Multimodal Sparse Classifier for Adolescent Brain Age Prediction

1 Apr 2019Peyman Hosseinzadeh KassaniAlexej GossmannYu-Ping Wang

The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity (FC) measures of three sets of data, derived from resting state functional magnetic resonance imaging (rs-fMRI) and task fMRI data, including a working memory n-back task (nb-fMRI) and an emotion identification task (em-fMRI)... (read more)

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