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Modeling motor task activation from resting-state fMRI using machine learning in individual subjects

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Abstract

Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications.

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Acknowledgments

The first author gratefully acknowledges support of the China Scholarship Council (CSC).

Funding

This study was partially supported by the National Natural Science Foundation of China (Project No.81871331, to M.Z.) and the Key Research and Development Program of Shaanxi Province (2018SF-113, to C.N.).

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Authors and Affiliations

  1. Department of Medical Imaging, the First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061, Shaanxi Province, China

    Chen Niu, Xin Wen & Ming Zhang

  2. Institute for Biomedical Engineering, Technical University of Munich, Munich, Germany

    Chen Niu, Xin Liu & Bjoern H. Menze

  3. Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA

    Alexander D. Cohen, Ziyi Chen & Yang Wang

  4. Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, China

    Pan Lin

  5. Department of Computer Science, Technical University of Munich, Munich, Germany

    Bjoern H. Menze

  6. Department of Neuroradiology, Klinikum rechts der Isar, TU München, Munich, Germany

    Benedikt Wiestler

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  1. Chen Niu
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  2. Alexander D. Cohen
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  3. Xin Wen
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  6. Xin Liu
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  7. Bjoern H. Menze
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  9. Yang Wang
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  10. Ming Zhang
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Correspondence to Yang Wang or Ming Zhang.

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Conflict of interest

Chen Niu, Alexander D. Cohen, Xin Wen, Ziyi Chen, Pan Lin, Xin Liu, Bjoern H. Menze, Benedikt Wiestler, Yang Wang, Ming Zhang declare that they have no conflicts of interest.

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All procedures performed in this study were approved by the Human Research Ethics Committee of the Xi’an Jiaotong University, and were conducted in accordance with the 1964 Helsinki declaration and its later amendments.

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Informed consent was obtained from all individual participants included in the study.

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Niu, C., Cohen, A.D., Wen, X. et al. Modeling motor task activation from resting-state fMRI using machine learning in individual subjects. Brain Imaging and Behavior 15, 122–132 (2021). https://doi.org/10.1007/s11682-019-00239-9

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