ArticleFrontiers in neuroscience2017
Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Linking cortical morphology and neurophysiological dynamics in Parkinson's disease.Scientific reports · 2026Article
- Linking Cortical Morphology and Neurophysiological Dynamics in Parkinson's Disease.Research square · 2025Article
- Multivariate Associations Between Neurophysiological Activity and Sensory Motor Cortical Morphometry in Parkinson's Disease.bioRxiv : the preprint server for biology · 2025Article
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Groupwise structural sparsity for discriminative voxels identification.Frontiers in neuroscience · 2023Article
- Article
- Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques.Scientific reports · 2021Article
- Individual differences in local functional brain connectivity affect TMS effects on behavior.Scientific reports · 2020Article
- Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection.Scientific reports · 2020Article
- Toward a unified framework for interpreting machine-learning models in neuroimaging.Nature protocols · 2020Article
- Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data.Neuroinformatics · 2020Article
- Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.Biological psychiatry · 2020Article
- Towards Algorithmic Analytics for Large-scale Datasets.Nature machine intelligence · 2019Article
- Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm.Computational intelligence and neuroscience · 2018Article
- Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.Neuroinformatics · 2018Article
- The Cognitive Information Effect of Televised News.Frontiers in psychology · 2017Review
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3 authors.
Funding
Abstract
Structured sparse methods have received significant attention in neuroimaging. These methods allow the incorporation of domain knowledge through additional spatial and temporal constraints in the predictive model and carry the promise of being more interpretable than non-structured sparse methods, such as LASSO or Elastic Net methods. However, although sparsity has often been advocated as leading to more interpretable models it can also lead to unstable models under subsampling or slight changes of the experimental conditions. In the present work we investigate the impact of using stability/reproducibility as an additional model selection criterion on several different sparse (and structured sparse) methods that have been recently applied for fMRI brain decoding. We compare three different model selection criteria: (i) classification accuracy alone; (ii) classification accuracy and overlap between the solutions; (iii) classification accuracy and correlation between the solutions. The methods we consider include LASSO, Elastic Net, Total Variation, sparse Total Variation, Laplacian and Graph Laplacian Elastic Net (GraphNET). Our results show that explicitly accounting for stability/reproducibility during the model optimization can mitigate some of the instability inherent in sparse methods. In particular, using accuracy and overlap between the solutions as a joint optimization criterion can lead to solutions that are more similar in terms of accuracy, sparsity levels and coefficient maps even when different sparsity methods are considered.
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