ArticleNature communications2024
Data leakage inflates prediction performance in connectome-based machine learning models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 2 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Predicting big five personality traits from resting-state EEG connectivity: a connectome predictive modeling study.Brain structure & function · 2026Article
- Duplicate-Aware Internal Validation of Machine-Learning Models for Classifying a Tanita BIA-Derived High-Adiposity Phenotype Using Simple Anthropometric Predictors.Journal of clinical medicine · 2026Article
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- Tumor-educated platelets in cancer diagnostics and prognostics: A critical appraisal and roadmap for clinical translation.International journal of cancer · 2026Review
- Nine Coupled Irrigation-Agronomic Treatments for Water-Saving Rice Production on Albic Soil: An Interpretable Machine-Learning Diagnosis.Plants (Basel, Switzerland) · 2026Article
- Linking the exposome to the brain-behaviour phenotype.Nature reviews. Neuroscience · 2026Review
- Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.Nature communications · 2026Article
- 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset.Journal of imaging · 2026Article
- An integrated machine learning framework for TCM five-flavor classification based on E-tongue profiling and SHAP analysis.Chinese medicine · 2026Article
- Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.Diabetes, obesity & metabolism · 2026Review
- Brain maintenance biomarkers from structural and functional interactions in aging and neurodegeneration.Nature communications · 2026Article
21 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage-involving feature selection, covariate correction, and dependence between subjects-on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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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.