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Although the neurofeedback of real-time fMRI can reportedly enable people to gain control of the activity in the premotor cortex (PMA) during motor imagery, it is unclear how the neurofeedback training of PMA affect the motor network engaged in the motor execution (ME) and imagery (MI) task. In this study, we investigated the changes in the motor network(More)
BACKGROUND Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could(More)
Numerous studies provide evidences that motor skill learning changes the activity of some brain regions during task as well as some resting networks during rest. However, it is still unclear how motor learning affects the resting-state default-mode network (DMN). Using functional magnetic resonance imaging, this study investigated the alteration of the DMN(More)
Normal aging has been shown to modulate the neural underpinnings of autobiographical memory and emotion processing. Moreover, previous researches have suggested that aging produces a "positivity effect" in autobiographical memory. Although a few imaging studies have investigated the neural mechanism of the positivity effect, the neural substrates underlying(More)
BACKGROUND As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Although either temporal or spatial prior information has been introduced into the constrained ICA and semi-blind ICA methods to improve the performance of ICA in fMRI data analysis, certain types of(More)
Independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data analysis. Although ICA assumes that the sources underlying data are statistically independent, it usually ignores sources' additional properties, such as sparsity. In this study, we propose a two-step super-GaussianICA (2SGICA) method that(More)
BACKGROUND Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from(More)
BACKGROUND Parcellating brain regions into functionally homogeneous subdivisions is critical for understanding normal and abnormal brain functions. NEW METHOD In this study, we developed a new sparse representation-based parcellation method for functional magnetic resonance imaging (fMRI) data, and applied the new method to investigate functional insular(More)
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