Data from: Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

In vivo calcium imaging through microendoscopic lenses enables imaging of
previously inaccessible neuronal populations deep within the brains of
freely moving animals. However, it is computationally challenging to
extract single-neuronal activity from microendoscopic data, because of the
very large background fluctuations and high spatial overlaps intrinsic to
this recording modality. Here, we describe a new constrained matrix
factorization approach to accurately separate the background and then
demix and denoise the neuronal signals of interest. We compared the
proposed method against previous independent components analysis and
constrained nonnegative matrix factorization approaches. On both simulated
and experimental data recorded from mice, our method substantially
improved the quality of extracted cellular signals and detected more
well-isolated neural signals, especially in noisy data regimes. These
advances can in turn significantly enhance the statistical power of
downstream analyses, and ultimately improve scientific conclusions derived
from microendoscopic data.