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Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.

Peishan Dai1, Li Chen2, Shuyu Guo2

  • 1School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, P.R. China. daipeishan@csu.edu.cn.

Neuroinformatics
|July 10, 2026
PubMed
Summary

Functional magnetic resonance imaging (fMRI) analysis relies on brain graph sparsification to refine noisy connectivity networks. This review synthesizes methods, categorizing them into topology-guided and supervision-guided approaches for improved brain connectome modeling.

Keywords:
Brain Graph SparsificationFunctional ConnectivitySupervision-guided SparsificationTopology-guided SparsificationfMRI

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Area of Science:

  • Neuroimaging
  • Network Neuroscience
  • Computational Neuroscience

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for mapping brain function using graph-based connectome analysis.
  • Pearson correlation-based functional connectivity networks are often dense and noisy, hindering analysis stability and interpretability.
  • Effective brain graph sparsification is essential for reliable and efficient fMRI network modeling.

Purpose of the Study:

  • To provide a structured methodological review of functional brain graph sparsification techniques for fMRI data.
  • To address the fragmentation of existing sparsification methods by offering a comprehensive synthesis.
  • To establish a framework for evaluating and reporting sparsification methods to enhance transparency and reproducibility.

Main Methods:

  • A taxonomy-driven review categorizing sparsification methods based on edge selection criteria: intrinsic graph properties (topology-guided) or external supervision signals (supervision-guided).
  • Analysis of underlying principles, advantages, and limitations of each sparsification paradigm.
  • Proposal of an evaluation framework and reporting guidelines for fMRI brain graph sparsification.

Main Results:

  • Existing fMRI brain graph sparsification methods are organized into two primary paradigms: topology-guided and supervision-guided.
  • Key trade-offs and principles governing each approach are clarified.
  • An evaluation framework is proposed to standardize the assessment of sparsification techniques.

Conclusions:

  • A systematic review and taxonomy of fMRI brain graph sparsification methods are presented.
  • The review aims to guide researchers in selecting appropriate methods for robust and biologically meaningful connectome modeling.
  • Future directions emphasize addressing challenges in creating reliable and interpretable brain network models from fMRI data.