ArticlePloS one2022
Comparing the reliability of different ICA algorithms for fMRI analysis.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Dynamic functional connectivity in borderline personality disorder: associations with trauma, emotion regulation and symptom severity.European archives of psychiatry and clinical neuroscience · 2026Article
- Functional connectivity changes in obsessive-compulsive disorder following exposure and response prevention.Translational psychiatry · 2026Article
- Evaluating seed-based and independent component analysis approaches for resting-state functional connectivity mapping with fNIRS.Neurophotonics · 2026Article
- DMN function changes on resting state fMRI in perimenopausal women.Open medicine (Warsaw, Poland) · 2026Article
- Group Information Guided Smooth Independent Component Analysis Method for Multi-Subject fMRI Data Analysis.IEEE journal of biomedical and health informatics · 2026Article
- Source-based morphometry analyses yield associations between large-scale structural brain networks and psychopathic traits in incarcerated adolescents.Frontiers in psychology · 2026Article
- Robustness, spatial detail, and pitfalls of fixed ICA dimensionality in resting-state fMRI networks at 1.5, 3, and 7 T.Frontiers in neuroscience · 2025Article
- Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function.Drug and alcohol dependence · 2024Article
- SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks.Neuroscience bulletin · 2024Article
- The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study.Addiction biology · 2024Article
- A fronto-insular network underlies individual variations in anger expression and control.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter.Frontiers in neurology · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.
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