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The brain connectome collects the complex network architectures, looking at both static and dynamic functional connectivity. The former normally requires stationary signals and connections. However, the human brain activity and connections are most likely time dependent and dynamic, and related to ongoing rhythmic activity. We developed an open-source MATLAB toolbox DynamicBC with user-friendly graphical user interfaces, implementing both dynamic functional and effective connectivity for tracking brain dynamics from functional MRI. We provided two strategies for dynamic analysis: (1) the commonly utilized sliding-window analysis and (2) the flexible least squares based time-varying parameter regression strategy. The toolbox also implements multiple functional measures including seed-to-voxel analysis, region of interest (ROI)-to-ROI analysis, and voxel-to-voxel analysis. We describe the principles of the implemented algorithms, and then present representative results from simulations and empirical data applications. We believe that this toolbox will help neuroscientists and neurologists to easily map dynamic brain connectomics.

Abstract-Palmprint is a unique and reliable biometric characteristic with high usability. With the increasing demand of highly accurate and robust palmprint authentication system, multispectral imaging has been employed to acquire more discriminative information and increase the antispoof capability of palmprint. This paper presents an online multispectral palmprint system that could meet the requirement of real-time application. A data acquisition device is designed to capture the palmprint images under Blue, Green, Red, and near-infrared (NIR) illuminations in less than 1 s. A large multispectral palmprint database is then established to investigate the recognition performance of each spectral band. Our experimental results show that the Red channel achieves the best result, whereas the Blue and Green channels have comparable performance but are slightly inferior to the NIR channel. After analyzing the extracted features from different bands, we propose a score level fusion scheme to integrate the multispectral information. The palmprint verification experiments demonstrated the superiority of multispectral fusion to each single spectrum, which results in both higher verification accuracy and antispoofing capability.