Pith Number

pith:QCYUGPLS

pith:2026:QCYUGPLS6IGOJLGCKRXFG2RVE3

not attested not anchored not stored refs resolved

An MCMC-Based Method for Dynamic Causal Modeling of Effective Connectivity in Functional MRI

Hyebin Song, Kaitlyn R. Fales, Nicole A. Lazar

CDCM uses MCMC and a simpler observation model to estimate fMRI effective connectivity with consistent parameters and reliable uncertainty.

arxiv:2605.14056 v1 · 2026-05-13 · stat.ME · stat.AP

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\pithnumber{QCYUGPLS6IGOJLGCKRXFG2RVE3}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The results indicate that CDCM provides reliable uncertainty quantification and consistent estimation of parameters related to experimental inputs for simulated and real data.

C2weakest assumption

The simpler observation model is adequate to capture the essential neural-hemodynamic dynamics without introducing bias in connectivity estimates.

C3one 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.

References

81 extracted · 81 resolved · 0 Pith anchors

[1] Identification of affine dynamical systems from a single trajectory.Inverse Problems 2020

[2] Identifiability of linear and linear-in-parameters dy- namical systems from a single trajectory.SIAM Journal on Applied Dynamical Systems 2014

[3] Perspectives on system identification.Annual Reviews in Control 2010

[4] System identification of nonlinear state-space models 2011

[5] Identifiability and asymp- totics in learning homogeneous linear ODE systems from discrete observations.Journal of Machine Learning Research 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:12.584030Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

80b1433d72f20ce4acc2546e536a3526ebe586f68b174bcba1ef129673b4751c

Aliases

arxiv: 2605.14056 · arxiv_version: 2605.14056v1 · doi: 10.48550/arxiv.2605.14056 · pith_short_12: QCYUGPLS6IGO · pith_short_16: QCYUGPLS6IGOJLGC · pith_short_8: QCYUGPLS

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QCYUGPLS6IGOJLGCKRXFG2RVE3 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 80b1433d72f20ce4acc2546e536a3526ebe586f68b174bcba1ef129673b4751c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4b296d17e99191de1d72376a2824c3684cc4665d30d198ee7ff40485f9a6476e",
    "cross_cats_sorted": [
      "stat.AP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-13T19:28:46Z",
    "title_canon_sha256": "d74ccb2ff78745a29c3c9e00470c707e018852aaf8f63f0964dd28d70dfcbdb3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14056",
    "kind": "arxiv",
    "version": 1
  }
}