ArticleScientific reports2018
An ANOVA approach for statistical comparisons of brain networks.
Article in Scientific reports, 2018. 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.
- Two-year impact of COVID-19: Longitudinal MRI brain changes and neuropsychiatric trajectories.Psychiatry and clinical neurosciences · 2025Article
- Assessing brain-muscle networks during motor imagery to detect covert command-following.BMC medicine · 2025Article
- XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.Scientific reports · 2024Article
- Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of Cognitive Decline in Parkinson's Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems.Diagnostics (Basel, Switzerland) · 2023Article
- netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA.Briefings in bioinformatics · 2023Article
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
- Curve Fitting for Damage Evolution through Regression Analysis for the Kachanov-Rabotnov Model to the Norton-Bailey Creep Law of SS-316 Material.Materials (Basel, Switzerland) · 2021Article
- Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison.Brain sciences · 2021Article
- Construct social-behavioral association network to study management impact on waterbirds community ecology using digital video recording cameras.Ecology and evolution · 2021Article
- A Path-Based Distribution Measure for Network Comparison.Entropy (Basel, Switzerland) · 2020Article
- CHARACTERIZING FREQUENCY-SELECTIVE NETWORK VULNERABILITY FOR ALZHEIMER'S DISEASE BY IDENTIFYING CRITICAL HARMONIC PATTERNS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
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Authors and funding
2 authors.
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Abstract
The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.
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