Machine learning-based multimodal prediction of language outcomes in chronic aphasia

Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and specific language measures based on a multimodal n...

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Bibliographic Details
Main Authors:	Basilakos, A. (Author), Bonilha, L. (Author), Fridriksson, J. (Author), Hillis, A. (Author), Kristinsson, S. (Author), Newman-Norlund, R. (Author), Rorden, C. (Author), Xiao, F. (Author), Yourganov, G. (Author), Zhang, W. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2021
Subjects:	adult Adult aged Aged Aged, 80 and over aphasia Aphasia Article brain blood flow brain circulation brain hemorrhage CBF cerebrovascular accident Cerebrovascular Circulation chronic aphasia chronic disease Chronic Disease complication controlled study data accuracy diffusion tensor imaging Diffusion Tensor Imaging disease severity FA female Female fMRI fractional anisotropy functional magnetic resonance imaging functional neuroimaging Functional Neuroimaging human Humans ischemic stroke language processing language test Language Tests left hemisphere lesion machine learning Magnetic Resonance Imaging major clinical study male Male middle aged Middle Aged multimodal multimodal imaging Multimodal Imaging neuroimaging Neuroimaging nuclear magnetic resonance imaging Outcome Assessment, Health Care pathology pathophysiology physiology priority journal procedures severity of illness index Severity of Illness Index Stroke support vector machine Support Vector Machine very elderly Western aphasia battery
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Machine learning-based multimodal prediction of language outcomes in chronic aphasia

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