Version history

{
  "kind": "infographic",
  "prompt": "Three lines of analysis converge on the same global picture of the mouse cortico-cortical weighted graph: dense, log-normal-weighted, and rule-based but shallowly hierarchical.",
  "provider": "other",
  "raw_fields": {
    "papers": [
      {
        "n": 27,
        "doi": "10.1016/j.neuron.2017.12.037",
        "value": "0.97",
        "method": "retrograde tracer + cell counting",
        "metric": "binary inter-areal connection density",
        "n_analyzed": 27,
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "retrograde tracer injections",
        "scope_region": "mouse cortex (47-area parcellation)",
        "study_system": "Mouse cortex (19 of 47 parcellated areas)",
        "taxonomic_level": "area-to-area",
        "scope_population": "inter-areal connections (E projections)",
        "value_source_sentence": "The cortical network has a density of 97%, considerably higher than the 66% density reported in macaques.",
        "experimental_conditions": "retrograde tract tracing, flat-mount histology"
      },
      {
        "n": 1000,
        "doi": "10.1038/s41586-019-1716-z",
        "value": "shallow hierarchy across 43 isocortical areas + thalamus",
        "method": "Cre-driver AAV anterograde + axon-pattern hierarchy fit",
        "metric": "qualitative hierarchy depth",
        "n_analyzed": 1000,
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "Cre-driver anterograde tracer experiments",
        "scope_region": "mouse isocortex + thalamus",
        "study_system": "Mouse, Allen Mouse Brain Connectivity Atlas",
        "taxonomic_level": "area + cell class",
        "scope_population": "cell-class-specific projections",
        "value_source_sentence": "Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network.",
        "experimental_conditions": "AAV anterograde tracing"
      },
      {
        "n": null,
        "doi": "10.1162/netn_a_00345",
        "value": "85–90%",
        "method": "machine-learning prediction on weighted graph",
        "metric": "weighted-link prediction accuracy (mouse)",
        "n_analyzed": null,
        "ci_or_error": null,
        "text_access": "abstract_only",
        "n_definition": "inter-areal link entries in the weighted matrix",
        "scope_region": "mouse + macaque cortex (inter-areal)",
        "study_system": "Mouse + macaque inter-areal cortical matrix",
        "taxonomic_level": "area-to-area",
        "scope_population": "weighted links",
        "value_source_sentence": "Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species.",
        "experimental_conditions": "ML imputation from retrograde tract-tracing data + projection-length"
      }
    ],
    "audit_issues": [
      {
        "dimension": "metric_definition",
        "description": "Row 1 reports binary inter-areal density = 0.97 (Gămănuţ); row 2 reports a qualitative 'shallow hierarchy depth' (Harris); row 3 reports weighted-link prediction accuracy = 85–90% (Beul). These are three different scalars on different graphs.",
        "entries_affected": [
          "10.1016/j.neuron.2017.12.037",
          "10.1038/s41586-019-1716-z",
          "10.1162/netn_a_00345"
        ]
      },
      {
        "dimension": "study_system",
        "description": "Graphs differ: 47-area binary mouse cortex parcellation, 43-area Cre-driver AAV cortex+thalamus, ML-imputed mouse+macaque weighted matrix. Density values are computed on non-identical underlying matrices.",
        "entries_affected": [
          "10.1016/j.neuron.2017.12.037",
          "10.1038/s41586-019-1716-z",
          "10.1162/netn_a_00345"
        ]
      }
    ],
    "audit_verdict": "SPLIT",
    "comparison_id": "mouse-cc-graph-density",
    "comparison_name": "Mouse vs macaque inter-areal cortico-cortical graph density",
    "comparison_type": "convergent evidence",
    "what_it_reveals": "Three lines of analysis converge on the same global picture of the mouse cortico-cortical weighted graph: dense, log-normal-weighted, and rule-based but shallowly hierarchical.",
    "homogeneity_check": {
      "caveats": [
        "Gămănuţ et al. report binary density on a 47-area mouse parcellation; Harris et al. operate on a Cre-driver tracer dataset across 43 isocortical areas; Beul et al. work on inter-areal weighted matrices in both mouse and macaque.",
        "Numerical comparison should not equate '97% density' (binary) with the '85–90% weighted-link prediction accuracy' — these are different statistics on overlapping but non-identical matrices."
      ],
      "n_definition_uniform": "false",
      "scope_region_uniform": "false",
      "taxonomic_level_uniform": "false",
      "scope_population_uniform": "false"
    },
    "suggested_plot_type": "grouped bar",
    "mandatory_caption_caveats": [
      "Row 1's 0.97 is a binary graph density; row 3's 85–90% is a weighted-link prediction accuracy. The two scalars are not the same quantity and should not be plotted on a shared y-axis.",
      "Row 2 reports a qualitative claim (shallow hierarchy depth) with no scalar to plot.",
      "Phase 7 writer: SPLIT verdict — implement as: Convert to a table summarising each study's graph definition and scalar; or split into a binary-density panel and a separate prediction-accuracy panel."
    ]
  },
  "section_id": "section_08",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_08_evidence_package.json",
  "target_ref": "wiki_page:computationalreviewrecurrence-08-cross-areal",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-08-cross-areal",
  "source_path": "evidence/section_08_evidence_package.json",
  "source_refs": [
    "paper:paper-259a50694faa",
    "paper:paper-d7dd6ae02de1",
    "paper:paper-ec3295162895"
  ],
  "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"
  },
  "generation_status": "complete",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "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"
}