FastSRM: A fast, memory efficient and identifiable implementation of the shared response model

Original Research Manuscript

FastSRM: A fast, memory efficient and identifiable implementation of the shared response model

Functional MRIUnivariate/Multivariate ModelingMachine and Deep Learning

Abstract

The shared response model (SRM) provides a simple but effective framework to analyze fMRI data of subjects exposed to naturalistic stimuli. However when the number of subjects or runs is large, fitting the model requires a large amount of memory and computational power, which limits its use in practice.

Furthermore, SRM is not identifiable, which makes the shared response difficult to interpret. In this work, we implement an identifiable version of SRM and show on real data that it improves the stability of the recovered shared response.

We then introduce FastSRM, which relies on a dimension reduction step that we prove to yield the same solution as the original algorithm. We show experimentally using synthetic and real fMRI data that FastSRM is considerably faster and more memory efficient than current implementations.

The experiments performed in this article are fully reproducible: our code available at https://github.com/hugorichard/FastSRM allows you to download the data, run the experiments and plot the figures.

Received