{"paper":{"title":"An MCMC-Based Method for Dynamic Causal Modeling of Effective Connectivity in Functional MRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"CDCM uses MCMC and a simpler observation model to estimate fMRI effective connectivity with consistent parameters and reliable uncertainty.","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Hyebin Song, Kaitlyn R. Fales, Nicole A. Lazar","submitted_at":"2026-05-13T19:28:46Z","abstract_excerpt":"Effective connectivity analysis in functional magnetic resonance imaging (fMRI) studies directional interactions among brain regions and experimental stimuli. Dynamic causal modeling (DCM) is a widely used method to estimate effective connectivity, based on a state-space representation consisting of a latent neural signal model and an observation model transforming the neural signal into the observed blood-oxygen-level-dependent (BOLD) response. A standard DCM combines ordinary differential equation (ODE) dynamics for the latent signal with a complex neural-hemodynamic system for the observati"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The results indicate that CDCM provides reliable uncertainty quantification and consistent estimation of parameters related to experimental inputs for simulated and real data.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The simpler observation model is adequate to capture the essential neural-hemodynamic dynamics without introducing bias in connectivity estimates.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CDCM is a new MCMC method for dynamic causal modeling that uses a simpler observation model to improve uncertainty quantification and parameter estimation in fMRI effective connectivity analysis.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"CDCM uses MCMC and a simpler observation model to estimate fMRI effective connectivity with consistent parameters and reliable uncertainty.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"18f4ea47630f5173ebe0cd3c87a7e6af7f3fdc9b45f3be691324659e58c00a15"},"source":{"id":"2605.14056","kind":"arxiv","version":1},"verdict":{"id":"0c7b8358-9ea0-4b50-9d5d-cd4b413687c3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:07:31.057436Z","strongest_claim":"The results indicate that CDCM provides reliable uncertainty quantification and consistent estimation of parameters related to experimental inputs for simulated and real data.","one_line_summary":"CDCM is a new MCMC method for dynamic causal modeling that uses a simpler observation model to improve uncertainty quantification and parameter estimation in fMRI effective connectivity analysis.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The simpler observation model is adequate to capture the essential neural-hemodynamic dynamics without introducing bias in connectivity estimates.","pith_extraction_headline":"CDCM uses MCMC and a simpler observation model to estimate fMRI effective connectivity with consistent parameters and reliable uncertainty."},"references":{"count":81,"sample":[{"doi":"","year":2020,"title":"Identification of affine dynamical systems from a single trajectory.Inverse Problems","work_id":"7a7e6c81-9dfa-4445-b1a8-4a0dc396897f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"Identifiability of linear and linear-in-parameters dy- namical systems from a single trajectory.SIAM Journal on Applied Dynamical Systems","work_id":"00b74bd9-1420-4e50-ac1b-34baca48450e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2010,"title":"Perspectives on system identification.Annual Reviews in Control","work_id":"2dae39af-cd2b-4d0c-8e0f-1039c048d0ad","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"System identification of nonlinear state-space models","work_id":"9fb9bbe7-2ce7-4419-aa52-6d49621673f0","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Identifiability and asymp- totics in learning homogeneous linear ODE systems from discrete observations.Journal of Machine Learning Research","work_id":"2d8c01c2-fcbd-4721-9f3f-0ac9d94b8969","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":81,"snapshot_sha256":"18c8996264ad8b9696ad97d3ff7e03596400159bea1122b1703da127b817dfe6","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"fbdfbbe8fcd8d009c6bca8d36d24a30a9394d3187f84772a8fcf4a528658a5fe"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}