On spurious and real fluctuations of dynamic functional connectivity during rest

By Nora Leonardi, D. Van de Ville. NeuroImage, 2015

819 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/da882d12def737d5e387f5e319440174ad7c400d

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Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “…suggests that the optimal window length to minimize these concerns may be shorter than the minimum of ∼ 60 s that has been previously recommended (Leonardi & Van De Ville, 2015; Zalesky & Breakspear, 2015), and one can consider the choice of window size to be a tunable ﬁlter that can be…”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, background): “, 2014), deterministic oscillations (Leonardi et al., 2014; Lindquist et al., 2014), and transients (Kang et al.”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, neutral): “To determine whether using a sliding-‐‐window Pearson’s correlation gave similar results to the MTD in this study, we re-‐‐analyzed the data in our experiment using a Pearson’s correlation with a sliding window of w = 14 (the window length used in our”
Dynamic models of large-scale brain activity (2017, background): “First, linear correlations in time-series data can give rise to (spurious) fluctuations in time-windowed statistics 115,116 .”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “By using simple sinusoids (Leonardi and Van De Ville, 2015) reported that the minimum window length should at least be equal to 1/f min in order to avoid spurious fluctuations arising due to the SWC algorithm itself.”
Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015, methodology): “(49), we used the Morlet wavelet transform to estimate the functional connectivity between each pair of brain regions in the frequency interval 0.”
Brain network dynamics are hierarchically organized in time (2017, methodology): “This approach has access to temporal scales as fast as the data modality allows and overcomes the statistical limitations of the sliding windows technique when applied to the analysis of brain dynamics (14, 15, 16, 17).”