Fast PCA for processing calcium-imaging data from the brain of Drosophila melanogaster.

Background: The calcium-imaging technique allows us to record movies of brain activity in the antennal lobe of the fruitfly Drosophila melanogaster, a brain compartment dedicated to information about odors. Signal processing, e.g. with source separation techniques, can be slow on the large movie datasets.

Method: We have developed an approximate Principal Component Analysis (PCA) for fast dimensionality reduction. The method samples relevant pixels from the movies, such that PCA can be performed on a smaller matrix. Utilising a priori knowledge about the nature of the data, we minimise the risk of missing important pixels.

Results: Our method allows for fast approximate computation of PCA with adaptive resolution and running time. Utilising a priori knowledge about the data enables us to concentrate more biological signals in a small pixel sample than a general sampling method based on vector norms.

Conclusions: Fast dimensionality reduction with approximate PCA removes a computational bottleneck and leads to running time improvements for subsequent algorithms. Once in PCA space, we can efficiently perform source separation, e.g to detect biological signals in the movies or to remove artifacts.