Modeling motor task activation from resting-state fMRI using machine learning in individual subjects.
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 ac...
| Publicado en: | Brain Imaging & Behavior Vol. 15; no. 1; pp. 122 - 133 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148318904&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148318904 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: 2021 vid: 15 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148318904 148318904 NLM31903530 10.1007/s11682-019-00239-9 NLM31903530 148318904 ppf: 122 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Modeling motor task activation from resting-state fMRI using machine learning in individual subjects. aug: au: Niu, Chen Cohen, Alexander D. Wen, Xin Chen, Ziyi Lin, Pan Liu, Xin Menze, Bjoern H. Wiestler, Benedikt Wang, Yang Zhang, Ming affil: Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, No. 277 West Yanta Road, 710061, Xi'an, Shaanxi Province, China sug: subj: Brain Mapping Magnetic Resonance Imaging Hand Relaxation Clinical Assessment Tools Scales Questionnaires ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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