# Compressed Online Dictionary Learning for Fast fMRI Decomposition

arXiv: https://arxiv.org/abs/1602.02701 · HTML: https://mlsanity.com/paper/1602.02701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models](https://mlsanity.com/paper/2502.20408/) (2025)
- [Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning](https://mlsanity.com/paper/2106.09000/) (2021)

## Abstract (arXiv)

We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability.