Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
Emily S. Finn,Xilin Shen,Dustin Scheinost,Monica D. Rosenberg,Jessica S. Huang,Marvin M. Chun,Xenophon Papademetris,R. Todd Constable +7 more
TLDR
In this article, the authors show that every individual has a unique pattern of functional connections between brain regions, which act as a fingerprint that can accurately identify the individual from a large group.Abstract:
This study shows that every individual has a unique pattern of functional connections between brain regions. This functional connectivity profile acts as a ‘fingerprint’ that can accurately identify the individual from a large group. Furthermore, an individual's connectivity profile can predict his or her level of fluid intelligence.read more
Citations
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Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion
Jing Sui,Shile Qi,Theo G.M. van Erp,Juan R. Bustillo,Rongtao Jiang,Dongdong Lin,Jessica A. Turner,Eswar Damaraju,Andrew R. Mayer,Yue Cui,Zening Fu,Yuhui Du,Jiayu Chen,Steven G. Potkin,Adrian Preda,Daniel H. Mathalon,Daniel H. Mathalon,Judith M. Ford,Judith M. Ford,James T. Voyvodic,Bryon A. Mueller,Aysenil Belger,Sarah McEwen,Daniel S. O'Leary,Agnes B. McMahon,Tianzi Jiang,Vince D. Calhoun +26 more
TL;DR: Results highlight the salience network, corpus callosum, central executive and default-mode networks, and fALFF as modality-specific biomarkers of generalized cognition as well as co-varying multimodal signatures that can be used as predictors of multi-domain cognition.
Journal ArticleDOI
Exploration, Inference, and Prediction in Neuroscience and Biomedicine.
TL;DR: In this article, the antagonistic philosophies behind two quantitative approaches: certifying robust effects in understandable variables, and evaluating how accurately a built model can forecast future outcomes, are discussed.
Journal ArticleDOI
Beyond Functional Connectivity: Investigating Networks of Multivariate Representations
TL;DR: The recent emergence of multivariate and nonlinear methods for studying interactions between brain regions bring sensitivity to fluctuations in multivariate information, and offer the possibility to ask not only whether brain regions interact, but how they do so.
Journal ArticleDOI
Higher-order interactions in complex networks of phase oscillators promote abrupt synchronization switching
TL;DR: In this article, higher-order interactions between coupled phase oscillators, encoded microscopically in a simplicial complex, give rise to added nonlinearity in the macroscopic system dynamics that induces abrupt synchronization transitions via hysteresis and bistability of synchronized and incoherent states.
Journal ArticleDOI
Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies.
Angus Ho Ching Fong,Kwangsun Yoo,Monica D. Rosenberg,Sheng Zhang,Chiang-Shan R. Li,Dustin Scheinost,R. Todd Constable,Marvin M. Chun +7 more
TL;DR: DFC predicts attention performance across individuals by considering temporal changes in network structure and combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant.
References
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Research domain criteria (RDoC): toward a new classification framework for research on mental disorders
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Correspondence of the brain's functional architecture during activation and rest.
Stephen M. Smith,Peter T. Fox,Karla L. Miller,David C. Glahn,P. Mickle Fox,Clare E. Mackay,Nicola Filippini,Kate E. Watkins,Roberto Toro,Angela R. Laird,Christian F. Beckmann,Christian F. Beckmann +11 more
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