research-article

Structural Graphical Lasso for Learning Mouse Brain Connectivity

Authors:

Jieping YeAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1385 - 1394

https://doi.org/10.1145/2783258.2783391

Published: 10 August 2015 Publication History

Abstract

Investigations into brain connectivity aim to recover networks of brain regions connected by anatomical tracts or by functional associations. The inference of brain networks has recently attracted much interest due to the increasing availability of high-resolution brain imaging data. Sparse inverse covariance estimation with lasso and group lasso penalty has been demonstrated to be a powerful approach to discover brain networks. Motivated by the hierarchical structure of the brain networks, we consider the problem of estimating a graphical model with tree-structural regularization in this paper. The regularization encourages the graphical model to exhibit a brain-like structure. Specifically, in this hierarchical structure, hundreds of thousands of voxels serve as the leaf nodes of the tree. A node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. The whole brain is considered as the root of the tree. We propose to apply the tree-structural regularized graphical model to estimate the mouse brain network. However, the dimensionality of whole-brain data, usually on the order of hundreds of thousands, poses significant computational challenges. Efficient algorithms that are capable of estimating networks from high-dimensional data are highly desired. To address the computational challenge, we develop a screening rule which can quickly identify many zero blocks in the estimated graphical model, thereby dramatically reducing the computational cost of solving the proposed model. It is based on a novel insight on the relationship between screening and the so-called proximal operator that we first establish in this paper. We perform experiments on both synthetic data and real data from the Allen Developing Mouse Brain Atlas; results demonstrate the effectiveness and efficiency of the proposed approach.

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Cited By

Ulak MOzguven E(2024)Identifying the latent relationships between factors associated with traffic crashes through graphical modelsAccident Analysis & Prevention10.1016/j.aap.2024.107470197(107470)Online publication date: Mar-2024
https://doi.org/10.1016/j.aap.2024.107470
Tavassolipour MKaramzade AMirzaeifard RMotahari SManzuri Shalmani M(2020)Structure Learning of Sparse GGMs Over Multiple Access NetworksIEEE Transactions on Communications10.1109/TCOMM.2019.295708068:2(987-997)Online publication date: Feb-2020
https://doi.org/10.1109/TCOMM.2019.2957080
Wang MHuang JLiu MZhang D(2019)Functional connectivity network analysis with discriminative hub detection for brain disease identificationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011198(1198-1205)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33011198
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Index Terms

Structural Graphical Lasso for Learning Mouse Brain Connectivity
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
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Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
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Graham Williams
Australian Taxation Office

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Published: 10 August 2015

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NSF IIS-0953662, NSF III-1421100, NSF III-1421057

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Ulak MOzguven E(2024)Identifying the latent relationships between factors associated with traffic crashes through graphical modelsAccident Analysis & Prevention10.1016/j.aap.2024.107470197(107470)Online publication date: Mar-2024
https://doi.org/10.1016/j.aap.2024.107470
Tavassolipour MKaramzade AMirzaeifard RMotahari SManzuri Shalmani M(2020)Structure Learning of Sparse GGMs Over Multiple Access NetworksIEEE Transactions on Communications10.1109/TCOMM.2019.295708068:2(987-997)Online publication date: Feb-2020
https://doi.org/10.1109/TCOMM.2019.2957080
Wang MHuang JLiu MZhang D(2019)Functional connectivity network analysis with discriminative hub detection for brain disease identificationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011198(1198-1205)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33011198
Zhao LGkountouna OPfoser D(2019)Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological ConstraintsACM Transactions on Spatial Algorithms and Systems10.1145/33398235:3(1-28)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3339823
Cespedes MMcGree JDrovandi CMengersen KDoecke JFripp J(2018)An efficient algorithm for estimating brain covariance networksPLOS ONE10.1371/journal.pone.019858313:7(e0198583)Online publication date: 12-Jul-2018
https://doi.org/10.1371/journal.pone.0198583
Pavez EEgilmez HOrtega A(2018)Learning Graphs With Monotone Topology Properties and Multiple Connected ComponentsIEEE Transactions on Signal Processing10.1109/TSP.2018.281333766:9(2399-2413)Online publication date: 1-May-2018
https://doi.org/10.1109/TSP.2018.2813337
Pasdeloup BGripon VMercier GPastor DRabbat M(2018)Characterization and Inference of Graph Diffusion Processes From Observations of Stationary SignalsIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2017.27429404:3(481-496)Online publication date: Sep-2018
https://doi.org/10.1109/TSIPN.2017.2742940
Huang FChen S(2018)Learning Dynamic Conditional Gaussian Graphical ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.277746230:4(703-716)Online publication date: 1-Apr-2018
https://doi.org/10.1109/TKDE.2017.2777462
Marjanovic GSolo V(2018)Vector ℓ0 Sparse Conditional Independence Graphs2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8461742(2731-2735)Online publication date: Apr-2018
https://doi.org/10.1109/ICASSP.2018.8461742
Safavi TSripada CKoutra D(2018)Fast network discovery on sequence data via time-aware hashingKnowledge and Information Systems10.1007/s10115-018-1293-8Online publication date: 19-Dec-2018
https://doi.org/10.1007/s10115-018-1293-8
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