Literature DB >> 32910894

Dynamic Functional Connectivity of Resting-State Spinal Cord fMRI Reveals Fine-Grained Intrinsic Architecture.

Nawal Kinany1, Elvira Pirondini2, Silvestro Micera3, Dimitri Van De Ville4.   

Abstract

The neuroimaging community has shown tremendous interest in exploring the brain's spontaneous activity using functional magnetic resonance imaging (fMRI). On the contrary, the spinal cord has been largely overlooked despite its pivotal role in processing sensorimotor signals. Only a handful of studies have probed the organization of spinal resting-state fluctuations, always using static measures of connectivity. Many innovative approaches have emerged for analyzing dynamics of brain fMRI, but they have not yet been applied to the spinal cord, although they could help disentangle its functional architecture. Here, we leverage a dynamic connectivity method based on the clustering of hemodynamic-informed transients to unravel the rich dynamic organization of spinal resting-state signals. We test this approach in 19 healthy subjects, uncovering fine-grained spinal components and highlighting their neuroanatomical and physiological nature. We provide a versatile tool, the spinal innovation-driven co-activation patterns (SpiCiCAP) framework, to characterize spinal circuits during rest and task, as well as their disruption in neurological disorders.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  dynamic functional connectivity; fMRI; networks; resting-state; spinal cord

Year:  2020        PMID: 32910894     DOI: 10.1016/j.neuron.2020.07.024

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  3 in total

1.  Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis.

Authors:  Andreas Rowald; Salif Komi; Robin Demesmaeker; Edeny Baaklini; Sergio Daniel Hernandez-Charpak; Edoardo Paoles; Hazael Montanaro; Antonino Cassara; Fabio Becce; Bryn Lloyd; Taylor Newton; Jimmy Ravier; Nawal Kinany; Marina D'Ercole; Aurélie Paley; Nicolas Hankov; Camille Varescon; Laura McCracken; Molywan Vat; Miroslav Caban; Anne Watrin; Charlotte Jacquet; Léa Bole-Feysot; Cathal Harte; Henri Lorach; Andrea Galvez; Manon Tschopp; Natacha Herrmann; Moïra Wacker; Lionel Geernaert; Isabelle Fodor; Valentin Radevich; Katrien Van Den Keybus; Grégoire Eberle; Etienne Pralong; Maxime Roulet; Jean-Baptiste Ledoux; Eleonora Fornari; Stefano Mandija; Loan Mattera; Roberto Martuzzi; Bruno Nazarian; Stefan Benkler; Simone Callegari; Nathan Greiner; Benjamin Fuhrer; Martijn Froeling; Nik Buse; Tim Denison; Rik Buschman; Christian Wende; Damien Ganty; Jurriaan Bakker; Vincent Delattre; Hendrik Lambert; Karen Minassian; Cornelis A T van den Berg; Anne Kavounoudias; Silvestro Micera; Dimitri Van De Ville; Quentin Barraud; Erkan Kurt; Niels Kuster; Esra Neufeld; Marco Capogrosso; Leonie Asboth; Fabien B Wagner; Jocelyne Bloch; Grégoire Courtine
Journal:  Nat Med       Date:  2022-02-07       Impact factor: 87.241

Review 2.  Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.

Authors:  Taban Eslami; Fahad Almuqhim; Joseph S Raiker; Fahad Saeed
Journal:  Front Neuroinform       Date:  2021-01-20       Impact factor: 4.081

3.  Scientific Achievements of Our Era: "Making the Lame Walk".

Authors:  Martin N Stienen; Yoon Ha
Journal:  Neurospine       Date:  2022-03-31
  3 in total

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