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

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.


Topology vectors of multi-brain graphs.Left panels: Components of the first (FSV) and the third (TSV) structure vectors plotted against the topology level q for the whole multi-brain network and for some its subgraphs, as indicated in the corresponding legends. The additive components of the FSV allow a comparison of the whole MBN with the sum of the corresponding component of each participating SBN, the line is indicated by , where k runs over all listeners and the two speakers in stimulus1. TSV of cross-graphs in two-brain networks from Fig 10 and their counterparts are shown. Right panels: Components of the second (SSV) structure vector of the largest four communities in stimulus11 (top) and three communities in stimulus1 (bottom). For comparison, the values obtained for the corresponding SBN of the speakers and listeners participating in these communities are also shown.
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pone.0166787.g013: Topology vectors of multi-brain graphs.Left panels: Components of the first (FSV) and the third (TSV) structure vectors plotted against the topology level q for the whole multi-brain network and for some its subgraphs, as indicated in the corresponding legends. The additive components of the FSV allow a comparison of the whole MBN with the sum of the corresponding component of each participating SBN, the line is indicated by , where k runs over all listeners and the two speakers in stimulus1. TSV of cross-graphs in two-brain networks from Fig 10 and their counterparts are shown. Right panels: Components of the second (SSV) structure vector of the largest four communities in stimulus11 (top) and three communities in stimulus1 (bottom). For comparison, the values obtained for the corresponding SBN of the speakers and listeners participating in these communities are also shown.

Mentions: The results of algebraic topology analysis of the entire multi-brain network (MBN) and its largest communities are given in Fig 13. First, we compare the (additive) components of the first structure vectors FSV of the whole MBN with the sum of the components of all SBN. Remarkably, the MBN exhibits a more complex structure, i.e., higher values at all topology levels, which can be attributed to the contributions of inter-brain subgraphs, cf. the adjacency matrix in Fig 3. Hence, this feature of the MBN is a good quantifier of the social impact among the communicating brains. Similarly, the third structure TSV shows that the simplexes at all topology levels up to q = 28 are strongly interconnected in the MBN. In this context, the TSV of the corresponding cross-brain subgraphs suitably quantifies the speaker–listener coordination. The results of TSV for the cross-links in the two-brain network in Fig 10 show that the proper coordination among the listener L2−3 and the speaker S2 corresponds to a topologically rich structure; in contrast, a weaker or improper coordination between L1−4 and the speaker S1 results in a much simple topology.


Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications
Topology vectors of multi-brain graphs.Left panels: Components of the first (FSV) and the third (TSV) structure vectors plotted against the topology level q for the whole multi-brain network and for some its subgraphs, as indicated in the corresponding legends. The additive components of the FSV allow a comparison of the whole MBN with the sum of the corresponding component of each participating SBN, the line is indicated by , where k runs over all listeners and the two speakers in stimulus1. TSV of cross-graphs in two-brain networks from Fig 10 and their counterparts are shown. Right panels: Components of the second (SSV) structure vector of the largest four communities in stimulus11 (top) and three communities in stimulus1 (bottom). For comparison, the values obtained for the corresponding SBN of the speakers and listeners participating in these communities are also shown.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5120797&req=5

pone.0166787.g013: Topology vectors of multi-brain graphs.Left panels: Components of the first (FSV) and the third (TSV) structure vectors plotted against the topology level q for the whole multi-brain network and for some its subgraphs, as indicated in the corresponding legends. The additive components of the FSV allow a comparison of the whole MBN with the sum of the corresponding component of each participating SBN, the line is indicated by , where k runs over all listeners and the two speakers in stimulus1. TSV of cross-graphs in two-brain networks from Fig 10 and their counterparts are shown. Right panels: Components of the second (SSV) structure vector of the largest four communities in stimulus11 (top) and three communities in stimulus1 (bottom). For comparison, the values obtained for the corresponding SBN of the speakers and listeners participating in these communities are also shown.
Mentions: The results of algebraic topology analysis of the entire multi-brain network (MBN) and its largest communities are given in Fig 13. First, we compare the (additive) components of the first structure vectors FSV of the whole MBN with the sum of the components of all SBN. Remarkably, the MBN exhibits a more complex structure, i.e., higher values at all topology levels, which can be attributed to the contributions of inter-brain subgraphs, cf. the adjacency matrix in Fig 3. Hence, this feature of the MBN is a good quantifier of the social impact among the communicating brains. Similarly, the third structure TSV shows that the simplexes at all topology levels up to q = 28 are strongly interconnected in the MBN. In this context, the TSV of the corresponding cross-brain subgraphs suitably quantifies the speaker–listener coordination. The results of TSV for the cross-links in the two-brain network in Fig 10 show that the proper coordination among the listener L2−3 and the speaker S2 corresponds to a topologically rich structure; in contrast, a weaker or improper coordination between L1−4 and the speaker S1 results in a much simple topology.

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.