# Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

arXiv: https://arxiv.org/abs/2006.06635 · HTML: https://mlsanity.com/paper/2006.06635/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style](https://mlsanity.com/paper/2106.04619/) (2021)
- [Aligning individual brains with Fused Unbalanced Gromov-Wasserstein](https://mlsanity.com/paper/2206.09398/) (2022)
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- [Aligning brain functions boosts the decoding of visual semantics in novel subjects](https://mlsanity.com/paper/2312.06467/) (2023)
- [Multi-View Independent Component Analysis with Shared and Individual Sources](https://mlsanity.com/paper/2210.02083/) (2022)
- [Deep Recurrent Encoder: A scalable end-to-end network to model brain signals](https://mlsanity.com/paper/2103.02339/) (2021)
- [Shared Independent Component Analysis for Multi-Subject Neuroimaging](https://mlsanity.com/paper/2110.13502/) (2021)
- [MultiView Independent Component Analysis with Delays](https://mlsanity.com/paper/2312.00484/) (2023)
- [Adaptive Multi-View ICA: Estimation of noise levels for optimal inference](https://mlsanity.com/paper/2102.10964/) (2021)
- [Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks](https://mlsanity.com/paper/2410.19774/) (2024)
- [Spectral Differential Network Analysis for High-Dimensional Time Series](https://mlsanity.com/paper/2412.07905/) (2024)
- [MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations](https://mlsanity.com/paper/2501.07426/) (2025)
- [Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data](https://mlsanity.com/paper/2505.00196/) (2025)
- [Good-Enough LLM Obfuscation (GELO)](https://mlsanity.com/paper/2603.05035/) (2026)

## Abstract (arXiv)

Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. However, the aggregation of data coming from multiple subjects is challenging, since it requires accounting for large variability in anatomy, functional topography and stimulus response across individuals. Data modeling is especially hard for ecologically relevant conditions such as movie watching, where the experimental setup does not imply well-defined cognitive operations. We propose a novel MultiView Independent Component Analysis (ICA) model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. Contrary to most group-ICA procedures, the likelihood of the model is available in closed form. We develop an alternate quasi-Newton method for maximizing the likelihood, which is robust and converges quickly. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects. Moreover, the sources recovered by our model exhibit lower between-session variability than other methods.On magnetoencephalography (MEG) data, our method yields more accurate source localization on phantom data. Applied on 200 subjects from the Cam-CAN dataset it reveals a clear sequence of evoked activity in sensor and source space. The code is freely available at https://github.com/hugorichard/multiviewica.