A principled framework to assess the information-theoretic fitness of brain functional sub-circuits

TL;DR

This paper introduces a formal framework based on information theory and stochastic block models to evaluate the appropriateness of brain sub-circuit partitions and thresholding strategies in connectome analysis, enhancing the rigor of neuroscience methods.

Contribution

It provides a novel, theoretically grounded method to assess and optimize the fitness of functional network partitions and thresholding in brain connectome studies.

Findings

Validated the common threshold of 0.25 for group-average FCs.

Demonstrated the framework's ability to evaluate different parcellation granularities.

Provided insights into individualized brain parcellation strategies.

Abstract

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.