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Task-based functional magnetic resonance imaging (tfMRI) is widely used to localize brain regions or networks in response to various cognitive tasks. However, given two groups of tfMRI data acquired under distinct task paradigms, it is not clear whether there exist intrinsic inter-group differences in signal composition patterns, and if so, whether these differences could be used for data discrimination...
Sparse coding has been increasingly used to explore brain networks using functional magnetic resonance imaging (fMRI). However, modeling and comparing brain network based on sparse coding is still challenging, especially in clinical applications. In this study, we propose a novel temporal sparse coding method to identify functional connectivity biomarkers in patients with Attention-Deficit/Hyperactivity...
Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity...
Automated cell phenotype image classification is an interesting bioinformatics problem. In this paper, an automated cell phase classification framework is investigated for zebra fish presomitic mesoderm (PSM) images. Low image resolution, gradual transitions between adjacent categories and irregularity of real cell images make this classification task tough but intriguing. The proposed framework first...
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