Daniel J. Lurie, Daniel Kessler, Danielle S. Bassett, Richard F. Betzel, Michael Breakspear, Shella Kheilholz, Aaron Kucyi, Raphaël Liégeois, Martin A. Lindquist, Anthony Randal McIntosh, Russell A. Poldrack, James M. Shine, William Hedley Thompson, Natalia Z. Bielczyk, Linda Douw, Dominik Kraft, Robyn L. Miller, Muthuraman Muthuraman, Lorenzo Pasquini, Adeel Razi, Diego Vidaurre, Hua Xie, Vince D. Calhoun
- The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area.The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.…
MetadatenAuthor: | Daniel J. Lurie, Daniel Kessler, Danielle S. Bassett, Richard F. Betzel, Michael Breakspear, Shella Kheilholz, Aaron Kucyi, Raphaël Liégeois, Martin A. Lindquist, Anthony Randal McIntosh, Russell A. Poldrack, James M. Shine, William Hedley Thompson, Natalia Z. Bielczyk, Linda Douw, Dominik Kraft, Robyn L. Miller, Muthuraman MuthuramanORCiDGND, Lorenzo Pasquini, Adeel Razi, Diego Vidaurre, Hua Xie, Vince D. Calhoun |
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URN: | urn:nbn:de:bvb:384-opus4-1101228 |
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Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/110122 |
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ISSN: | 2472-1751OPAC |
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Parent Title (English): | Network Neuroscience |
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Publisher: | MIT Press |
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Place of publication: | Cambridge, MA |
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Type: | Article |
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Language: | English |
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Year of first Publication: | 2020 |
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Publishing Institution: | Universität Augsburg |
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Release Date: | 2023/12/13 |
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Tag: | Applied Mathematics; Artificial Intelligence; Computer Science Applications; General Neuroscience |
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Volume: | 4 |
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Issue: | 1 |
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First Page: | 30 |
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Last Page: | 69 |
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DOI: | https://doi.org/10.1162/netn_a_00116 |
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Institutes: | Fakultät für Angewandte Informatik |
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| Fakultät für Angewandte Informatik / Institut für Informatik |
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| Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Informatik in der Medizintechnik |
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Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
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Licence (German): | CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |
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