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1. DeepCaImX: An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data

   https://doi.org/10.5281/zenodo.12650420  

   Zhang, Kangning; Tang, Sean; Zhu, Vivian; Barchini, Majd; Yang, Weijian (September 2024, Zenodo)

   {"Abstract":["# DeepCaImX## Introduction#### Two-photon calcium imaging provides large-scale recordings of neuronal activities at cellular resolution. A robust, automated and high-speed pipeline to simultaneously segment the spatial footprints of neurons and extract their temporal activity traces while decontaminating them from background, noise and overlapping neurons is highly desirable to analyze calcium imaging data. In this paper, we demonstrate DeepCaImX, an end-to-end deep learning method based on an iterative shrinkage-thresholding algorithm and a long-short-term-memory neural network to achieve the above goals altogether at a very high speed and without any manually tuned hyper-parameters. DeepCaImX is a multi-task, multi-class and multi-label segmentation method composed of a compressed-sensing-inspired neural network with a recurrent layer and fully connected layers. It represents the first neural network that can simultaneously generate accurate neuronal footprints and extract clean neuronal activity traces from calcium imaging data. We trained the neural network with simulated datasets and benchmarked it against existing state-of-the-art methods with in vivo experimental data. DeepCaImX outperforms existing methods in the quality of segmentation and temporal trace extraction as well as processing speed. DeepCaImX is highly scalable and will benefit the analysis of mesoscale calcium imaging. \n\n## System and Environment Requirements#### 1. Both CPU and GPU are supported to run the code of DeepCaImX. A CUDA compatible GPU is preferred. * In our demo of full-version, we use a GPU of Quadro RTX8000 48GB to accelerate the training speed.* In our demo of mini-version, at least 6 GB momory of GPU/CPU is required.#### 2. Python 3.9 and Tensorflow 2.10.0#### 3. Virtual environment: Anaconda Navigator 2.2.0#### 4. Matlab 2023a\n\n## Demo and installation#### 1 (_Optional_) GPU environment setup. We need a Nvidia parallel computing platform and programming model called _CUDA Toolkit_ and a GPU-accelerated library of primitives for deep neural networks called _CUDA Deep Neural Network library (cuDNN)_ to build up a GPU supported environment for training and testing our model. The link of CUDA installation guide is https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html and the link of cuDNN installation guide is https://docs.nvidia.com/deeplearning/cudnn/installation/overview.html. #### 2 Install Anaconda. Link of installation guide: https://docs.anaconda.com/free/anaconda/install/index.html#### 3 Launch Anaconda prompt and install Python 3.x and Tensorflow 2.9.0 as the virtual environment.#### 4 Open the virtual environment, and then  pip install mat73, opencv-python, python-time and scipy.#### 5 Download the "DeepCaImX_training_demo.ipynb" in folder "Demo (full-version)" for a full version and the simulated dataset via the google drive link. Then, create and put the training dataset in the path "./Training Dataset/". If there is a limitation on your computing resource or a quick test on our code, we highly recommand download the demo from the folder "Mini-version", which only requires around 6.3 GB momory in training. #### 6 Run: Use Anaconda to launch the virtual environment and open "DeepCaImX_training_demo.ipynb" or "DeepCaImX_testing_demo.ipynb". Then, please check and follow the guide of "DeepCaImX_training_demo.ipynb" or or "DeepCaImX_testing_demo.ipynb" for training or testing respectively.#### Note: Every package can be installed in a few minutes.\n\n## Run DeepCaImX#### 1. Mini-version demo* Download all the documents in the folder of "Demo (mini-version)".* Adding training and testing dataset in the sub-folder of "Training Dataset" and "Testing Dataset" separately.* (Optional) Put pretrained model in the the sub-folder of "Pretrained Model"* Using Anaconda Navigator to launch the virtual environment and opening "DeepCaImX_training_demo.ipynb" for training or "DeepCaImX_testing_demo.ipynb" for predicting.\n\n#### 2. Full-version demo* Download all the documents in the folder of "Demo (full-version)".* Adding training and testing dataset in the sub-folder of "Training Dataset" and "Testing Dataset" separately.* (Optional) Put pretrained model in the the sub-folder of "Pretrained Model"* Using Anaconda Navigator to launch the virtual environment and opening "DeepCaImX_training_demo.ipynb" for training or "DeepCaImX_testing_demo.ipynb" for predicting.\n\n## Data Tailor#### A data tailor developed by Matlab is provided to support a basic data tiling processing. In the folder of "Data Tailor", we can find a "tailor.m" script and an example "test.tiff". After running "tailor.m" by matlab, user is able to choose a "tiff" file from a GUI as loading the sample to be tiled. Settings include size of FOV, overlapping area, normalization option, name of output file and output data format. The output files can be found at local folder, which is at the same folder as the "tailor.m".\n\n## Simulated Dataset#### 1. Dataset generator (FISSA Version): The algorithm for generating simulated dataset is based on the paper of FISSA (_Keemink, S.W., Lowe, S.C., Pakan, J.M.P. et al. FISSA: A neuropil decontamination toolbox for calcium imaging signals. Sci Rep 8, 3493 (2018)_) and SimCalc repository (https://github.com/rochefort-lab/SimCalc/). For the code used to generate the simulated data, please download the documents in the folder "Simulated Dataset Generator". #### Training dataset: https://drive.google.com/file/d/1WZkIE_WA7Qw133t2KtqTESDmxMwsEkjJ/view?usp=share_link#### Testing Dataset: https://drive.google.com/file/d/1zsLH8OQ4kTV7LaqQfbPDuMDuWBcHGWcO/view?usp=share_link\n\n#### 2. Dataset generator (NAOMi Version): The algorithm for generating simulated dataset is based on the paper of NAOMi (_Song, A., Gauthier, J. L., Pillow, J. W., Tank, D. W. & Charles, A. S. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. Journal of neuroscience methods 358, 109173 (2021)_). For the code use to generate the simulated data, please go to this link: https://bitbucket.org/adamshch/naomi_sim/src/master/code/## Experimental Dataset#### We used the samples from ABO dataset:https://github.com/AllenInstitute/AllenSDK/wiki/Use-the-Allen-Brain-Observatory-%E2%80%93-Visual-Coding-on-AWS.#### The segmentation ground truth can be found in the folder "Manually Labelled ROIs". #### The segmentation ground truth of depth 175, 275, 375, 550 and 625 um are manually labeled by us. #### The code for creating ground truth of extracted traces can be found in "Prepro_Exp_Sample.ipynb" in the folder "Preprocessing of Experimental Sample"."]} 

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2. FARCI: Fast and Robust Connectome Inference

   https://doi.org/10.3390/brainsci11121556  

   Meamardoost, Saber; Bhattacharya, Mahasweta; Hwang, Eun Jung; Komiyama, Takaki; Mewes, Claudia; Wang, Linbing; Zhang, Ying; Gunawan, Rudiyanto (December 2021, Brain Sciences)

   The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling. 

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3. Deep inference of latent dynamics with spatio-temporal super resolution using selective backpropagation through time

   Zhu, F; Sedler, AR; Grier, HA; Ahad, N; Davenport, MA; Kaufman, MT; Giovannucci, A; Pandarinath, C (January 2021, Advances in Neural Information Processing Systems (NeurIPS))

   Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data. 

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4. Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization

   https://doi.org/10.3389/fnins.2021.797421  

   Bao, Yijun; Redington, Emily; Agarwal, Agnim; Gong, Yiyang (January 2022, Frontiers in Neuroscience)

   Fluorescence microscopy and genetically encoded calcium indicators help understand brain function by recording large-scalein vivovideos in assorted animal models. Extracting the fluorescent transients that represent active periods of individual neurons is a key step when analyzing imaging videos. Non-specific calcium sources and background adjacent to segmented neurons contaminate the neurons’ temporal traces with false transients. We developed and characterized a novel method, temporal unmixing of calcium traces (TUnCaT), to quickly and accurately unmix the calcium signals of neighboring neurons and background. Our algorithm used background subtraction to remove the false transients caused by background fluctuations, and then applied targeted non-negative matrix factorization to remove the false transients caused by neighboring calcium sources. TUnCaT was more accurate than existing algorithms when processing multiple experimental and simulated datasets. TUnCaT’s speed was faster than or comparable to existing algorithms. 

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5. Nonparanormal graph quilting with applications to calcium imaging

   https://doi.org/10.1002/sta4.623  

   Chang, Andersen; Zheng, Lili; Dasarathy, Gautam; Allen, Genevera I. (September 2023, Stat)

   Abstract Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functional neuronal connectivity from two‐photon calcium imaging data. However, in the context of calcium imaging, technological limitations only allow for partially overlapping layers of neurons in a brain region of interest to be jointly recorded. In this case, graph estimation for the full data requires inference for edge selection when many pairs of neurons have no simultaneous observations. This leads to the graph quilting problem, which seeks to estimate a graph in the presence of block‐missingness in the empirical covariance matrix. Solutions for the graph quilting problem have previously been studied for Gaussian graphical models; however, neural activity data from calcium imaging are often non‐Gaussian, thereby requiring a more flexible modelling approach. Thus, in our work, we study two approaches for nonparanormal graph quilting based on the Gaussian copula graphical model, namely, a maximum likelihood procedure and a low rank‐based framework. We provide theoretical guarantees on edge recovery for the former approach under similar conditions to those previously developed for the Gaussian setting, and we investigate the empirical performance of both methods using simulations as well as real data calcium imaging data. Our approaches yield more scientifically meaningful functional connectivity estimates compared to existing Gaussian graph quilting methods for this calcium imaging data set. 

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