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

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.

journal_name

Brain Imaging Behav

journal_title

Brain imaging and behavior

authors

Niu C,Cohen AD,Wen X,Chen Z,Lin P,Liu X,Menze BH,Wiestler B,Wang Y,Zhang M

doi

10.1007/s11682-019-00239-9

subject

Has Abstract

pub_date

2021-02-01 00:00:00

pages

122-132

issue

1

eissn

1931-7557

issn

1931-7565

pii

10.1007/s11682-019-00239-9

journal_volume

15

pub_type

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