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FARCI: Fast and Robust Connectome Inference

Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, USA
Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, USA
Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
Cell Biology and Anatomy Discipline, Center for Brain Function and Repair, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USA
Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA
Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Department of Cell and Molecular Biology, University of Rhode Island, Kingston, RI 02881, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Dimiter Prodanov and Newton Howard
Brain Sci. 2021, 11(12), 1556;
Received: 9 July 2021 / Revised: 12 November 2021 / Accepted: 18 November 2021 / Published: 24 November 2021
(This article belongs to the Special Issue Neuroinformatics and Signal Processing)
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. View Full-Text
Keywords: connectome inference; functional connectome; two-photon Ca2+ imaging; Neural Connectomics Challenge connectome inference; functional connectome; two-photon Ca2+ imaging; Neural Connectomics Challenge
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MDPI and ACS Style

Meamardoost, S.; Bhattacharya, M.; Hwang, E.J.; Komiyama, T.; Mewes, C.; Wang, L.; Zhang, Y.; Gunawan, R. FARCI: Fast and Robust Connectome Inference. Brain Sci. 2021, 11, 1556.

AMA Style

Meamardoost S, Bhattacharya M, Hwang EJ, Komiyama T, Mewes C, Wang L, Zhang Y, Gunawan R. FARCI: Fast and Robust Connectome Inference. Brain Sciences. 2021; 11(12):1556.

Chicago/Turabian Style

Meamardoost, Saber, Mahasweta Bhattacharya, Eun J. Hwang, Takaki Komiyama, Claudia Mewes, Linbing Wang, Ying Zhang, and Rudiyanto Gunawan. 2021. "FARCI: Fast and Robust Connectome Inference" Brain Sciences 11, no. 12: 1556.

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