Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

By Nora Leonardi, Jonas Richiardi, M. Gschwind, S. Simioni, J. Annoni, M. Schluep, Patrik Vuilleumier, D. Van de Ville. NeuroImage, 2013

410 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e6d1b8cadffff82aab977dc83bb4eac478e495a2

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

On spurious and real fluctuations of dynamic functional connectivity during rest (2015, positive): “(2012) and less variable dynFC with longer windows is a well documented empirical observation (e.g., Chang and Glover, 2010; Hutchison et al., 2013b; Leonardi et al., 2013).”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “For example, Du et al. used ICA to extract individual spatial maps as the initial features and then combined a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD) in the classification of SZ (Du et al., 2012).”
Learning Laplacian Matrix in Smooth Graph Signal Representations (2014, positive): “Two other graph learning works have been described in [31] and [32], where the authors have proposed to use, respectively, correlations between wavelet coefficients of the time series of brain signals, and Principal Component Analysis applied to correlation matrices, to estimate functional connectiv”
State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016, positive): “It should be emphasized that, compared to the previous work that mostly used a sliding window technique (Leonardi et al., 2013) to estimate dynamic functional connectivity, our method could estimate the functional dynamics in rs-fMRI, where a varying number of time points can be involved in differen”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “…the course of the scan (within a few seconds) and reveal a number of functional connectivity (FC) states in the brain, which can be sensitive to changes related to neurological disorders (Sakoğlu et al., 2010; Leonardi et al., 2013a, 2013b; Damaraju et al., 2014; Li et al., 2014; Ou et al., 2015).”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “To address this limitation, recent efforts have begun to employ so-called soft-partitioning methods that detect coherent patterns in mesoscale neural activity and connectivity [19, 32, 60, 67].”