ArticleImaging neuroscience (Cambridge, Mass.)2025
The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. 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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19 citing papers in PubMed.
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- 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
- Discourse Context and Co-Speech Gestures Jointly Shape Hierarchical Prediction During the Processing of a Multimodal Narrative.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Rapid cortical mapping with cross-participant encoding models.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Left-Right Asymmetry in Predicting Brain Activity From LLMs' Representations Emerges With Their Formal Linguistic Competence.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Visual Semantic Encoding and Identification of Naturalistic Movies via High-Density Diffuse Optical Tomography.bioRxiv : the preprint server for biology · 2025Article
- Reconstructing music perception from brain activity using a prior guided diffusion model.Scientific reports · 2025Article
- Individual differences shape conceptual representation in the brain.bioRxiv : the preprint server for biology · 2025Article
- Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations.bioRxiv : the preprint server for biology · 2025Article
- The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.Scientific data · 2025Article
- Cortical language areas are coupled via a soft hierarchy of model-based linguistic features.bioRxiv : the preprint server for biology · 2025Article
- The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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3 authors.
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Abstract
The Voxelwise Encoding Model framework (VEM) is a powerful approach for functional brain mapping. In the VEM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also encoded in the brain. In VEM, a separate encoding model is fitted on each spatial sample (i.e., each voxel). VEM has many benefits compared to other methods for analyzing and modeling neuroimaging data. Most importantly, VEM can use large numbers of features simultaneously, which enables the analysis of complex naturalistic stimuli and tasks. Therefore, VEM can produce high-dimensional functional maps that reflect the selectivity of each voxel to large numbers of features. Moreover, because model performance is estimated on a separate test dataset not used during fitting, VEM minimizes overfitting and inflated Type I error confounds that plague other approaches, and the results of VEM generalize to new subjects and new stimuli. Despite these benefits, VEM is still not widely used in neuroimaging, partly because no tutorials on this method are available currently. To demystify the VEM framework and ease its dissemination, this paper presents a series of hands-on tutorials accessible to novice practitioners. The VEM tutorials are based on free open-source tools and public datasets, and reproduce the analysis presented in previously published work.
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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.