Version history

{
  "scope": "Mouse visual cortex; intrinsic-signal imaging parcellation",
  "claim_text": "Mouse visual cortex contains at least nine retinotopically defined visual areas plus two newly described retinotopically organized areas, providing the parcellation backbone for cortico-cortical connectivity studies.",
  "raw_fields": {
    "n": null,
    "doi": "10.1523/jneurosci.1124-14.2014",
    "claim": "Mouse visual cortex contains at least nine retinotopically defined visual areas plus two newly described retinotopically organized areas, providing the parcellation backbone for cortico-cortical connectivity studies.",
    "cite_key": "Garrett2014",
    "evidence": "Automated retinotopic mapping based on intrinsic-signal imaging applied to multiple mice, with quantitative area-boundary detection.",
    "effect_size": "9 known + 2 new retinotopic areas; quantification of area size, position, visual-field coverage, and cortical magnification (qualitative listing).",
    "text_access": "abstract_only",
    "study_system": "Mouse visual cortex; intrinsic-signal imaging parcellation",
    "argument_role": "supporting",
    "replication_status": "replicated",
    "claim_source_sentence": "This approach facilitated detailed parcellation of mouse visual cortex, delineating nine known areas (primary visual cortex, lateromedial area, anterolateral area, rostrolateral area, anteromedial area, posteromedial area, laterointermediate area, posterior area, and postrhinal area), and revealing two additional areas that have not been previously described as visuotopically mapped in mice (laterolateral anterior area and medial area).",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [
      "10.1016/j.neuron.2011.12.004"
    ],
    "effect_size_source_sentence": "We demonstrate that higher areas in mice often have representations that are incomplete or biased toward particular regions of visual space, suggestive of specializations for processing specific types of information about the environment."
  },
  "section_id": "section_08",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_08_evidence_package.json",
  "effect_size": "9 known + 2 new retinotopic areas; quantification of area size, position, visual-field coverage, and cortical magnification (qualitative listing).",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-08-cross-areal",
  "source_kind": "review_finding",
  "source_path": "evidence/section_08_evidence_package.json",
  "source_refs": [
    "paper:paper-e8764d6d6205"
  ],
  "source_span": "This approach facilitated detailed parcellation of mouse visual cortex, delineating nine known areas (primary visual cortex, lateromedial area, anterolateral area, rostrolateral area, anteromedial area, posteromedial area, laterointermediate area, posterior area, and postrhinal area), and revealing two additional areas that have not been previously described as visuotopically mapped in mice (laterolateral anterior a...",
  "study_system": "Mouse visual cortex; intrinsic-signal imaging parcellation",
  "evidence_refs": [
    {
      "ref": "paper:paper-e8764d6d6205"
    }
  ],
  "section_title": "8. Cross-areal mouse cortico-cortical excitatory connectivity — hierarchical feedforward and feedback as recurrent loops at the network level; Allen Mouse Connectivity Atlas anchored views",
  "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": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "Automated retinotopic mapping based on intrinsic-signal imaging applied to multiple mice, with quantitative area-boundary detection.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "replicated",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-08-cross-areal",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_08_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}