A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits

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...

Full description

Saved in:
Bibliographic Details
Published inMathematics (Basel) Vol. 12; no. 19; p. 2967
Main Authors Duong-Tran, Duy, Nguyen, Nghi, Mu, Shizhuo, Chen, Jiong, Bao, Jingxuan, Xu, Frederick H, Garai, Sumita, Cadena-Pico, Jose, Kaplan, Alan David, Chen, Tianlong, Zhao, Yize, Shen, Li, Goñi, Joaquín
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2024
Subjects
Analysis
Brain
brain connectomics
Brain research
Circuits
Computational linguistics
functional networks
individualized parcellation
Information theory
Language processing
Medical imaging
Natural language interfaces
Neuroimaging
Neurosciences
Phase transitions
Schizophrenia
stochastic block model
Online AccessGet full text

Cover

More Information
Summary: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 the 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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12192967