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Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

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ABSTRACT

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli.

No MeSH data available.


Subject-specific brain activity patterns of the speaker S1.From left to right, SBNs represent EEG correlation patterns of the speaker S1 narrating a fairy tale (in stimulus1) and giving instructions (in stimulus11), and the components of the first and the second topology vector of these SBNs.
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pone.0166787.g014: Subject-specific brain activity patterns of the speaker S1.From left to right, SBNs represent EEG correlation patterns of the speaker S1 narrating a fairy tale (in stimulus1) and giving instructions (in stimulus11), and the components of the first and the second topology vector of these SBNs.

Mentions: Significant differences between SBNs occur across the listeners and speakers, and are quantified by the topology vectors. Across the listeners’ groups, the degree of heterogeneity strongly correlates with the increased distance to the appointed speaker. We also find in all studied examples, suggesting that the listener’s brain activity results in a more complex architecture than the speaker’s. In agreement with the statistical analysis in [41], this fact relates to the processing of semantic content in the presence of noise. A more detailed analysis reveals the excess links in the listener’s SBN; these links correspond to the coherence between a set of different EEG channels, not occurring in the speaker’s network. Moreover, these topology quantifiers accurately distinguish the patterns of the brain activity of the same speaker while narrating different subjects. Fig 14 displays the differences between corresponding EEG correlation networks as graphs and at each topology level for the speaker S1. Notably in contrast to the stimulus1, the number of big organised structures (for 16 < q < 24) occur in the case of the speaker’s narration in stimulus11, which also obtained higher ratings by the listeners.


Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications
Subject-specific brain activity patterns of the speaker S1.From left to right, SBNs represent EEG correlation patterns of the speaker S1 narrating a fairy tale (in stimulus1) and giving instructions (in stimulus11), and the components of the first and the second topology vector of these SBNs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120797&req=5

pone.0166787.g014: Subject-specific brain activity patterns of the speaker S1.From left to right, SBNs represent EEG correlation patterns of the speaker S1 narrating a fairy tale (in stimulus1) and giving instructions (in stimulus11), and the components of the first and the second topology vector of these SBNs.
Mentions: Significant differences between SBNs occur across the listeners and speakers, and are quantified by the topology vectors. Across the listeners’ groups, the degree of heterogeneity strongly correlates with the increased distance to the appointed speaker. We also find in all studied examples, suggesting that the listener’s brain activity results in a more complex architecture than the speaker’s. In agreement with the statistical analysis in [41], this fact relates to the processing of semantic content in the presence of noise. A more detailed analysis reveals the excess links in the listener’s SBN; these links correspond to the coherence between a set of different EEG channels, not occurring in the speaker’s network. Moreover, these topology quantifiers accurately distinguish the patterns of the brain activity of the same speaker while narrating different subjects. Fig 14 displays the differences between corresponding EEG correlation networks as graphs and at each topology level for the speaker S1. Notably in contrast to the stimulus1, the number of big organised structures (for 16 < q < 24) occur in the case of the speaker’s narration in stimulus11, which also obtained higher ratings by the listeners.

View Article: PubMed Central - PubMed

ABSTRACT

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener&rsquo;s concentration to the story, confirmed by self-rating, and closeness to the speaker&rsquo;s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener&rsquo;s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener&rsquo;s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli.

No MeSH data available.