Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification.

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.

Bending losses of trench-assisted few-mode optical fibers.

A semianalytical method based on the perturbation theory is developed to calculate the bending losses of individual modes of few-mode fibers (FMFs); it is applicable for optical fibers with arbitrary circularly symmetric index profile, especially for trench-assisted fibers. The bending performance of trench-assisted step-index FMFs and parabolic-index FMFs are investigated with three key parameters (i.e., the refractive index difference of trench-cladding, the width of the trench, and the distance of the core-trench). Then, a performance index is defined to estimate the bending performance for FMFs. It is shown that changing the distance of the trench-core, for each order of mode, there is a minimum bending loss, which can be used for fiber optimization. This optimization position (core-trench distance) decreases as the mode order increases. It is found that the bending performance of parabolic-index FMFs is better than that of step-index FMFs with fixed core radius and cutoff wavelength. The conclusions are helpful for understanding the mechanism of bending loss for FMFs, and make contributions to designing and manufacturing of few-mode bend-insensitive fibers.