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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 measures of the SBNs of speakers and two groups of listeners for stimulus1.Components of the second (SSV) and third (TSV) structure vectors (left panels), and the ranking distributions of the nodes’ topological dimensions (right panels). The full lines indicate the corresponding measures of the speakers.
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pone.0166787.g009: Topology measures of the SBNs of speakers and two groups of listeners for stimulus1.Components of the second (SSV) and third (TSV) structure vectors (left panels), and the ranking distributions of the nodes’ topological dimensions (right panels). The full lines indicate the corresponding measures of the speakers.

Mentions: As mentioned in the Introduction, we anticipate that the key features of the brain activation of an individual during social communication are contained in the hierarchical organisation of links between involved brain areas, which leads to the occurrence of topological complexes. In the multi-brain graphs, the occurrence of higher-order structures is manifested in two ways: (i) the appearance of hierarchical organisation along different topology levels in each SBN, and (ii) the inter-brain correlations leading to a nontrivial community structure of multi-brain graph as well as the hierarchical organisation of these communities. In comparison with known heuristic approaches for hierarchical communities [52, 55], here we rely on mathematically strict definition of simplexes and simplicial complexes, as described in Methods. First, we compute the structure vectors defined in Methods to describe the higher-order structures in the individual brain connections both for speakers and listeners. In Fig 9, we show the results of the first and third structure vectors for each of 14 single-brain networks in the case of the stimulus1.


Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications
Topology measures of the SBNs of speakers and two groups of listeners for stimulus1.Components of the second (SSV) and third (TSV) structure vectors (left panels), and the ranking distributions of the nodes’ topological dimensions (right panels). The full lines indicate the corresponding measures of the speakers.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0166787.g009: Topology measures of the SBNs of speakers and two groups of listeners for stimulus1.Components of the second (SSV) and third (TSV) structure vectors (left panels), and the ranking distributions of the nodes’ topological dimensions (right panels). The full lines indicate the corresponding measures of the speakers.
Mentions: As mentioned in the Introduction, we anticipate that the key features of the brain activation of an individual during social communication are contained in the hierarchical organisation of links between involved brain areas, which leads to the occurrence of topological complexes. In the multi-brain graphs, the occurrence of higher-order structures is manifested in two ways: (i) the appearance of hierarchical organisation along different topology levels in each SBN, and (ii) the inter-brain correlations leading to a nontrivial community structure of multi-brain graph as well as the hierarchical organisation of these communities. In comparison with known heuristic approaches for hierarchical communities [52, 55], here we rely on mathematically strict definition of simplexes and simplicial complexes, as described in Methods. First, we compute the structure vectors defined in Methods to describe the higher-order structures in the individual brain connections both for speakers and listeners. In Fig 9, we show the results of the first and third structure vectors for each of 14 single-brain networks in the case of the stimulus1.

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.