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

Chen Niu, Alexander D. Cohen, Xin Wen, Ziyi Chen, Pan Lin, Xin Liu, Bjoern H. Menze, Benedikt Wiestler, Yang Wang, Ming Zhang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalBrain Imaging and Behavior
DOIs
StateAccepted/In press - Jan 1 2020
Externally publishedYes

Keywords

  • Functional MRI
  • General linear model
  • Independent component analysis
  • Machine learning
  • Motor function
  • Resting state

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Cognitive Neuroscience
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

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  • Cite this

    Niu, C., Cohen, A. D., Wen, X., Chen, Z., Lin, P., Liu, X., Menze, B. H., Wiestler, B., Wang, Y., & Zhang, M. (Accepted/In press). Modeling motor task activation from resting-state fMRI using machine learning in individual subjects. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-019-00239-9