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Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: Path Processing Score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); Path Broadcasting Strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main “communication regimes” of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; Visual and somato-motor cortices act as multi-channel transducted broadcasters. This work paves the way towards the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to Task transitional reconfiguration, and iii) Within-Task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN-reconfigurations that result from the brain switching between mental states.

We introduce a simple mathematical operation that systematically improves the extraction of functional connectivity fingerprints from neuroimaging data, according to three different metrics. The results suggest that the information related to individual traits lies in part in weakly connected brain areas and can be compressed in a low-dimensional space. We also show the benefits of using multiple metrics to quantify fingerprint in a dataset. Our approach could improve future individual-level studies of functional neuroimaging data, which are crucial for the personalized diagnosis and treatment of neurological disorders, as well as for the study of the relationship between brain and behavior.

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