FARCI: Fast and Robust Connectome Inference
| dc.contributor.author | Meamardoost, Saber | en |
| dc.contributor.author | Bhattacharya, Mahasweta | en |
| dc.contributor.author | Hwang, Eun Jung | en |
| dc.contributor.author | Komiyama, Takaki | en |
| dc.contributor.author | Mewes, Claudia | en |
| dc.contributor.author | Wang, Linbing | en |
| dc.contributor.author | Zhang, Ying | en |
| dc.contributor.author | Gunawan, Rudiyanto | en |
| dc.date.accessioned | 2021-11-29T13:26:44Z | en |
| dc.date.available | 2021-11-29T13:26:44Z | en |
| dc.date.issued | 2021-11-24 | en |
| dc.date.updated | 2021-11-25T16:00:28Z | en |
| dc.description.abstract | 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. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | 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. | en |
| dc.identifier.doi | https://doi.org/10.3390/brainsci11121556 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/106767 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | connectome inference | en |
| dc.subject | functional connectome | en |
| dc.subject | two-photon Ca2+ imaging | en |
| dc.subject | Neural Connectomics Challenge | en |
| dc.title | FARCI: Fast and Robust Connectome Inference | en |
| dc.title.serial | Brain Science | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |