Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning
Description
Dataset used in Park et al. [1]. [1] Park, S., Lipton, M., & Dadarlat, M. (2023). Decoding multi-limb movements from low temporal resolution calcium imaging using deep learning. bioRxiv, 2023-11. https://www.biorxiv.org/content/10.1101/2023.11.30.569459v1 Code used for Park et al. [1] https://github.com/Dadarlat-Lab/decoding-2ptrace https://github.com/seungbin201803/decoding-2ptrace Neural decoding is the process of predicting behavior from brain signals, which is crucial for gaining insights into the functions of various brain regions and for advancing technology such as brain-computer interface to aid individuals suffering from neurological injuries and diseases. Considering that behavior results from interactions of a large population of neurons in circuits across various brain regions, recording and analyzing extensive neuronal populations is imperative for enhancing the field of neural decoding. Two-photon calcium imaging is a promising technique for recording the activity of thousands of neurons in a single-cell resolution. Dadarlatlab at Purdue University organized the dataset of neural signals recorded by two-photon calcium imaging and running trajectories of mice. While a transgenic mouse (TRE-GCaMP6s x CAMKⅡ-tTa) was running on an air-lifted ball freely, the neural activity of neurons in the primary somatosensory cortex and the behavior of the mouse were recorded simultaneously with a two-photon microscope (Neurolabware) and two cameras on the left and right side of the mouse, respectively. Neural signals were extracted using Suite2p (https://www.suite2p.org/) and limb coordinates were extracted using DeepLabCut (https://www.mackenziemathislab.org/deeplabcut). Descriptions for files ffneu_z_sel.npy : Neuropil-corrected fluorescence signals (somatic fluorescence – 0.7 * neuropil fluorescence), z-scored. spks_z_sel.npy: Deconvolved somatic fluorescence signals, z-scored. neu_z_sel.npy: Neuropil fluorescence signals, z-scored. neudeconv_z_sel.npy: Deconvolved neuropil signals, z-scored. behave_coord_likeli_ori.npy: x and y coordinates of right front, right hind, left front, and left hind limbs. behave_coord_likeli_norm.npy: Min-max scaled x and y coordinates of right front, right hind, left front, and left hind limbs. Idx_coord_neural.npy: Indices of neural frames that match behavior video frames. Because the sampling rate of two-photon calcium imaging (7.8 Hz) is lower than the one of behavior video (30 Hz), there is a discrepancy between neural frames and behavior video frames. This array provides information about frame matching.
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Purdue University West Lafayette
NSF HDR
2117997