# A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

arXiv: https://arxiv.org/abs/1608.04846 · HTML: https://mlsanity.com/paper/1608.04846/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding](https://mlsanity.com/paper/2402.08994/) (2024)
- [Learning shared neural manifolds from multi-subject FMRI data](https://mlsanity.com/paper/2201.00622/) (2021)
- [Shared Independent Component Analysis for Multi-Subject Neuroimaging](https://mlsanity.com/paper/2110.13502/) (2021)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)

## Abstract (arXiv)

Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality. We first do this directly by combining a recent factor method known as a shared response model with searchlight analysis. Then we design a multi-view convolutional autoencoder for the same task. Both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain. We also report a system design to handle the computational challenge of training the convolutional autoencoder.