- Computational Tutorials
[recording cut short due to technical issues]
Pengcheng Zhou, Columbia University
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.
We developed a new matrix factorization approach, named CNMF-E, to accurately separate the background and then simultaneously demix and denoise the neuronal signals of interest (https://arxiv.org/abs/1605.07266). The method has been thoroughly compared against widely- used independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data, 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.
In this tutorial, we will briefly review existing methods in extracting neurons from raw calcium imaging video data, and then focus on our method CNMF-E. We will give a thorough tutorial on analyzing microendoscopic data using the MATLAB version of CNMF-E (https://github.com/zhoupc/CNMF_E). The method is specialized for, but not limited to, 1-photon microendoscopic data. In practice, we have also successfully applied the method to 2-photon imaging data. You are welcome to bring your own datasets and we will try to make CNMF-E work on them.
After the tutorial, slides and resources will be posted on the computational tutorials stellar page.
slides, references, and exercises: https://stellar.mit.edu/S/project/bcs-comp-tut/materials.html