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Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many...
Separate brain regions exhibit synchronous intrinsic activity used to assess connectivity patterns known to appear among brain areas. Connectivity is evaluated from functional magnetic resonance imaging (fMRI) measuring the blood oxygen level dependent signal (BOLD) signal. Extensive research has revealed a distinctive pattern of connectivity among brain areas that can be visualized through a functional...
We investigate the correspondence between genetic variations with single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI). A group sparse canonical correlation analysis method (group sparse CCA) was proposed to explore the correlation between these two types of data, which are high dimensional with small number of samples. It can exploit the...
The complexity of the human brain and the limitation of any one imaging approach motivates the need for multimodal measurements to better understand cerebral processing. A very natural goal is to integrate electrophysiological and hemodynamic activity. Among them, concurrent EEG-fMRI studies have shown great promise for understanding intrinsic brain properties yet analyzing such data presents a significant...
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