Compressed Online Dictionary Learning for Fast fMRI Decomposition
This work addresses computational bottlenecks in fMRI analysis for researchers, though it is incremental as it builds on existing dictionary learning methods.
The authors tackled the problem of fast spatial decomposition of large resting-state fMRI datasets by reducing the temporal dimension before applying dictionary learning, showing that this approach produces results as reliable as non-reduced methods while significantly improving computational scalability.
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.