ArticleNeuroImage2015
Comparison of PCA approaches for very large group ICA.
Article in NeuroImage, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.Human brain mapping · 2025Article
- Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale.World journal of clinical cases · 2023Article
- An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.Human brain mapping · 2022Article
- Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.Frontiers in neuroscience · 2020Article
- Functional Neuroimaging in the New Era of Big Data.Genomics, proteomics & bioinformatics · 2019Review
- Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings.NeuroImage · 2019Article
- ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide.NeuroImage · 2017Review
- Article
- Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.NeuroImage · 2016Article
- Memory Efficient PCA Methods for Large Group ICA.Frontiers in neuroscience · 2016Article
- Studies on Chromatographic Fingerprint and Fingerprinting Profile-Efficacy Relationship of Saxifraga stolonifera Meerb.Molecules (Basel, Switzerland) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Large data sets are becoming more common in fMRI and, with the advent of faster pulse sequences, memory efficient strategies for data reduction via principal component analysis (PCA) turn out to be extremely useful, especially for widely used approaches like group independent component analysis (ICA). In this commentary, we discuss results and limitations from a recent paper on the topic and attempt to provide a more complete perspective on available approaches as well as discussing various issues to consider related to PCA for very large group ICA. We also provide an analysis of computation time, memory use, and number of dataloads for a variety of approaches under multiple scenarios of small and extremely large data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.