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

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.

Origin of Hyperbolicity in Brain-to-Brain Coordination Networks

“…Mapping the recorded data to a multi-brain graph where each EEG channel is designated as a node, arranged a fine-grain network suitable for analysis of coordination between brains. Furthermore, it was demonstrated that the methods of algebraic topology provide adequate measures of inter-brain coordinations by uncovering the simple geometric descriptors which arrange themselves into higher organized structures [4]. Referring to this work, Falk et al in recent Feature Review [6] stated that "these approaches pave the way to understand the manners in which brain network dynamics in one person might influence or reflect the brain network dynamics in another."…”

“…In this regard, a systematic mapping of the imaging data on networks and the use of objective graph theory analysis [1][2][3] represents a significant leap. Recently, we have extended this approach to analyse the multi-brain networks [4]; the underlying data obtained in the original experiment [5] represent the simultaneous EEG brain imaging in a group of participants during spoken communications. Mapping the recorded data to a multi-brain graph where each EEG channel is designated as a node, arranged a fine-grain network suitable for analysis of coordination between brains.…”

“…Based on the set of data from Kuhlen et al [5] analyzed in our recent work Tadić et al [4], here we study hidden geometries of two-brain coordination networks. In particular, we consider all speaker-listener pairs in the underlying communication dataset of Stimulus-1.…”

“…For a detailed description of the experimental set-up and the composition of the Stimuli as well as the psychology background of the experiment we refer to the original work [5]. The Stimulus-1 and the signals that we consider here are also described in Tadić et al [4].…”

“…For this reason, the underlying functional mechanisms can be probed effectively by coupling functions [32], which accurately describes the paths to and from the synchronization [33]. In the case of brain-to-brain coordination networks, the links are emanating from the dynamics-correlations among EEG signals in various pairs of channels across two brains [4]. Moreover, considering neuroanatomy, the brain can be seen as a network embedded in a hyperbolic space [34].…”