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Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI

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Abstract

Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Shijie Zhao, Junwei Han, Xintao Hu, Jinglei Lv, Tuo Zhang & Lei Guo

  2. Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, Georgia

    Xi Jiang, Shu Zhang & Tianming Liu

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  1. Shijie Zhao
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  2. Junwei Han
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  3. Xintao Hu
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  4. Xi Jiang
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  6. Tuo Zhang
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Correspondence to Junwei Han or Tianming Liu.

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Funding

J. Han was supported by the National Science Foundation of China under Grant 61473231 and 61522207. S. Zhao was supported by the China Postdoctoral Science Foundation under Grant 2017M613206. X. Hu was supported by the National Science Foundation of China under grant 61473234. L. Guo was supported by the National Science Foundation of China under Grant 61333017. T Liu was funded by NSF CAREER Award (IIS-1149260), NIH R01 DA-033393, NIH R01 AG-042599, NSF CBET-1302089, NSF BCS-1439051, and NSF DBI-1564736.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Zhao, S., Han, J., Hu, X. et al. Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI. Brain Imaging and Behavior 12, 743–757 (2018). https://doi.org/10.1007/s11682-017-9733-8

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