each couple were constructed for 10 different adaptive thresholds (see Methods for details), and the SW metrics were measured as a function of the threshold (Figure 3). As expected, increasing thresholds resulted in lower costs, which indicate sparser networks (Fig. 3A). Sparser networks have lower global but higher local efficiency (Fig. 3B and 3C), which are correspondingly related to a higher CPL and also a higher CC (Fig. 3D and 3E). Thus, sparsity in hyper-brain networks leads to higher segregation but lower integration of information flow. The small-worldness coefficient σ was always greater than 1 and increased with lower costs (Fig. 3F) indicating SW properties for all networks independently of the threshold or sparseness. The other small-worldness coefficient ω ranged between −0.3 and +0.3 for individual networks and decreased with lower costs (Fig. 3G), also indicating SW properties of the observed networks and a tendency to become more regular with higher sparseness. Further, we compared network characteristics at the eighth threshold level (f8 = 1.035) with high sparsity and optimal SW parameters.


Hyper-brain networks support romantic kissing in humans.

Müller V, Lindenberger U - PLoS ONE (2014)

Hyper-brain network properties under the three kissing conditions.A: Changes in hyper-brain network costs dependent on the coupling threshold. B: Changes in global efficiency (Eglob) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. C: Changes in local efficiency (Eloc) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. D: Changes in characteristic path length (CPL) in the hyper-brain and random networks dependent on the coupling threshold. The CPL of regular networks was always equal infinity and is, therefore, not presented in the diagram. E: Changes in the clustering coefficient (CC) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. F: Changes in the small-worldness coefficient (σ) in the hyper-brain network dependent on the coupling threshold. G: Changes in the small-worldness coefficient (ω) in the hyper-brain network dependent on the coupling threshold. RK = romantic kissing, K-SA = kissing while performing silent arithmetic, and HK = hand kissing. Hyper-brain network: red line; regular network: blue line; and random network: green line.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4222975&req=5

pone-0112080-g003: Hyper-brain network properties under the three kissing conditions.A: Changes in hyper-brain network costs dependent on the coupling threshold. B: Changes in global efficiency (Eglob) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. C: Changes in local efficiency (Eloc) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. D: Changes in characteristic path length (CPL) in the hyper-brain and random networks dependent on the coupling threshold. The CPL of regular networks was always equal infinity and is, therefore, not presented in the diagram. E: Changes in the clustering coefficient (CC) in the hyper-brain, regular (lattice), and random networks dependent on the coupling threshold. F: Changes in the small-worldness coefficient (σ) in the hyper-brain network dependent on the coupling threshold. G: Changes in the small-worldness coefficient (ω) in the hyper-brain network dependent on the coupling threshold. RK = romantic kissing, K-SA = kissing while performing silent arithmetic, and HK = hand kissing. Hyper-brain network: red line; regular network: blue line; and random network: green line.
Mentions: The real and control networks for each couple were constructed for 10 different adaptive thresholds (see Methods for details), and the SW metrics were measured as a function of the threshold (Figure 3). As expected, increasing thresholds resulted in lower costs, which indicate sparser networks (Fig. 3A). Sparser networks have lower global but higher local efficiency (Fig. 3B and 3C), which are correspondingly related to a higher CPL and also a higher CC (Fig. 3D and 3E). Thus, sparsity in hyper-brain networks leads to higher segregation but lower integration of information flow. The small-worldness coefficient σ was always greater than 1 and increased with lower costs (Fig. 3F) indicating SW properties for all networks independently of the threshold or sparseness. The other small-worldness coefficient ω ranged between −0.3 and +0.3 for individual networks and decreased with lower costs (Fig. 3G), also indicating SW properties of the observed networks and a tendency to become more regular with higher sparseness. Further, we compared network characteristics at the eighth threshold level (f8 = 1.035) with high sparsity and optimal SW parameters.

Bottom Line: Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies.Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains.We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior.

View Article: PubMed Central - PubMed

Affiliation: Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.

ABSTRACT
Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies. Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains. Specifically, we compare the electroencephalograms (EEG) of 15 heterosexual couples during romantic kissing to kissing one's own hand, and to kissing one another while performing silent arithmetic. Using graph-theory methods, we identify theta-alpha hyper-brain networks, with alpha serving a cleaving or pacemaker function. Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand. In both partner-oriented kissing conditions, greater strength and shorter path length for 5-Hz oscillation nodes correlated reliably with greater partner-oriented kissing satisfaction. This correlation was especially strong for inter-brain connections in both partner-oriented kissing conditions but not during kissing one's own hand. Kissing quality assessed after the kissing with silent arithmetic correlated reliably with intra-brain strength of 10-Hz oscillation nodes during both romantic kissing and kissing with silent arithmetic. We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior.

No MeSH data available.