ArticleNature protocols2020
Toward a unified framework for interpreting machine-learning models in neuroimaging.
Article in Nature protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 6 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
90 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- Demographic reporting across a decade of neuroimaging: a systematic review.Brain imaging and behavior · 2022Pooled it
- Explainable AI: A review of applications to neuroimaging data.Frontiers in neuroscience · 2022Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- The effect of intranasal oxytocin on the neuropsychological substrates of intergroup categorization.Brain imaging and behavior · 2026Trial
- Pallidal and subthalamic stimulations modulate inter-hemispheric interaction and asymmetry in Parkinson's disease.Molecular psychiatry · 2026Article
- Model-based semantic distance reveals adaptive coordination of distinct cognitive systems in flexible knowledge retrieval.Nature communications · 2026Article
- A neural signature of sleep deprivation in the human brain.Nature communications · 2026Article
- Variations of global brain asymmetry are associated with aging and related diseases.Science advances · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- The functional neurobiology of dispositions towards negative emotions.Nature communications · 2026Article
- Smart Nano-Antibiotics: AI-Guided Stimuli-Responsive Nanoplatforms for Precision Antimicrobial Therapy.Antibiotics (Basel, Switzerland) · 2026Review
- Neural Representations and Functional Connections Underlie Subjective Positive Affect.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Latent neural architecture organising shared aesthetic evaluations of visual artworks.Nature communications · 2026Article
- Machine-Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study.The Clinical neuropsychologist · 2026Article
- Common and distinct neurofunctional signatures of dynamic naturalistic emotion regulation strategies.Nature communications · 2026Article
- Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model.Molecular psychiatry · 2026Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories.
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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.