@inproceedings{
geenjaar2023uncovering,
title={Uncovering the latent dynamics of whole-brain f{MRI} tasks with a sequential variational autoencoder},
author={Eloy Geenjaar and Donghyun Kim and Riyasat Ohib and Marlena Duda and Amrit Kashyap and Sergey Plis and Vince Calhoun},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=c4p3ng0SCt}
}
The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduction techniques fail to capture these dynamics, producing latent timeseries that inadequately relate to behavioral tasks. To address this issue, we introduce a novel approach to learning low-dimensional approximations of neural dynamics using a sequential variational autoencoder (SVAE) that learns the latent dynamical system. Importantly, our method finds smooth dynamics that can predict cognitive processes with accuracy higher than classical methods, with improved spatial localization to task-relevant brain regions, and we find fixed points for the dynamics that are stable across random initialization of the model.
Uncovering the latent dynamics of whole-brain fMRI tasks with a sequential variational autoencoder
Eloy Geenjaar Donghyun Kim Riyasat Ohib Marlena Duda Amrit Kashyap Sergey M. Plis Vince Calhoun