Version history

{
  "scope": "50 healthy adults (23 women; 29.54 ± 5.62 yr); 3T MRI",
  "claim_text": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
  "raw_fields": {
    "n": 0,
    "doi": "10.1038/s41597-022-01682-y",
    "claim": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
    "cite_key": "Royer2022",
    "evidence": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
    "effect_size": "50",
    "text_access": "fulltext",
    "study_system": "50 healthy adults (23 women; 29.54 ± 5.62 yr); 3T MRI",
    "source_cluster_id": "cluster_07",
    "replication_status": "replication_unknown",
    "claim_source_sentence": "Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla.",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses."
  },
  "section_id": "section_08",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_08_evidence_package.json",
  "effect_size": "50",
  "review_repo": "ComputationalReviewLoops",
  "section_ref": "wiki_page:computationalreviewloops-08",
  "source_kind": "review_finding",
  "source_path": "evidence/section_08_evidence_package.json",
  "source_refs": [
    "paper:paper-c717abaed1d9"
  ],
  "source_span": "Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla.",
  "study_system": "50 healthy adults (23 women; 29.54 ± 5.62 yr); 3T MRI",
  "evidence_refs": [
    {
      "ref": "paper:paper-c717abaed1d9"
    }
  ],
  "section_title": "Cortical Association Connectome: Intra-Cortical Loops and Modules",
  "source_policy": {
    "mode": "public_source_pointer_with_short_context",
    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
    ],
    "source_commit_sha": "0632aae8abc141909207fe91f6349b9e36489c3b",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
  },
  "evidence_summary": "MICA-MICs is an open multimodal MRI dataset (n=50 healthy adults) providing T1-weighted, quantitative T1, diffusion, and resting-state fMRI connectomes plus microstructure covariance and geodesic cortical-distance matrices across multiple parcellation scales, supporting multiscale connectome and gradient analyses.",
  "review_bundle_ref": "analysis_bundle:ab-d49e54403ef9",
  "replication_status": "replication_unknown",
  "review_package_ref": "analysis_bundle:ab-d49e54403ef9",
  "source_artifact_ref": "wiki_page:computationalreviewloops-08",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops/blob/0632aae8abc141909207fe91f6349b9e36489c3b/evidence/section_08_evidence_package.json",
  "commit_sha": "0632aae8abc141909207fe91f6349b9e36489c3b",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewLoops"
}