ArticleNeuroImage2017
Large-scale sparse functional networks from resting state fMRI.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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.
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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.
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Who cites it
49 citing papers in PubMed.
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.Human brain mapping · 2026Article
- Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts.IEEE transactions on medical imaging · 2026Article
- Group Sparse Representation Enhances Brain Network Classification of Major Depressive Disorder in Two Chinese Cohorts.Alpha psychiatry · 2026Article
- Personalized functional network connectivity abnormalities in chronic insomnia disorder.Psychoradiology · 2026Article
- Taming dimensionality in big neuroimaging data: Efficient orthonormal projective NMF via stochastic learning and data compression.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Topologically Optimized Intrinsic Brain Networks.Human brain mapping · 2025Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.bioRxiv : the preprint server for biology · 2025Article
- Precision Neuromodulation with Real-Time Brain Decoding for Working Memory Enhancement.bioRxiv : the preprint server for biology · 2025Article
- Reproducible sex differences in personalised functional network topography in youth.The British journal of psychiatry : the journal of mental science · 2025Article
- Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.bioRxiv : the preprint server for biology · 2025Article
- Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Deep learning-based embedding of functional connectivity profiles for precision functional mapping.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children.BMC medicine · 2024Article
- Reproducible Sex Differences in Personalized Functional Network Topography in Youth.bioRxiv : the preprint server for biology · 2024Article
- A precision functional atlas of personalized network topography and probabilities.Nature neuroscience · 2024Article
- Towards personalized precision functional mapping in infancy.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
- A general exposome factor explains individual differences in functional brain network topography and cognition in youth.Developmental cognitive neuroscience · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Delineation of large-scale functional networks (FNs) from resting state functional MRI data has become a standard tool to explore the functional brain organization in neuroscience. However, existing methods sacrifice subject specific variation in order to maintain the across-subject correspondence necessary for group-level analyses. In order to obtain subject specific FNs that are comparable across subjects, existing brain decomposition techniques typically adopt heuristic strategies or assume a specific statistical distribution for the FNs across subjects, and therefore might yield biased results. Here we present a novel data-driven method for detecting subject specific FNs while establishing group level correspondence. Our method simultaneously computes subject specific FNs for a group of subjects regularized by group sparsity, to generate subject specific FNs that are spatially sparse and share common spatial patterns across subjects. Our method is built upon non-negative matrix decomposition techniques, enhanced by a data locality regularization term that makes the decomposition robust to imaging noise and improves spatial smoothness and functional coherences of the subject specific FNs. Our method also adopts automatic relevance determination techniques to eliminate redundant FNs in order to generate a compact set of informative sparse FNs. We have validated our method based on simulated, task fMRI, and resting state fMRI datasets. The experimental results have demonstrated our method could obtain subject specific, sparse, non-negative FNs with improved functional coherence, providing enhanced ability for characterizing the functional brain of individual subjects.
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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.