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The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome

James K. RuffleQueen Square Institute of Neurology, University College London, UK; Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Centre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK. Electronic address: [email protected]Harpreet HyareQueen Square Institute of Neurology, University College London, UK; Department of Radiology, University College London Hospital NHS Foundation Trust, London, UKMatthew A. HowardDepartment of Neuroimaging, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK Adam D. FarmerCentre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK; Department of Gastroenterology, University Hospitals Midlands NHS Trust, Stoke on Trent, Staffordshire, UK; Institute of Applied Clinical Sciences, University of Keele, Keele, UKA. Vania ApkarianDepartment of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA Steven WilliamsDepartment of Neuroimaging, King's College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK Qasim AzizCentre for Neuroscience and Trauma, Blizard Institute, Wingate Institute of Neurogastroenterology, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UKParashkev NachevQueen Square Institute of Neurology, University College London, UK

2021en

ABI

Annotatsiya

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods-and data scales-hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system-a multidimensional, generative network-that renders its richness tractable within future models of its function in health and disease.

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Identifikatorlar

DOI: 10.1016/j.cortex.2021.06.012

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