FARCI: Fast and Robust Connectome Inference

Simple item page
dc.contributor.authorMeamardoost, Saber
dc.contributor.authorBhattacharya, Mahasweta
dc.contributor.authorHwang, Eun Jung
dc.contributor.authorKomiyama, Takaki
dc.contributor.authorMewes, Claudia
dc.contributor.authorWang, Linbing
dc.contributor.authorZhang, Ying
dc.contributor.authorGunawan, Rudiyanto
dc.contributor.otherState University of New York (SUNY) Buffalo
dc.contributor.otherUniversity of California San Diego
dc.contributor.otherRosalind Franklin University Medical & Science
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherVirginia Polytechnic Institute & State University
dc.contributor.otherUniversity of Rhode Island
dc.date.accessioned2023-09-28T22:04:40Z
dc.date.available2023-09-28T22:04:40Z
dc.date.issued2021
dc.description.abstractThe 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_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationMeamardoost, S., Bhattacharya, M., Hwang, E. J., Komiyama, T., Mewes, C., Wang, L., Zhang, Y., & Gunawan, R. (2021). FARCI: Fast and Robust Connectome Inference. In Brain Sciences (Vol. 11, Issue 12, p. 1556). MDPI AG. https://doi.org/10.3390/brainsci11121556
dc.identifier.doi10.3390/brainsci11121556
dc.identifier.orcidhttps://orcid.org/0000-0001-8759-0194
dc.identifier.orcidhttps://orcid.org/0000-0001-9609-4600
dc.identifier.orcidhttps://orcid.org/0000-0002-9491-457X
dc.identifier.orcidhttps://orcid.org/0000-0001-9653-2893
dc.identifier.urihttps://ir.ua.edu/handle/123456789/12298
dc.languageEnglish
dc.language.isoen_US
dc.publisherMDPI
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectconnectome inference
dc.subjectfunctional connectome
dc.subjecttwo-photon Ca2+ imaging
dc.subjectNeural Connectomics Challenge
dc.subjectDYNAMICS
dc.subjectNeurosciences
dc.titleFARCI: Fast and Robust Connectome Inferenceen_US
dc.typeArticle
dc.typetext

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PMC8699247-brainsci-11-01556.pdf
Size:
2.82 MB
Format:
Adobe Portable Document Format
Download

Collections

Research and Publications - Department of Health Science