MEASUREMENT OF LOCAL CONSISTENCY OF THE WHOLE BRAIN AFTER AEROBIC EXERCISE BASED ON MAGNETIC RESONANCE IMAGING

ABSTRACT Introduction: The rapid development of rs-fMRI in recent years can provide new scientific evidence of the plasticity of the child's brain. Objective: To reveal the effect of short-term moderate-intensity aerobic exercise on local consistency of brain function in children at rest, and to provide new evidence for elucidating the relationship between physical exercise and plasticity of children's brain. Methods: Using resting state functional magnetic resonance imaging (rs-fMRI) technology and local consistency (ReHo) analysis method to detect a 30-min short-term moderate-intensity aerobic exercise before and after children's brain function local consistency changes; using the Flanker task measurement Changes in children's executive function before and after exercise. Results: 1) A 30-min short-term moderate-intensity aerobic exercise made the children's bilateral posterior buckle back, left dorsolateral prefrontal lobe, left frontal medial gyrus, bilateral central posterior gyrus, left suboccipital gyrus, and tongue gyrus. 2) A 30-minute short-term moderate-intensity aerobic exercise improves children's executive function. 3) ReHo increases in bilateral posterior buckle gyrus, bilateral central parietal posterior gyrus, and left dorsal lateral prefrontal lobe are significantly associated with improved executive function. Conclusions: Short-term moderate-intensity aerobic exercise can improve brain plasticity and executive function by increasing local consistency of brain function in children at rest. Level of evidence II; Therapeutic studies - investigation of treatment results.

Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification

Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.