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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.


Speaker-related communities occurring in multi-brain network.Two communities dominated by frontal and parietal lobe locations are shown as separate graphs in stimulus1, (a) and (b), and in stimulus11 (c), and (d).
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pone.0166787.g012: Speaker-related communities occurring in multi-brain network.Two communities dominated by frontal and parietal lobe locations are shown as separate graphs in stimulus1, (a) and (b), and in stimulus11 (c), and (d).

Mentions: In general, the presence of communities is relevant for the synchronisation of stochastic processes taking part on the graph [52, 56]; the characteristic time scale of the coherence dynamics on different communities is directly related with the lowest eigenvalues of the Laplacian operator related with the graph’s adjacency matrix while the corresponding eigenvectors localise on these communities [57]. Here, the activity patterns, involving different areas in the multi-brain graph, lead to the enhanced correlations and dense subgraphs or communities that involve scalp locations of several listeners and a speaker. The community structure of the multi-brain network both for simulus1 and stimulus11 are shown in Fig 11. In each case, there are several communities of different sizes. While some single-brain network (as the listener L1−1 and L2−2 in the case of stimulus1, and similarly, speaker S3 in stimulus11) comprises a separate community, the majority of the identified communities are cross-brain type involving parts of the nodes in SBN of different participants. Two such communities, related to the speaker S2 in simulus1 are shown separately in Fig 12a and 12b. Similarly, examples of the communities related to the speaker S1 in stimulus11 are shown in Fig 12c and 12d, respectively. It is interesting to stress that typically frontal scalp areas across different brains often form a separate community while parietal areas belong to another (here termed F- and P-community), cf. labels in Fig 12a–12d. A similar structure of the communities occurs in the two-brain network in Fig 10a in a direct relation to a right speaker–listener coordination.


Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications
Speaker-related communities occurring in multi-brain network.Two communities dominated by frontal and parietal lobe locations are shown as separate graphs in stimulus1, (a) and (b), and in stimulus11 (c), and (d).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0166787.g012: Speaker-related communities occurring in multi-brain network.Two communities dominated by frontal and parietal lobe locations are shown as separate graphs in stimulus1, (a) and (b), and in stimulus11 (c), and (d).
Mentions: In general, the presence of communities is relevant for the synchronisation of stochastic processes taking part on the graph [52, 56]; the characteristic time scale of the coherence dynamics on different communities is directly related with the lowest eigenvalues of the Laplacian operator related with the graph’s adjacency matrix while the corresponding eigenvectors localise on these communities [57]. Here, the activity patterns, involving different areas in the multi-brain graph, lead to the enhanced correlations and dense subgraphs or communities that involve scalp locations of several listeners and a speaker. The community structure of the multi-brain network both for simulus1 and stimulus11 are shown in Fig 11. In each case, there are several communities of different sizes. While some single-brain network (as the listener L1−1 and L2−2 in the case of stimulus1, and similarly, speaker S3 in stimulus11) comprises a separate community, the majority of the identified communities are cross-brain type involving parts of the nodes in SBN of different participants. Two such communities, related to the speaker S2 in simulus1 are shown separately in Fig 12a and 12b. Similarly, examples of the communities related to the speaker S1 in stimulus11 are shown in Fig 12c and 12d, respectively. It is interesting to stress that typically frontal scalp areas across different brains often form a separate community while parietal areas belong to another (here termed F- and P-community), cf. labels in Fig 12a–12d. A similar structure of the communities occurs in the two-brain network in Fig 10a in a direct relation to a right speaker–listener coordination.

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’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.