Inferring functional connectivity through graphical directed information

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dc.citation.articleNumber46019en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleJournal of Neural Engineeringen_US
dc.citation.volumeNumber18en_US
dc.contributor.authorYoung, Josephen_US
dc.contributor.authorNeveu, Curtis L.en_US
dc.contributor.authorByrne, John H.en_US
dc.contributor.authorAazhang, Behnaamen_US
dc.date.accessioned2021-05-07T19:23:46Zen_US
dc.date.available2021-05-07T19:23:46Zen_US
dc.date.issued2021en_US
dc.description.abstractObjective. Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections. Approach. Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series. Main results. GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections. Significance. GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.en_US
dc.identifier.citationYoung, Joseph, Neveu, Curtis L., Byrne, John H., et al.. "Inferring functional connectivity through graphical directed information." <i>Journal of Neural Engineering,</i> 18, no. 4 (2021) IOP Publishing: https://doi.org/10.1088/1741-2552/abecc6.en_US
dc.identifier.digitalYoung_2021en_US
dc.identifier.doihttps://doi.org/10.1088/1741-2552/abecc6en_US
dc.identifier.urihttps://hdl.handle.net/1911/110494en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.titleInferring functional connectivity through graphical directed informationen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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