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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.Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand.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.


Related in: MedlinePlus

Calculation of the adaptive Integrative Coupling Index (aICI).A: Complex Morlet wavelet transformation of signals from two channels (Fz and Cz) in the time-frequency domain. B: The phase difference is depicted in the form of the vectors in complex space, where the blue arrows reflect single phase angles and the red arrow represents the mean vector of the angular dispersions (its length displays the PSI measure); θ is angle of this mean vector. Boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by the yellow dashed arrows. C: Time course of instantaneous phases from two channels (Fz and Cz) at fi = 10 Hz and their phase difference (Fz = violet curve; Cz = green curve; Fz–Cz = red curve). Angle of the mean vector θ and boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by yellow dotted and dashed lines. D: Coding of the phase difference of two signals, S1 (e.g., Fz) and S2 (e.g., Cz), at a given frequency (θ−π/4<S1–S2< θ: blue stripes; θ<S1–S2<θ+π/4: red stripes; S1–S2<−π/4 or S1–S2>+π/4: green stripes = nonsynchronization). E: Pair-wise synchronization pattern of all possible electrode pairs with Fz as a reference electrode. Each line represents one pair of channels.
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pone-0112080-g001: Calculation of the adaptive Integrative Coupling Index (aICI).A: Complex Morlet wavelet transformation of signals from two channels (Fz and Cz) in the time-frequency domain. B: The phase difference is depicted in the form of the vectors in complex space, where the blue arrows reflect single phase angles and the red arrow represents the mean vector of the angular dispersions (its length displays the PSI measure); θ is angle of this mean vector. Boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by the yellow dashed arrows. C: Time course of instantaneous phases from two channels (Fz and Cz) at fi = 10 Hz and their phase difference (Fz = violet curve; Cz = green curve; Fz–Cz = red curve). Angle of the mean vector θ and boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by yellow dotted and dashed lines. D: Coding of the phase difference of two signals, S1 (e.g., Fz) and S2 (e.g., Cz), at a given frequency (θ−π/4<S1–S2< θ: blue stripes; θ<S1–S2<θ+π/4: red stripes; S1–S2<−π/4 or S1–S2>+π/4: green stripes = nonsynchronization). E: Pair-wise synchronization pattern of all possible electrode pairs with Fz as a reference electrode. Each line represents one pair of channels.

Mentions: Given the estimates of the phase difference between two signals, it is possible to ascertain how long the phase difference remains stable in defined phase angle boundaries by counting the number of points that are phase-locked at a defined time window. We slightly modified the procedure described in Müller and colleagues [8] in that we defined phase angle boundaries not related to phase zero but to the phase angle θ. The further procedure was similar, as depicted in Figure 1. After the complex wavelet transform of the signals (Fig. 1A) and determination of PSI and θ (Fig. 1B), we divided the range between θ−π/4 and θ+π/4 into two ranges and distinguished between positive and negative deviations from phase θ (Fig. 1C). As shown in Figure 1D, we marked negative deviations in the range between θ−π/4 and θ in blue (coded with “−1”) and the positive deviations in the range between θ and θ+π/4 in red (coded with “+1”). Phase difference values beyond these range were marked with green (coded with “0”) and represent non-synchronization. In the case of two channels, X (e.g., Fz) and Y (e.g., Cz), a blue stripe in the diagram would mean that the phase of channel Y precedes the phase of channel X and a red stripe would mean that the phase of channel X precedes the phase of channel Y. We then counted the number of data points that are phase-locked separately in each of these two ranges. Before counting, successive points in the defined range (between θ−π/4 and θ+π/4) with a time interval shorter than a period of the corresponding oscillation at the given frequency (Ti = 1/fi) were discarded from the analysis. In the case of CFC, the lower frequency was considered. This cleaning procedure effectively eliminated instances of accidental synchronization. Figure 1E represents synchronization pattern of several electrode pairs after this cleaning procedure. On the basis of the counting described above, we obtained several synchronization indices: (1) the Positive Coupling Index, PCI, or the relative number of phase-locked points in the positive range (between θ and θ+π/4); (2) the Negative Coupling Index, NCI, or the relative number of phase-locked points in the negative range (between θ−π/4 and θ); (3) the Absolute Coupling Index, ACI, or the relative number of phase-locked points in the positive and negative range (i.e., between θ−π/4 and θ+π/4) indicating absolute synchronization; (4) the adaptive Integrative Coupling Index, aICI, calculated by the formulae:


Hyper-brain networks support romantic kissing in humans.

Müller V, Lindenberger U - PLoS ONE (2014)

Calculation of the adaptive Integrative Coupling Index (aICI).A: Complex Morlet wavelet transformation of signals from two channels (Fz and Cz) in the time-frequency domain. B: The phase difference is depicted in the form of the vectors in complex space, where the blue arrows reflect single phase angles and the red arrow represents the mean vector of the angular dispersions (its length displays the PSI measure); θ is angle of this mean vector. Boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by the yellow dashed arrows. C: Time course of instantaneous phases from two channels (Fz and Cz) at fi = 10 Hz and their phase difference (Fz = violet curve; Cz = green curve; Fz–Cz = red curve). Angle of the mean vector θ and boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by yellow dotted and dashed lines. D: Coding of the phase difference of two signals, S1 (e.g., Fz) and S2 (e.g., Cz), at a given frequency (θ−π/4<S1–S2< θ: blue stripes; θ<S1–S2<θ+π/4: red stripes; S1–S2<−π/4 or S1–S2>+π/4: green stripes = nonsynchronization). E: Pair-wise synchronization pattern of all possible electrode pairs with Fz as a reference electrode. Each line represents one pair of channels.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0112080-g001: Calculation of the adaptive Integrative Coupling Index (aICI).A: Complex Morlet wavelet transformation of signals from two channels (Fz and Cz) in the time-frequency domain. B: The phase difference is depicted in the form of the vectors in complex space, where the blue arrows reflect single phase angles and the red arrow represents the mean vector of the angular dispersions (its length displays the PSI measure); θ is angle of this mean vector. Boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by the yellow dashed arrows. C: Time course of instantaneous phases from two channels (Fz and Cz) at fi = 10 Hz and their phase difference (Fz = violet curve; Cz = green curve; Fz–Cz = red curve). Angle of the mean vector θ and boundaries for calculation of aICI (θ−π/4 and θ+π/4) are indicated by yellow dotted and dashed lines. D: Coding of the phase difference of two signals, S1 (e.g., Fz) and S2 (e.g., Cz), at a given frequency (θ−π/4<S1–S2< θ: blue stripes; θ<S1–S2<θ+π/4: red stripes; S1–S2<−π/4 or S1–S2>+π/4: green stripes = nonsynchronization). E: Pair-wise synchronization pattern of all possible electrode pairs with Fz as a reference electrode. Each line represents one pair of channels.
Mentions: Given the estimates of the phase difference between two signals, it is possible to ascertain how long the phase difference remains stable in defined phase angle boundaries by counting the number of points that are phase-locked at a defined time window. We slightly modified the procedure described in Müller and colleagues [8] in that we defined phase angle boundaries not related to phase zero but to the phase angle θ. The further procedure was similar, as depicted in Figure 1. After the complex wavelet transform of the signals (Fig. 1A) and determination of PSI and θ (Fig. 1B), we divided the range between θ−π/4 and θ+π/4 into two ranges and distinguished between positive and negative deviations from phase θ (Fig. 1C). As shown in Figure 1D, we marked negative deviations in the range between θ−π/4 and θ in blue (coded with “−1”) and the positive deviations in the range between θ and θ+π/4 in red (coded with “+1”). Phase difference values beyond these range were marked with green (coded with “0”) and represent non-synchronization. In the case of two channels, X (e.g., Fz) and Y (e.g., Cz), a blue stripe in the diagram would mean that the phase of channel Y precedes the phase of channel X and a red stripe would mean that the phase of channel X precedes the phase of channel Y. We then counted the number of data points that are phase-locked separately in each of these two ranges. Before counting, successive points in the defined range (between θ−π/4 and θ+π/4) with a time interval shorter than a period of the corresponding oscillation at the given frequency (Ti = 1/fi) were discarded from the analysis. In the case of CFC, the lower frequency was considered. This cleaning procedure effectively eliminated instances of accidental synchronization. Figure 1E represents synchronization pattern of several electrode pairs after this cleaning procedure. On the basis of the counting described above, we obtained several synchronization indices: (1) the Positive Coupling Index, PCI, or the relative number of phase-locked points in the positive range (between θ and θ+π/4); (2) the Negative Coupling Index, NCI, or the relative number of phase-locked points in the negative range (between θ−π/4 and θ); (3) the Absolute Coupling Index, ACI, or the relative number of phase-locked points in the positive and negative range (i.e., between θ−π/4 and θ+π/4) indicating absolute synchronization; (4) the adaptive Integrative Coupling Index, aICI, calculated by the formulae:

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.Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand.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.


Related in: MedlinePlus