A new approach for brain decoding, called inverse inference (or brain-reading), introduced initially in [Dehaene 98, Cox 03], has become recently popular. This method relies on statistical learning tools, and more precisely on pattern recognition approaches. The main idea is to consider the fMRI analysis as a pattern recognition problem, i.e. using a pattern of voxels to predict a behavioral, perceptual or cognitive variable. In this approach, the accuracy of the prediction can be used to validate (or invalidate) that the pattern of voxels used in the predictive model is implied in the neural coding. In short, reverse inference is an approach for decoding neural activity.
Large-scale decoding for a reverse inference and knowledge accumulation
- Extracting universal representations of cognition across brain-imaging studies
- Charting the brain: Joint modeling of anatomical and functional brain features
Developing better decoders
- Assessing and tuning brain decoders
- Ensembling multivariate estimators improves multivariate brain maping
- TV (Total-Variation) and TV-ℓ1 regularization
- Sparse variation regularization
- Structured sparsity for brain mapping
Encoding models
- Data-driven HRF estimation for encoding and decoding models
- Learning to rank medical images
- Modeling the Visual cortex
- Modeling brain activity during movie watching
