Work

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

Year: 2024

Type: preprint

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.

Source: PubMed

DOI: 10.48550/arxiv.2406.18531

Authors: Duong-Tran, Duy, Nguyen, Nghi, Mu, Shizhuo, Chen, Jiong, Bao, Jingxuan, Xu, Frederick, Garai, Sumita, Cadena-Pico, Jose, Kaplan, Alan David, Chen, Tianlong, Zhao, Yize, Shen, Li, Goñi, Joaquín

Institutions: Bar-Ilan University, University of Pennsylvania, Lawrence Livermore National Laboratory, University of North Carolina at Chapel Hill

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, European Commission, National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute on Aging, Office of Naval Research, National Center for Advancing Translational Sciences, Lawrence Livermore National Laboratory

Awards: G1088636562, G1356433355, Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression, G1716777315, Mapping the Human Connectome: Structure, Function, and Heritability, G5999008256, BIGDATA: IA: Collaborative Research: Asynchronous Distributed Machine Learning Framework for Multi-Site Collaborative Brain Big Data Mining, G6935074504, G801680829, G8299918676, G9000014608, G9000056083, G9000089182, G9000091263, G9000135767, G9000183963, Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks, G991281326

OpenAlex ID: W4400105148

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