Efficient coding in the economics of human brain connectomics

Abstract

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8–23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior—beyond the conventional network efficiency metric—for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Evidence for increased parallel information transmission in human brain networks compared to macaques and mice

“…In addition, parallel information transmission may be functional to specific processing needs of unimodal-transmodal communication supporting cognition. Recent computational studies suggest that brain regions with largest allometric scaling privilege fidelity rather than compression of incoming signals from unimodal areas 24 . High-fidelity information transmission may be achieved through parallel streaming of redundant signals, expression of a more resilient communication process.…”

“…Taking advantage of structural and functional connectivity information extracted from multimodal brain data (i.e., functional MRI, diffusion MRI, tract tracing), we explore the intricate pathways of communication in the mouse, monkey and human connectomes. We employ information-theoretical principles 24, 35, 36 to identify the structural pathways selected for neural communication by different neural systems, and measure the level of selective and parallel information processing across the different species. We report a strong evolutionary gradient in the brain communication dynamics of mammals, with predominant selective information routing in lower mammalian species such as mice and macaques, morphing into more complex communication patterns in human brains.…”

Homophilic wiring principles underpin neuronal network topologyin vitro

“…One prominent explanation for these consistent organizational hallmarks is that they reflect the economics of forming and maintaining connections 37–39 . Given finite available resources, trade-offs between incurred costs (e.g.…”

“…These complex organizational hallmarks allow for functional hierarchies, in which distinct segregated modules perform specialized local computations reflecting basic representational features of incoming signals, while their intermediary nodes integrate those signals to code for a more complex representation of the incoming signal 36 . One prominent explanation for these consistent organizational hallmarks is that they reflect the economics of forming and maintaining connections [37][38][39] . Given finite available resources, trade-offs between incurred costs (e.g.…”

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

“…We hypothesized that numerous structural and functional neuronal features found in brains across species could be the result of this very fundamental optimization and how it unfolds. Indeed, the predominant explanation for anatomical organization of the brain has focused on minimizing wiring costs while maximizing adaptive topological features (Bullmore & Sporns, 2012;Zhou et al, 2022).…”