# Exploring the transmission of cognitive task information through optimal brain pathways

## TL;DR

This paper investigates how cognitive task information is transmitted through brain networks, finding that the shortest path is not always optimal and that spatial and functional factors improve predictions.

## Contribution

The study introduces empirical evidence that cognitive information transmission is better modeled with direct paths and spatial-functional constraints rather than shortest paths.

## Key findings

- Shortest path routing outperformed other communication strategies but was less accurate than direct path routing.
- Incorporating spatial distance and functional asymmetry improved activity flow prediction accuracy.
- Cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

## Abstract

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

A fundamental concern of cognitive neuroscience is the emergence of the complex brain functions in humans. The transmission of neural signals in the brain is thought to be fundamental to cognition. However, it remains unclear how does cognitive information transmit effectively from the perspective of large-scale brain networks. While graph theory is innately dedicated to characterizing brain networks, there is still a gap between graph routing protocols and cognitive task activity. To this end, we test whether the graph-theory-based shortest path outperforms direct path and decentralized network communication routes leveraging empirical activity flow modeling. Results demonstrate that shortest path routing is superior to other network communication strategies in activity flow prediction, but inferior to direct path routing. Importantly, the incorporation of spatial distance and functional asymmetry improves prediction accuracy. This study not only sheds light on the mechanistic relationships between cognitive task activation, resting-state network topology, spatial geometry, and functional embedding, but also advances our understanding of complex communication mechanisms of the human brain.