RT Journal Article
SR Electronic
T1 FARCI: Fast and Robust Connectome Inference
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.07.330175
DO 10.1101/2020.10.07.330175
A1 Saber Meamardoost
A1 Mahasweta Bhattacharya
A1 EunJung Hwang
A1 Takaki Komiyama
A1 Claudia Mewes
A1 Linbing Wang
A1 Ying Zhang
A1 Rudiyanto Gunawan
YR 2020
UL http://biorxiv.org/content/early/2020/10/08/2020.10.07.330175.abstract
AB 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 gold standard datasets from the Neural Connectomics Challenge (NCC) 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 to the best performing algorithm in the NCC, FARCI produces more accurate networks over different network sizes and subsampling, while providing over two orders of magnitude faster computational speed.Competing Interest StatementThe authors have declared no competing interest.