Principled neuro-functional connectivity discovery

Kejun Huang, Christos Faloutsos, Nikolaos Sidiropoulos, Partha Pratim Talukdar, Evangelos E. Papalexakis, Tom M. Mitchell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


How can we reverse-engineer the brain connectivity, given the input stimulus, and the corresponding brain-activity measurements, for several experiments? We show how to solve the problem in a principled way, modeling the brain as a linear dynamical system (LDS), and solving the resulting "system identification" problem after imposing sparsity and non-negativity constraints on the appropriate matrices. These are reasonable assumptions in some applications, including magnetoencephalography (MEG). There are three contributions: (a) Proof: We prove that this simple condition resolves the ambiguity of similarity transformation in the LDS identification problem; (b) Algorithm, : we propose an effective algorithm which further induces sparse connectivity in a principled way; and (c) Validation: our experiments on semi-synthetic (C. elegans), as well as real MEG data, show that our method recovers the neural connectivity, and it leads to interpretable results.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsJieping Ye, Suresh Venkatasubramanian
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811522
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015


OtherSIAM International Conference on Data Mining 2015, SDM 2015

Bibliographical note

Funding Information:
Acknowledgments: Research was funded by grants NSF IIS-1247632, NSF IIS-1247489, NSF CDI 0835797, NIH/NICHD 12165321, and a gift from Google. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.

Publisher Copyright:
Copyright © SIAM.


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