Time-varying functional network information extracted from brief instances of spontaneous brain activity

By Xiao Liu, Jeff H. Duyn. Proceedings of the National Academy of Sciences of the United States of America, 2013

677 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3db9b2819a5ed060611c64826ab8e00a0bad7162

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “…in both animals and humans demonstrated that the spontaneous blood oxygen level dependent (BOLD) signals measured during rest exhibit intrinsic spatiotemporal dynamic organization (Chang and Glover, 2010; Hutchison et al., 2013b; Keilholz et al., 2013; Liu and Duyn, 2013; Sakoğlu et al., 2010).”
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, background): “Our discussion of the implications of these observations is motivated by several recent papers that have also grappled with the observation of both stable and dynamic components of functional network configurations [48–53].”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “Beyond the methods described above, other approaches worth noting include time-frequency analysis (Chang and Glover, 2010, Yaesoubi et al., 2015), phase- dependent eigenconnectivities (Preti et al., 2016) or point process analysis (Tagliazucchi et al., 2012a, Liu and Duyn, 2013). ii.”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “…although some studies have demonstrated the existence of BOLD-like response in resting state data (Petridou et al., 2013; Tagliazucchi et al., 2012; Liu and Duyn, 2013; Karahanoğlu and Van de Ville, 2015), it is still uncertain whether BOLD signal changes at rest achieve the same magnitude as in…”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “This activity possibly mirrors free thought modalities (Doucet et al., 2012) associated to neural activity events localized in space and time or to faster electrophysiological processes (Liu and Duyn,2013),whicharenecessarily overlooked when averagingcorrela-tionsthroughoutlongrecordingperiods.”
The Global Signal in fMRI: Nuisance or Information? (2017, positive): “…the spatial patterns observed in the retained time points are similar to those that have been observed in studies showing that key features of resting-state functional connectivity maps can be obtained using a fraction of the original time points (Tagliazucchi et al., 2016; Liu and Duyn, 2013).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “proposed clustering selected individual BOLD volumes of a resting-state scan based on spatial similarity (Liu and Duyn, 2013).”
Communication dynamics in complex brain networks (2017, background): “Notably, functional connections, whether static or dynamic 60,61 , do not resolve the trajectories or temporal patterns of signal propagation through anatomical connections; instead, the co-variation of the activity of two neural elements expresses the aggregation of local, global, feedforward and…”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, background): “Such a viewpoint is taken, for example, in Majeed et al. (2011) and Liu and Duyn (2013) (see Hutchison et al., 2013a for a review).”
Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015, methodology): “Proc Natl Acad Sci USA 108(18):7641–7646.”
Spatio-Temporal Data Mining (2017, methodology): “When clustering spatial maps (sets of measurements from all locations in an ST raster on different times), the central objective is to find groups of time stamps that have similar spatial maps, e.g., time stamps with similar maps of brain activity [Liu and Duyn 2013].”
The dynamic functional connectome: State-of-the-art and perspectives (2017, positive): “…that these brief neuronal events would yield only short periods of co-activation and co-deactivation that are missed in stationary FC analysis, Liu and Duyn (2013) refined the technique by applying the point selection only to a specific seed timecourse, and then retaining the original (not…”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “These coactivation patterns are reliable across individuals (Gutierrez-Barragan, Basson, Panzeri, & Gozzi, 2019; Liu & Duyn, 2013) and may preferentially occur at distinct phases of the infraslow global signal (Gutierrez-Barragan et al., 2019).”