Estimating the number of independent components for functional magnetic resonance imaging data

By Yi-Ou Li, T. Adalı, Vince D. Calhoun. Human Brain Mapping, 2007

939 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d4e63e0225d61ba818e12d5cfbab6138040b72f3

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Citations in the corpus

A method for functional network connectivity among spatially independent resting-state components in schizophrenia (2008, methodology): “Dimension estimation, to determine the number of components, was performed using the minimum description length criteria, modified to account for spatial correlation (Li et al., 2007).”
Functional segmentation of the brain cortex using high model order group PICA (2009, methodology): “When the model order of the ICA estimation is increased, the separated BOLD signal sources have been shown to split into several functional nodes [Li et al., 2007; Ma et al., 2007; Malinen et al., 2007; Eichele et al., 2008].”
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data (2008, neutral): “There are a number of approaches for estimating the number of components using information theoretic approaches (Beckmann and Smith, 2004; Li et al., 2007).”
Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients (2010, methodology): “Dimensionality estimation to determine the number of components was performed using the minimum description length criteria, modified to account for spatial correlation (Li et al., 2007).”
Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study (2012, methodology): “ITC have been less successfuly applied to real fMRI data due to temporally and spatially correlated noise (Cordes and Nandy, 2006; Li et al., 2007), though Li et al. (2007) have proposed a subsampling scheme to obtain a set of effectively independent data samples onwhich the ITC formulaemay be…”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “While employing ICA to extract functional networks, the number of components is typically estimated using informationtheoretic principles, such as a modified minimum description length (MDL) criteria (Li et al., 2007).”
Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects (2009, methodology): “The number of components was determined using the minimum description length criteria (44).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “To address this issue, in [42], a subsampling scheme is introduced to obtain a set of effectively i.”
Group information guided ICA for fMRI data analysis (2013, neutral): “The number of ICs was automatically determined to be 18 using the MDL criterion (Li et al., 2007).”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, neutral): “…to estimate the number of components automatically: Akaike information criterion (Akaike, 1973), Bayesian information criterion (Schwarz, 1978), minimum description length (Rissanen, 1978), probabilistic PCA (Minka, 2001) and entropy rate enhanced minimum description length (Li et al., 2007).”
Comparison of multi-subject ICA methods for analysis of fMRI data (2010, methodology): “To make the estimation computationally tractable it is common to first perform a subject-level PCA dimension reduction (Figure 1, Step 4).”
A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia (2010, neutral): “The number of ICs in group ICA was estimated to be C = 19 by using the modified minimum description length criteria [54].”