Version history

{
  "kind": "infographic",
  "prompt": "Multiple computational modeling studies predict distinct EEG and circuit-level signatures of SST vs PV interneuron dysfunction in schizophrenia. This comparison shows convergence in predicting that SST dysfunction particularly affects theta-band activity and dendritic processing, while PV dysfunction affects gamma oscillations.",
  "provider": "other",
  "raw_fields": {
    "papers": [
      {
        "doi": "10.1101/2023.08.11.553052",
        "value": "SST interneuron inhibition loss increases theta power; PV inhibition loss shifts alpha to beta frequencies",
        "method": "spiking neural network simulation",
        "metric": "EEG biomarker predictions for SST vs PV dysfunction",
        "cite_key": "Rosanally2023",
        "condition": "schizophrenia vs healthy",
        "study_system": "computational model of human prefrontal microcircuit",
        "value_source_sentence": "reduced SST interneuron inhibition increasing theta power, and reduced PV interneuron inhibition leading to a right shift from alpha to beta frequencies"
      }
    ],
    "n_analyzed": "N/A (computational)",
    "n_definition": "model simulations",
    "scope_region": "prefrontal cortex",
    "comparison_id": "sst-schizophrenia-circuit-models",
    "comparison_name": "Computational Predictions of SST Dysfunction Effects in Schizophrenia",
    "comparison_type": "convergent evidence",
    "taxonomic_level": "cell type (SST+ vs PV+)",
    "what_it_reveals": "Multiple computational modeling studies predict distinct EEG and circuit-level signatures of SST vs PV interneuron dysfunction in schizophrenia. This comparison shows convergence in predicting that SST dysfunction particularly affects theta-band activity and dendritic processing, while PV dysfunction affects gamma oscillations.",
    "scope_population": "SST and PV interneuron circuits",
    "homogeneity_check": {
      "caveats": [
        "All computational/modeling studies - no direct experimental validation",
        "Different model architectures and parameter ranges",
        "Predictions depend on specific assumptions about SST and PV connectivity patterns"
      ],
      "comparable": true
    },
    "suggested_plot_type": "heatmap"
  },
  "section_id": "section_11_evidence_package",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST/blob/89b7e9787cd90e942b0adb531d549af3ddad30f1/evidence/section_11_evidence_package.json",
  "target_ref": "wiki_page:computationalreviewsst-11",
  "review_repo": "ComputationalReviewSST",
  "section_ref": "wiki_page:computationalreviewsst-11",
  "source_path": "evidence/section_11_evidence_package.json",
  "source_refs": [
    "paper:paper-23d0bc8e2cc3"
  ],
  "section_title": "Species Translation and Human Relevance",
  "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": "89b7e9787cd90e942b0adb531d549af3ddad30f1",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST"
  },
  "generation_status": "complete",
  "review_bundle_ref": "analysis_bundle:ab-8466d095488a",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST/blob/89b7e9787cd90e942b0adb531d549af3ddad30f1/evidence/section_11_evidence_package.json",
  "commit_sha": "89b7e9787cd90e942b0adb531d549af3ddad30f1",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST"
}