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Unsupervised Network Discovery for Brain Imaging Data

Published: 04 August 2017 Publication History

Abstract

A common problem with spatiotemporal data is how to simplify the data to discover an underlying network that consists of cohesive spatial regions (nodes) and relationships between those regions (edges). This network discovery problem naturally exists in a multitude of domains including climate data (dipoles), astronomical data (gravitational lensing) and the focus of this paper, fMRI scans of human subjects. Whereas previous work requires strong supervision, we propose an unsupervised matrix tri-factorization formulation with complex constraints and spatial regularization. We show that this formulation works well in controlled experiments with synthetic networks and is able to recover the underlying ground-truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals.

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MP4 File (bai_network_discovery.mp4)

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    cover image ACM Conferences
    KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2017
    2240 pages
    ISBN:9781450348874
    DOI:10.1145/3097983
    © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 04 August 2017

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    Author Tags

    1. brain
    2. fmri
    3. network discovery
    4. spatial regularization
    5. spatiotemporal data

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    KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2022)Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based ApproachesFrontiers in Neuroscience10.3389/fnins.2022.86140216Online publication date: 25-Apr-2022
    • (2022)Deep Learning for Prognosis Using Task-fMRIProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539362(1589-1597)Online publication date: 14-Aug-2022
    • (2022)ERNetProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539227(1666-1675)Online publication date: 14-Aug-2022
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