Temporally-independent functional modes of spontaneous brain activity

By Stephen M. Smith, Karla L. Miller, Steen Moeller, Junqian Xu, E. Auerbach, Mark William Woolrich, Christian F. Beckmann, Mark Jenkinson, Jesper L. R. Andersson, Matthew F. Glasser and 4 more. Proceedings of the National Academy of Sciences of the United States of America, 2012

741 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7a83ce7ad65cbd0b4e5d6c937f1d02490bc6c53a

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Citations in the corpus

Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, critical): “In other cases, it has been found wanting (Bohland, et al., 2012).”
The Human Connectome Project: A data acquisition perspective (2012, background): “, 2006); and richer modeling of the temporal dynamics of R-fMRI fluctuations and in the interactions between different functional areas (Smith et al., 2012).”
Fast transient networks in spontaneous human brain activity (2014, background): “One good example of this is that, when using temporal ICA on fMRI data, no single DMN is found (Smith et al., 2012).”
Network attributes for segregation and integration in the human brain. (2013, background): “higher fMRI sampling rates has identified multiple ‘temporal functional modes’ [80 ].”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “, the time series of a single node may have partial correlations with that of multiple networks), the FC between two regions that is attributed to their involvement in one particular network can appear to change if the time series of overlapping networks are not appropriately separated (Smith et al.”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, background): “Also, the underlying neural processes in the data may well be more non-Gaussian in space than in time (particularly for resting-state data), adding to the greater robustness of spatial ICA (Smith et al., 2012).”
Human Connectome Project Informatics: quality control, database services, and data visualization (2013, methodology): “4 min) support very high dimensionality individual subject ICA of 200 components ormore (Smith et al., 2012).”
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry (2012, methodology): “…is applying state-of-the-art resting-state structural and functional MRI (R-fMRI) and diffusion tensor imaging (DTI) techniques (Feinberg et al., 2010; Moeller et al., 2010; Feinberg and Yacoub, 2012; Smith et al., 2012), which minimize the obsolescence of these data along with participant burden.”
Ultra-Fast MRI of the Human Brain with Simultaneous Multi-Slice Imaging (2013, background): “Yet as shown in functional connectivity research [12,59],”
Time-resolved resting-state brain networks (2014, background): “Larger regions are more likely to encapsulate multiple temporally independent modes (24) that might cancel each other out when averaged.”
Integrated strategy for improving functional connectivity mapping using multiecho fMRI (2013, methodology): “To date, ICA has been applied in both spatial and temporal domains to produce connectivity maps (17).”
Simultaneous multislice (SMS) imaging techniques (2015, methodology): “In addition, the increase in the (temporal) degrees-offreedom allows the application of advanced analysis strategies such as temporal independent component analysis or ICA (94,95), as well as a denser temporal sampling of physiological fluctuations in the fMRI signal (96), which helps in their ident”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “, 1997), especially for recently developed fast imaging sequences (Feinberg et al., 2010; Moeller et al., 2010; Smith et al., 2012).”
Electroencephalographic Resting-State Networks: Source Localization of Microstates (2017, neutral): “, 2014) and they strongly overlap in space and time (Karahanoğlu and Van De Ville, 2015; Smith et al., 2012), suggesting dynamic interactions between RSNs.”
Time-varying functional network information extracted from brief instances of spontaneous brain activity (2013, background): “Recently, an alternative to spatial ICA was proposed that combines spatial and temporal ICA to extract temporally independent components from resting-state data (32).”