Compressed Online Dictionary Learning for Fast fMRI Decomposition
Arthur Mensch (PARIETAL), Ga\"el Varoquaux (PARIETAL), Bertrand, Thirion (PARIETAL)

TL;DR
This paper introduces a fast, scalable method for resting-state fMRI data decomposition by reducing temporal dimensions prior to dictionary learning, maintaining reliability while improving computational efficiency.
Contribution
It proposes a novel time-reduction approach for dictionary learning in fMRI analysis, enhancing scalability without sacrificing result quality.
Findings
Time-reduction maintains decomposition reliability.
Significant improvement in computational scalability.
Method effective on large datasets.
Abstract
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.
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Taxonomy
TopicsBlind Source Separation Techniques · Fractal and DNA sequence analysis · Domain Adaptation and Few-Shot Learning
