@article {67291, title = {Detecting and Correcting False Transients in Calcium Imaging}, journal = {bioRxiv}, year = {2018}, month = {11/2018}, abstract = {

Population recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.

}, doi = {10.1101/473470}, url = {https://www.biorxiv.org/content/early/2018/11/19/473470}, author = {Gauthier, Jeff L. and Koay, Sue Ann and Nieh, Edward H. and Tank, David W. and Pillow, Jonathan W. and Charles, Adam S.} } @conference {67293, title = {Stochastic filtering of two-photon imaging using reweighted l1}, booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2017}, month = {03/2017}, publisher = {IEEE}, organization = {IEEE}, address = {New Orleans, LA}, abstract = {
Two-photon (TP) calcium imaging is an important imaging modality in neuroscience, allowing for large-scale recording of neural activity in awake, behaving animals at behavior-relevant timescales. Interpretation of TP data requires the accurate extraction of temporal neural activity traces, which can be accomplished via manual or automated methods. In this work we seek to improve the accuracy of both manual and automated TP microscopy demixing methods by introducing a denoising algorithm based on a statistical model of TP data which includes spatial contiguity, sparse activity and Poisson observations. Our method leverages recent developments in stochastic filtering of structured signals based on Laplacian-scale mixture models (LSMs) to model the neural activity in TP data as a set of spatially correlated sparse variables. We apply our method on TP images taken from the visual cortex of an awake, behaving mouse, and demonstrate improved neural activity demixing over current pre-processing techniques.
}, doi = {10.1109/ICASSP.2017.7952314}, url = {https://ieeexplore.ieee.org/abstract/document/7952314}, author = {Charles, Adam S. and Song, Alexander and Koay, Sue Ann and Tank, David W. and Pillow, Jonathan W.} }