ArticleHuman brain mapping2020
Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Acupuncture modulates emotional network resting-state functional connectivity in patients with insomnia disorder: a randomized controlled trial and fMRI study.BMC complementary medicine and therapies · 2024Trial
- Representing Brain-Behavior Associations by Retaining High-Motion Minoritized Youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Predicting Real-Life Cognitive Scores From Functional Connectivity.Human brain mapping · 2025Article
- Connectome-based predictive modeling of grip strength: a marker of physical frailty.Frontiers in neuroscience · 2025Article
- Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations.bioRxiv : the preprint server for biology · 2024Article
- A radiomics-based brain network in T1 images: construction, attributes, and applications.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging.IEEE signal processing magazine · 2022Article
- Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study.Frontiers in neuroergonomics · 2022Article
- Resample aggregating improves the generalizability of connectome predictive modeling.NeuroImage · 2021Article
- Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.Human brain mapping · 2020Article
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3 authors.
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Abstract
Resting-state functional connectivity (RSFC) records enormous functional interaction information between any pair of brain nodes, which enriches the individual-phenotypic prediction. To reduce high-dimensional features, correlation analysis is a common way for feature selection. However, resting state fMRI signal exhibits typically low signal-to-noise ratio and the correlation analysis is sensitive to outliers and data distribution, which may bring unstable features to prediction. To alleviate this problem, a bootstrapping-based feature selection framework was proposed and applied to connectome-based predictive modeling, support vector regression, least absolute shrinkage and selection operator, and Ridge regression to predict a series of cognitive traits based on Human Connectome Project data. To systematically investigate the influences of different parameter settings on the bootstrapping-based framework, 216 parameter combinations were evaluated and the best performance among them was identified as the final prediction result for each cognitive trait. By using the bootstrapping methods, the best prediction performances outperformed the baseline method in all four prediction models. Furthermore, the proposed framework could effectively reduce the feature dimension by retaining the more stable features. The results demonstrate that the proposed framework is an easy-to-use and effective method to improve RSFC prediction of cognitive traits and is highly recommended in future RSFC-prediction studies.
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