{
    "abstract": "Effective connectivity (EC), which reflects the causal interactions between brain regions, is fundamental to understanding information processing in the brain; however, traditional methods for obtaining EC, which rely on neural responses to stimulation, are often invasive or limited in spatial coverage, making them unsuitable for whole-brain EC mapping in humans. Here, to address this gap, we introduce Neural Perturbational Inference (NPI), a data-driven framework for mapping whole-brain EC. NPI employs an artificial neural network trained to model large-scale neural dynamics, serving as a computational surrogate of the brain. By systematically perturbing all regions in the surrogate brain and analyzing the resulting responses in other regions, NPI maps the directionality, strength and excitatory/inhibitory properties of brain-wide EC. Validation of NPI on generative models with known ground-truth EC demonstrates its superiority over existing methods such as Granger causality and dynamic causal modeling. When applied to resting-state functional magnetic resonance imaging data across diverse datasets, NPI reveals consistent, structurally supported EC patterns. Furthermore, comparisons with cortico-cortical evoked potential data show a strong resemblance between NPI-inferred EC and real stimulation propagation patterns. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain's functional architecture and facilitating both neuroscience studies and clinical applications.",
    "authors": [
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.\nDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.",
            "firstname": "Zixiang",
            "initials": "Z",
            "lastname": "Luo"
        },
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.",
            "firstname": "Kaining",
            "initials": "K",
            "lastname": "Peng"
        },
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.",
            "firstname": "Zhichao",
            "initials": "Z",
            "lastname": "Liang"
        },
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.",
            "firstname": "Shengyuan",
            "initials": "S",
            "lastname": "Cai"
        },
        {
            "affiliation": "Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.",
            "firstname": "Chenyu",
            "initials": "C",
            "lastname": "Xu"
        },
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.",
            "firstname": "Dan",
            "initials": "D",
            "lastname": "Li"
        },
        {
            "affiliation": "Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.\nDivision of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.",
            "firstname": "Yu",
            "initials": "Y",
            "lastname": "Hu"
        },
        {
            "affiliation": "Department of Physics, Centre for Nonlinear Studies, Hong Kong Baptist University, Hong Kong SAR, China.",
            "firstname": "Changsong",
            "initials": "C",
            "lastname": "Zhou"
        },
        {
            "affiliation": "Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China. liuqy@sustech.edu.cn.",
            "firstname": "Quanying",
            "initials": "Q",
            "lastname": "Liu"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.",
    "doi": "10.1038/s41592-025-02654-x\n10.1126/science.1238411\n10.1038/s41562-020-01003-6\n10.1038/s41583-023-00718-5\n10.1002/jmri.27188\n10.1016/j.euroneuro.2010.03.008\n10.1073/pnas.1001791107\n10.1371/journal.pcbi.1011274\n10.1016/j.neuron.2023.03.002\n10.1038/s41586-023-06683-4\n10.1038/s41593-024-01570-1\n10.1016/j.neuroimage.2013.12.009\n10.1109/JBHI.2022.3210303\n10.1038/s41467-020-20655-6\n10.1038/s41593-020-0699-2\n10.1016/j.patrec.2021.06.030\n10.1038/s41592-023-02040-5\n10.1073/pnas.2105031118\n10.1073/pnas.1911240117\n10.1016/j.neuroimage.2011.10.018\n10.1162/netn_a_00061\n10.1016/j.neuroimage.2013.05.041\n10.1038/nature18933\n10.1152/jn.00338.2011\n10.1038/nature13186\n10.1093/cercor/bhs270\n10.1093/cercor/1.1.1\n10.1093/cercor/bhx179\n10.1093/texcom/tgaa015\n10.1016/j.dcn.2022.101115\n10.1093/brain/awab362\n10.1038/mp.2013.78\n10.1212/WNL.0b013e3181cb3e25\n10.1016/j.jneumeth.2013.10.018\n10.1016/j.neuroimage.2020.117046\n10.1016/j.neuron.2016.12.035\n10.1056/NEJMoa1205158\n10.1038/s41591-021-01480-w\n10.1038/s41591-020-01175-8\n10.1162/netn_a_00116\n10.1523/JNEUROSCI.1091-13.2013\n10.1371/journal.pbio.0060159\n10.1523/JNEUROSCI.16-13-04207.1996\n10.1016/j.neuroimage.2013.04.127\n10.1016/j.neuron.2019.01.017",
    "journal": "Nature methods",
    "keywords": [],
    "methods": null,
    "publication_date": "2025-04-23",
    "pubmed_id": "40263586\n24179229\n37438433\n32557893\n20471808\n20439736\n38215166\n37001524\n37914938\n38388734\n24345387\n33441557\n32895567\n37919419\n34819365\n32193345\n22019881\n30793083\n23684880\n27437579\n24695228\n23010748\n1822724\n28981612\n34296093\n35636343\n35416942\n23774715\n20042704\n24200508\n32603858\n28132829\n23406026\n34608328\n33462446\n32043043\n23825427\n18597554\n8753882\n23668970\n30744986",
    "results": null,
    "title": "Mapping effective connectivity by virtually perturbing a surrogate brain.",
    "xml": "<Element 'PubmedArticle' at 0x78850643fec0>"
}