ArticleCerebral cortex (New York, N.Y. : 1991)2023
Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- When Brain Models Aren't Universal: Benchmarking of Ethnic Bias in MRI-Based Cognitive Prediction Across Modalities.bioRxiv : the preprint server for biology · 2025Article
- A Replicable and Generalizable Neuroimaging-Based Indicator of Pain Sensitivity Across Individuals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Healthy Aging Metabolomic and Proteomic Signatures Across Multiple Physiological Compartments.Aging cell · 2025Article
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- Enhancing task fMRI individual difference research with neural signatures.medRxiv : the preprint server for health sciences · 2025Article
- Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework.NeuroImage · 2024Article
- Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals.eLife · 2024Article
- Stimulus shapes strategy: Effects of stimulus characteristics and individual differences in academic achievement on the neural mechanisms engaged during the N-back task.Developmental cognitive neuroscience · 2024Article
- Neurodevelopmental Subtypes of Functional Brain Organization in the ABCD Study Using a Rigorous Analytic Framework.bioRxiv : the preprint server for biology · 2024Article
- Inferring neurocognition using artificial intelligence on brain MRIs.Frontiers in neuroimaging · 2024Review
- The challenges and prospects of brain-based prediction of behaviour.Nature human behaviour · 2023Review
- Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities.NeuroImage · 2023Review
- Article
- From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks.Computer methods and programs in biomedicine · 2023Article
- Predicting executive functioning from functional brain connectivity: network specificity and age effects.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
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5 authors.
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Abstract
Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
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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.