# Extracting representations of cognition across neuroimaging studies improves brain decoding

arXiv: https://arxiv.org/abs/1809.06035 · HTML: https://mlsanity.com/paper/1809.06035/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [A foundation model of vision, audition, and language for in-silico neuroscience](https://mlsanity.com/paper/2605.04326/) (2026)
- [Challenges for cognitive decoding using deep learning methods](https://mlsanity.com/paper/2108.06896/) (2021)
- [Evaluating deep transfer learning for whole-brain cognitive decoding](https://mlsanity.com/paper/2111.01562/) (2021)
- [Comparing interpretation methods in mental state decoding analyses with deep learning models](https://mlsanity.com/paper/2205.15581/) (2022)
- [On the benefits of self-taught learning for brain decoding](https://mlsanity.com/paper/2209.10099/) (2022)
- [Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification](https://mlsanity.com/paper/2108.10427/) (2021)
- [Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding](https://mlsanity.com/paper/2203.04455/) (2022)

## Abstract (arXiv)

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.