Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia

Abstract: Published reports of functional abnormalities in schizophrenia remain divergent due to lack of staging point-of-view and whole-brain analysis. To identify key functional-connectivity differences of first-episode (FE) and chronic patients from controls using resting-state functional MRI, and determine changes that are specifically associated with disease onset, a clinical staging model is adopted. We analyze functional-connectivity differences in prodromal, FE (mostly drug naive), and chronic patients from their matched controls from 6 independent datasets involving a total of 789 participants (343 patients). Brain-wide functional-connectivity analysis was performed in different datasets and the results from the datasets of the same stage were then integrated by meta-analysis, with Bonferroni correction for multiple comparisons. Prodromal patients differed from controls in their pattern of functional-connectivity involving the inferior frontal gyri (Broca’s area). In FE patients, 90% of the functional-connectivity changes involved the frontal lobes, mostly the inferior frontal gyrus including Broca’s area, and these changes were correlated with delusions/blunted affect. For chronic patients, functional-connectivity differences extended to wider areas of the brain, including reduced thalamo-frontal connectivity, and increased thalamo-temporal and thalamo-sensorimoter connectivity that were correlated with the positive, negative, and general symptoms, respectively. Thalamic changes became prominent at the chronic stage. These results provide evidence for distinct patterns of functional-dysconnectivity across FE and chronic stages of schizophrenia. Importantly, abnormalities in the frontal language networks appear early, at the time of disease onset. The identification of stage-specific pathological processes may help to understand the disease course of schizophrenia and identify neurobiological markers crucial for early diagnosis.

High-Resolution ISAR Imaging by Exploiting Sparse Apertures

Abstract: Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven optimization problem. For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity. This paper derives a new autofocus algorithm to exploit the sparse apertures (SAs) data for ISAR imagery. A sparsity-driven optimization based on Bayesian compressive sensing (BCS) is developed. In addition, we also propose an approach to determine the sparsity coefficient in the optimization by using constant-false-alarm-rate (CFAR) detection. Solving the sparsity-driven optimization with a modified Quasi-Newton algorithm, the phase error is corrected by combining a two-step phase correction approach, and well-focused image with effective noise suppression is obtained from SA data. Real data experiments show the validity of the proposed method.