ArticleFrontiers in neuroscience2017
Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
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- A Multiscale Spatiotemporal Causal Mapping Algorithm for Revealing Neural Network Mechanisms of Transcutaneous Auricular Vagus Nerve Stimulation.Human brain mapping · 2026Article
- Relapse in alcohol dependence is characterized by disrupted modular brain network organization.European archives of psychiatry and clinical neuroscience · 2026Article
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- Resting-state EEG markers associated with violence risk in patients with major depressive disorder.Psychological medicine · 2026Article
- The power of Network Pluralism: multi-perspective modeling of heterogeneous legal document networks.The European physical journal. B · 2026Article
- Spatiotemporal profiling of functional network overlapping modules in Alzheimer's disease.Network neuroscience (Cambridge, Mass.) · 2026Article
- Regression and alignment for functional data and network topology.Biostatistics (Oxford, England) · 2025Article
- Graph theory methods for analyzing functional connectivity in multiple spike trains: application to data recorded from the visual cortex of a cat.Cognitive neurodynamics · 2025Article
- The Impact of OXTR, COMT, and GRIN2B Polymorphisms on Brain Development in Preterm Infants.Journal of clinical medicine · 2025Article
- Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer.Scientific reports · 2025Article
- Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant.bioRxiv : the preprint server for biology · 2025Article
- Methods for Brain Connectivity Analysis with Applications to Rat Local Field Potential Recordings.Entropy (Basel, Switzerland) · 2025Article
- Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer's Disease Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Inferring independent sets of Gaussian variables after thresholding correlations.Journal of the American Statistical Association · 2025Article
- Distinct neural patterns for various information in working memory: A brain connectivity study.PloS one · 2025Article
- Both k-core percolation and directed graph analysis revealed succession and transition of voxels' spatiotemporal progress on dynamic correlation resting-state fMRI.Frontiers in human neuroscience · 2025Article
- Early postnatal changes in thyroid-stimulating hormone and subsequent neurodevelopment in preterm infants.Frontiers in endocrinology · 2025Article
- Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.Neural networks : the official journal of the International Neural Network Society · 2025Article
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3 authors.
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Abstract
Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
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