Details
- scope
- mouse and macaque cortex, retrograde tracer interareal connectivity data
- section_id
- section_02
- source_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_02_evidence_package.json
- effect_size
- binary link AUC ≥ 0.8 (macaque); medium/strong weighted link accuracy 85-90% (mouse), 70-80% (macaque)
- review_repo
- ComputationalReviewRecurrence
- section_ref
- wiki_page:computationalreviewrecurrence-02-anatomy-primer
- source_kind
- review_finding
- source_path
- evidence/section_02_evidence_package.json
- source_span
- 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.
- study_system
- mouse and macaque cortex, retrograde tracer interareal connectivity data
- section_title
- 2. Mouse-cortex anatomy primer — areal map, layer structure, projection-class nomenclature (IT / PT / CT), tools available for E→E dissection (paired patch, Cre lines, two-photon-targeted patch, optogenetics, EM reconstruction)
- evidence_summary
- Machine-learning predictability framework applied to retrograde tract-tracing-derived weighted interareal cortical matrices in mouse and macaque.
- review_bundle_ref
- analysis_bundle:ab-d9c479db9be9
- replication_status
- replication_unknown
- review_package_ref
- analysis_bundle:ab-d9c479db9be9
- source_artifact_ref
- wiki_page:computationalreviewrecurrence-02-anatomy-primer
- origin_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_02_evidence_package.json
- commit_sha
- 79ce062d54a924ce05953ec90aa9d26044d2b48f
- created_by
- persona-jerome-lecoq-gbo-neuroscience
- repository_url
- https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence
Raw fields (5)
- claim_text
Across mouse and macaque, weighted interareal cortical connectivity is highly predictable: medium- and strong-weight links can be predicted from network features with ~85-90% accuracy in mouse (70-80% in macaque), whereas weak links remain unpredictable.
- raw_fields
{ "n": null, "doi": "10.1162/netn_a_00345", "claim": "Across mouse and macaque, weighted interareal cortical connectivity is highly predictable: medium- and strong-weight links can be predicted from network features with ~85-90% accuracy in mouse (70-80% in macaque), whereas weak links remain unpredictable.", "cite_key": "Molnar2024", "evidence": "Machine-learning predictability framework applied to retrograde tract-tracing-derived weighted interareal cortical matrices in mouse and macaque.", "effect_size": "binary link AUC ≥ 0.8 (macaque); medium/strong weighted link accuracy 85-90% (mouse), 70-80% (macaque)", "text_access": "abstract_only", "study_system": "mouse and macaque cortex, retrograde tracer interareal connectivity data", "argument_role": "supporting", "replication_status": "replication_unknown", "claim_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.", "source_provenance_status": "non_substring_match", "replication_evidence_dois": [], "effect_size_source_sentence": "At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. 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." }- source_refs
[ "paper:paper-d7dd6ae02de1" ]
- evidence_refs
[ { "ref": "paper:paper-d7dd6ae02de1" } ]- 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" }