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Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects...
Simultaneous collection of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a promising neuroimaging technique, which can provide high resolution in both spatial and temporal domain. Because EEG recorded in MRI scanners is heavily contaminated with gradient artefact (GA), removal of GA from EEG is a crucial step in EEG-fMRI data analysis. To date, the most efficient methods...
Functional magnetic resonance imaging (fMRI) has become a common tool in investigating brain activities on human subjects. Although each region of the brain was thought to be independent and responsible only for particular tasks and specialized functions, recent studies have shown that different regions of the brain interact with each other in performing specific tasks or even during the resting state...
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