ArticleNeuroImage2022
Feature-space selection with banded ridge regression.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- During natural vision, semantic novelty modulates fixation-related processing in primate cortex.bioRxiv : the preprint server for biology · 2026Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
- Pregistered movie-fMRI analyses reveal altered visual feature encoding in autism in pSTS.bioRxiv : the preprint server for biology · 2026Article
- Visual semantic tuning across the cortex shifts between tasks.bioRxiv : the preprint server for biology · 2026Article
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- Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms.bioRxiv : the preprint server for biology · 2026Article
- Neural encoding of auditory processing in schizophrenia.Frontiers in neuroscience · 2026Article
- A map of the cortical functional network mediating naturalistic navigation.bioRxiv : the preprint server for biology · 2025Article
- Neural encoding of biomechanically (im)possible human movements in occipitotemporal cortex.PLoS computational biology · 2025Article
- Text-to-music generation models capture musical semantic representations in the human brain.Nature communications · 2025Article
- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
- Conversational content is organized across multiple timescales in the brain.Nature human behaviour · 2025Article
- Data-driven frameworks to robustly predict solubility parameter of diverse polymers.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.
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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.