{
  "abstract": "Behavior arises from the coordinated activity across anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates robust, scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce current-based decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that autonomously produce dynamics consistent with experimentally obtained neural data. CURBD leverages the functional interactions inferred from such models to reveal directional currents between multiple brain regions simultaneously. We first show that CURBD accurately isolates inter-region currents in simulated, ground-truth networks with known connectivity and dynamics. We then apply CURBD to multi-region neural recordings obtained from many species-larval zebrafish, mice, macaques, and humans-to demonstrate the widespread applicability of CURBD in untangling brain-wide interactions and inter-area communication principles underlying behavior.",
  "abstract_shown": true,
  "affiliations": [
    "Département des neurosciences, Université de Montréal, Montréal, QC, Canada; Quebec Artificial Intelligence Institute (Mila), Montréal, QC, Canada",
    "Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA",
    "Harvard Medical School, Boston, MA, USA",
    "Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA",
    "Cedars-Sinai Medical Center, Los Angeles, CA, USA",
    "Department of Bioengineering, Stanford University, Stanford, CA, USA",
    "California Institute of Technology, Pasadena, CA",
    "Neurosciences Graduate Program, Stanford University, Stanford, CA, USA",
    "Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Cedars-Sinai Medical Center, Los Angeles, CA, USA",
    "Cedars-Sinai Medical Center, Los Angeles, CA, USA; California Institute of Technology, Pasadena, CA, USA",
    "Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA",
    "Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Kempner Institute for Natural and Artificial Intelligence, Harvard University, Boston, MA 02134, USA. Electronic address: kanaka_rajan@hms.harvard.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 16,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Matthew G. Perich",
      "orcid": "0000-0001-9800-2386"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Charlotte Arlt",
      "orcid": "0000-0002-1467-6592"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Sofia Soares",
      "orcid": "0000-0002-4594-0202"
    },
    {
      "affs": [
        2,
        1
      ],
      "name": "Siyan Zhou",
      "orcid": "0000-0002-8840-5066"
    },
    {
      "affs": [
        4,
        5,
        1
      ],
      "name": "Manuel Beirán",
      "orcid": "0000-0002-0275-5728"
    },
    {
      "affs": [
        6,
        7,
        5
      ],
      "name": "Aaron S. Andalman"
    },
    {
      "affs": [
        8,
        1
      ],
      "name": "Tyler Benster"
    },
    {
      "affs": [
        9,
        7,
        5
      ],
      "name": "Megan E. Young"
    },
    {
      "affs": [
        10,
        1
      ],
      "name": "Clayton P. Mosher",
      "orcid": "0000-0002-9213-3059"
    },
    {
      "affs": [
        11,
        3
      ],
      "name": "Juri Minxha",
      "orcid": "0000-0003-4942-3269"
    },
    {
      "affs": [
        9,
        1
      ],
      "name": "Eugene Carter"
    },
    {
      "affs": [
        11
      ],
      "name": "Ueli Rutishauser",
      "orcid": "0000-0002-9207-7069"
    },
    {
      "affs": [
        9
      ],
      "name": "Peter H. Rudebeck",
      "orcid": "0000-0002-1411-7555"
    },
    {
      "affs": [
        2
      ],
      "name": "Christopher D. Harvey",
      "orcid": "0000-0001-9850-2268"
    },
    {
      "affs": [
        12
      ],
      "name": "Karl A. Deisseroth",
      "orcid": "0000-0001-9440-3967"
    },
    {
      "affs": [
        13
      ],
      "name": "Kanaka Rajan",
      "orcid": "0000-0003-2749-2917"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1awqyx66wd403.bib",
  "cited_by": 69,
  "doi": "10.1016/j.neuron.2026.07.016",
  "fulltext_url": null,
  "id": "1awqyx66wd403",
  "issns": [
    "0896-6273",
    "1097-4199"
  ],
  "landing_url": "https://doi.org/10.1016/j.neuron.2026.07.016",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "2805|Neural dynamics and brain function"
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  "pdf_url": null,
  "pmcid": "PMC13565020",
  "pmid": "42567158",
  "published": "2026-08-01",
  "publisher": "Cell Press",
  "ris": "https://research.draconitic.com/paper/1awqyx66wd403.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:53Z",
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      "url": "https://doi.org/10.1016/j.neuron.2026.07.016"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural dynamics and brain function",
      "subfield": "neuroscience"
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  "title": "Inferring brain-wide interactions using data-constrained recurrent neural network models",
  "title_text": "Inferring brain-wide interactions using data-constrained recurrent neural network models",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:12Z",
  "url": "https://research.draconitic.com/paper/1awqyx66wd403",
  "venue": "Neuron",
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}