ArticleMeta-radiology2023
A comprehensive survey of complex brain network representation.
Article in Meta-radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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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Who cites it
9 citing papers in PubMed.
- Longitudinal co-activation pattern analysis of menstrual cycle-related brain dynamics in primary dysmenorrhea.iScience · 2026Article
- Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney disease.Frontiers in psychiatry · 2026Article
- Interpretable multimodal learning for integrating neuroimaging and genetic data in Alzheimer's disease.Frontiers in radiology · 2026Article
- BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Investigating the relationships of structural and functional neural networks of primary visual cortex with engineered AAVs and chemogenetic-fMRI techniques.Theranostics · 2025Article
- Active elements, effects, and working mechanisms of creative arts therapies in forensic psychiatric care: a realist review.Frontiers in psychiatry · 2025Review
- TGNet: tensor-based graph convolutional networks for multimodal brain network analysis.BioData mining · 2024Article
- Article
- Brain network functional connectivity changes in long illness duration chronic schizophrenia.Frontiers in psychiatry · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain networks and relate these features to different clinical measures or demographical variables. With the enormous successes in deep learning techniques, graph learning methods have played significant roles in brain network analysis. In this survey, we first provide a brief overview of neuroimaging-derived brain networks. Then, we focus on presenting a comprehensive overview of both traditional methods and state-of-the-art deep-learning methods for brain network mining. Major models, and objectives of these methods are reviewed within this paper. Finally, we discuss several promising research directions in this field.
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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.