Resting connectivity predicts task activation in pre-surgical populations

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• 作者：O. Parker Jones^a ; ^{oiwi@fmrib.ox.ac.uk} ; N.L. Voets^a ; ^b ; J.E. Adcock^b ; ^c ; R. Stacey^d ; S. Jbabdi^a
• 关键词：Connectivity ; Individual variation ; Neural pathology ; Resting-state fMRI
• 刊名：NeuroImage: Clinical
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：13
• 期：Complete
• 页码：378-385
• 全文大小：1304 K
• 卷排序：13

文摘

A method for identifying eloquent areas in the brain from resting fMRI is proposed. It uses supervised learning to predict task contrasts from resting connectivity. Good predictions were obtained in controls and in pre-surgical patient populations. Patient diagnoses included epilepsy, tumours, and vascular lesions. Language maps in patients could be predicted from models trained on controls.