ArticleScientific reports2016
Modular structure of brain functional networks: breaking the resolution limit by Surprise.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Relapse in alcohol dependence is characterized by disrupted modular brain network organization.European archives of psychiatry and clinical neuroscience · 2026Article
- Educational multimedia design principles affect local and global information processing in functional brain networks.Scientific reports · 2025Article
- Characterization of Neural Network Connectivity and Modularity of Pigeon Nidopallium Caudolaterale During Target Detection.Animals : an open access journal from MDPI · 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
- A brain structure learning-guided multi-view graph representation learning for brain network analysis.Quantitative imaging in medicine and surgery · 2024Article
- Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.Network neuroscience (Cambridge, Mass.) · 2024Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- Increased network centrality of the anterior insula in early abstinence from alcohol.Addiction biology · 2022Article
- Significance-based multi-scale method for network community detection and its application in disease-gene prediction.PloS one · 2020Article
- Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.NeuroImage. Clinical · 2020Article
- Critical analysis of (Quasi-)Surprise for community detection in complex networks.Scientific reports · 2018Article
- Global vs local modularity for network community detection.PloS one · 2018Article
- Disrupted modular organization of primary sensory brain areas in schizophrenia.NeuroImage. Clinical · 2018Article
- Classification and characterisation of brain network changes in chronic back pain: A multicenter study.Wellcome open research · 2018Article
- Article
- Graph theory reveals amygdala modules consistent with its anatomical subdivisions.Scientific reports · 2017Article
- Multi-scale brain networks.NeuroImage · 2017Review
- Hub-driven remote synchronization in brain networks.Scientific reports · 2017Article
- Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca.Scientific reports · 2017Article
- Community detection in weighted brain connectivity networks beyond the resolution limit.NeuroImage · 2017Article
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2 authors.
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Abstract
The modular organization of brain networks has been widely investigated using graph theoretical approaches. Recently, it has been demonstrated that graph partitioning methods based on the maximization of global fitness functions, like Newman's Modularity, suffer from a resolution limit, as they fail to detect modules that are smaller than a scale determined by the size of the entire network. Here we explore the effects of this limitation on the study of brain connectivity networks. We demonstrate that the resolution limit prevents detection of important details of the brain modular structure, thus hampering the ability to appreciate differences between networks and to assess the topological roles of nodes. We show that Surprise, a recently proposed fitness function based on probability theory, does not suffer from these limitations. Surprise maximization in brain co-activation and functional connectivity resting state networks reveals the presence of a rich structure of heterogeneously distributed modules, and differences in networks' partitions that are undetectable by resolution-limited methods. Moreover, Surprise leads to a more accurate identification of the network's connector hubs, the elements that integrate the brain modules into a cohesive structure.
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