ArticleGigaScience2022
How to remove or control confounds in predictive models, with applications to brain biomarkers.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The State of Motion: Demographic and Representational Consequences of Motion-Based Exclusion in FMRI.bioRxiv : the preprint server for biology · 2026Article
- Linking the exposome to the brain-behaviour phenotype.Nature reviews. Neuroscience · 2026Review
- Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder.Molecular psychiatry · 2026Article
- Predicting clinical outcomes in Helicobacter pylori-positive patients using supervised learning through the integration of demographic and genomic features.BMC gastroenterology · 2026Article
- Subject fingerprinting and task classification rely on distinct functional connectivity features.Brain structure & function · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Patterns of brain-wide associations reflect socioeconomics.bioRxiv : the preprint server for biology · 2025Article
- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Exploring electroencephalographic chronic pain biomarkers: a mega-analysis.EBioMedicine · 2025Article
- Biological markers and psychosocial factors predict chronic pain conditions.Nature human behaviour · 2025Article
- Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep feature batch correction using ComBat for machine learning applications in computational pathology.Journal of pathology informatics · 2024Article
- Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness.Science advances · 2024Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Power and reproducibility in the external validation of brain-phenotype predictions.Nature human behaviour · 2024Article
- Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundWith increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers.
resultsHere we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds.
conclusionsCross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.
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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.