ArticleFrontiers in neuroscience2020
Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.
Article in Frontiers in neuroscience, 2020. 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.
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- Chemical imaging for biological systems: techniques, AI-driven processing, and applications.Journal of materials chemistry. B · 2025Review
- SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks.Neuroscience bulletin · 2024Article
- CW_ICA: an efficient dimensionality determination method for independent component analysis.Scientific reports · 2024Article
- Controversies and progress on standardization of large-scale brain network nomenclature.Network neuroscience (Cambridge, Mass.) · 2023Article
- Psychopathic traits and altered resting-state functional connectivity in incarcerated adolescent girls.Frontiers in neuroimaging · 2023Article
- Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.NeuroImage · 2022Article
- Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping.NeuroImage · 2022Article
- Aberrant resting-state functional connectivity in incarcerated women with elevated psychopathic traits.Frontiers in neuroimaging · 2022Article
- Decreased Connectivity in Precuneus of the Ventral Attentional Network in First-Episode, Treatment-Naïve Patients With Major Depressive Disorder: A Network Homogeneity and Independent Component Analysis.Frontiers in psychiatry · 2022Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks.Frontiers in neuroscience · 2021Article
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Authors and funding
6 authors.
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Abstract
In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Using simulations and
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