{"paper":{"title":"Hierarchical Bayesian inference for community detection and connectivity of functional brain networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A Bayesian latent block model detects community structures in functional brain networks at both individual and group levels while preserving subject variability.","cross_cats":["stat.AP"],"primary_cat":"q-bio.NC","authors_text":"Adeel Razi, Jonathan Keith, Leonardo Novelli, Lingbin Bian, Nizhuan Wang","submitted_at":"2023-01-18T09:30:46Z","abstract_excerpt":"Most functional magnetic resonance imaging studies rely on estimates of hierarchically organized functional brain networks whose segregation and integration reflect the cognitive and behavioral changes in humans. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis methods do not account for the variability between subjects. In this paper, we develop a new multilayer community detection method based on Bayesian latent block model (LBM). The method can robustly detect the community structure of weighted functional networ"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Analyses using both synthetic and real data show that our proposed method is more accurate and reliable compared with the commonly used (multilayer) modularity models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The community structure-based multivariate Gaussian generative model proposed in the paper accurately represents the statistical properties of real fMRI signals, allowing the simulation study to serve as a valid test of the detection method.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A Bayesian latent block model enables multilayer community detection in weighted functional brain networks with automatic community count estimation, validated for consistency on synthetic data and improved reproducibility over modularity on HCP working memory fMRI.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A Bayesian latent block model detects community structures in functional brain networks at both individual and group levels while preserving subject variability.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c84a923151270396c5a129257726c168d87af46e51bf27d5551c4451f92338da"},"source":{"id":"2301.07386","kind":"arxiv","version":5},"verdict":{"id":"5cf1ed29-76f1-4bd0-a223-8ffdb7274822","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-24T09:45:49.819436Z","strongest_claim":"Analyses using both synthetic and real data show that our proposed method is more accurate and reliable compared with the commonly used (multilayer) modularity models.","one_line_summary":"A Bayesian latent block model enables multilayer community detection in weighted functional brain networks with automatic community count estimation, validated for consistency on synthetic data and improved reproducibility over modularity on HCP working memory fMRI.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The community structure-based multivariate Gaussian generative model proposed in the paper accurately represents the statistical properties of real fMRI signals, allowing the simulation study to serve as a valid test of the detection method.","pith_extraction_headline":"A Bayesian latent block model detects community structures in functional brain networks at both individual and group levels while preserving subject variability."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2301.07386/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}