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

Chen Niu1,2, Alexander D Cohen3, Xin Wen1

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

Brain Imaging and Behavior
|January 7, 2020
PubMed
Summary

A new machine learning approach accurately predicts brain activity using resting-state functional MRI (rs-fMRI) data, outperforming traditional methods for presurgical mapping.

Keywords:
Functional MRIGeneral linear modelIndependent component analysisMachine learningMotor functionResting state

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Area of Science:

  • Neuroimaging
  • Brain Physiology
  • Machine Learning

Background:

  • Resting-state functional MRI (rs-fMRI) offers insights into brain function and is a growing alternative to task-based fMRI for presurgical mapping.
  • A standardized method for identifying eloquent brain areas using rs-fMRI in clinical settings is lacking.

Purpose of the Study:

  • To evaluate a general linear model-based machine learning (GLM-ML) approach for predicting individual motor task activation from rs-fMRI data.
  • To compare the accuracy of the GLM-ML approach against conventional independent component analysis (ICA).

Main Methods:

  • A GLM-ML model was trained using rs-fMRI network maps and hand movement task fMRI data from 47 healthy subjects.
  • The trained model predicted task activation maps from rs-fMRI data alone for unseen subjects.
  • A low-resolution fMRI protocol was employed for resting-state, active, and passive motor tasks.

Main Results:

  • The GLM-ML approach accurately predicted individual task activation differences using rs-fMRI.
  • GLM-ML outperformed ICA in detecting task activation within the primary sensorimotor region.
  • Predicted activation maps from GLM-ML showed strong agreement with passive hand movement fMRI results on an individual level.

Conclusions:

  • The GLM-ML approach robustly predicts individual task activation from low-resolution rs-fMRI data.
  • This method holds significant promise for future clinical applications in presurgical brain mapping.