ArticleNeuroImage2017
Community detection in weighted brain connectivity networks beyond the resolution limit.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Relapse in alcohol dependence is characterized by disrupted modular brain network organization.European archives of psychiatry and clinical neuroscience · 2026Article
- Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks.bioRxiv : the preprint server for biology · 2025Article
- Characterization of Neural Network Connectivity and Modularity of Pigeon Nidopallium Caudolaterale During Target Detection.Animals : an open access journal from MDPI · 2025Article
- Uncovering narrative aging: an underlying neural mechanism compensated through spatial constructional ability.Communications biology · 2025Article
- Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.Network neuroscience (Cambridge, Mass.) · 2024Article
- Increased network centrality of the anterior insula in early abstinence from alcohol.Addiction biology · 2022Article
- Altered Functional Connectivity Within and Between Salience and Sensorimotor Networks in Patients With Functional Constipation.Frontiers in neuroscience · 2021Article
- LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks.Cerebral cortex (New York, N.Y. : 1991) · 2020Article
- Iterative consensus spectral clustering improves detection of subject and group level brain functional modules.Scientific reports · 2020Article
- Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering.Brain connectivity · 2020Article
- Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.NeuroImage. Clinical · 2020Article
- Refining modules to determine functionally significant clusters in molecular networks.BMC genomics · 2019Article
- Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks.Scientific reports · 2019Article
- Characterizing and Predicting Autism Spectrum Disorder by Performing Resting-State Functional Network Community Pattern Analysis.Frontiers in human neuroscience · 2019Article
- Disrupted modular organization of primary sensory brain areas in schizophrenia.NeuroImage. Clinical · 2018Article
- Low rank and sparsity constrained method for identifying overlapping functional brain networks.PloS one · 2018Article
- Hub-driven remote synchronization in brain networks.Scientific reports · 2017Article
- Improving resolution of dynamic communities in human brain networks through targeted node removal.PloS one · 2017Article
- Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold.Frontiers in neuroscience · 2017Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods.
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