Toward an information theoretical description of communication in brain networks

Abstract

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.

Multi-policy models of interregional communication in the human connectome

“…One powerful approach for investigating communication policies is to simulate them in silico, and compare the outcome of those simulations with empirical observation [4,7]. Most commonly, this means calculating a measure that denotes the ease of communication between pairs of brain regions and comparing these values with FC [8][9][10][11][12][13][14][15][16][17][18][19][20].…”

The information content of brain states is explained by structural constraints on state energetics

“…Brain state probabilities provide a useful measure of information content that is consistent yet non-redundant with prior fMRI assessments and is strongly correlated with theoretical linear dynamical models of functional activity trajectories. This empirical correlation distinguishes the present approach from prior work applying information theory metrics to brain function, structure, and activity variance [42,43], providing a conceptual link between information theory and control theory for efficient network communication [2,44]. Other information theoretical measures in neuroscience have been mostly applied to obtain insights into nonlinear relationships of multivariate data or to quantify uncertainty [45][46][47].…”

“…Other information theoretical measures in neuroscience have been mostly applied to obtain insights into nonlinear relationships of multivariate data or to quantify uncertainty [45][46][47]. For example, an information theoretical concept of cognitive control has been proposed [48], a robust information flow metric for the human brain is available [49], and the communication dynamics in brain networks have been studied [43]. The relationship between brain entropy and human intelligence remains a particularly active area of research [50][51][52][53].…”

Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

“…Early attempts to investigate structure-function relationships in the brain spanned from simple approaches, such as correlational analyses (Amico and Goñi, 2018b;Goñi et al, 2014;Honey et al, 2009;Mišić et al, 2016;Zhang et al, 2011), to more complex ones, like whole brain computational and communication models (Amico et al, 2021;Avena-Koenigsberger et al, 2018;Deco et al, 2011;Griffa et al, 2017;Mišić et al, 2015;Seguin et al, 2020). More recently, graph signal processing provided a novel framework for a combined structurefunction analysis (Huang et al, 2018;Medaglia et al, 2018;Preti and Van De Ville, 2019).…”