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It has been proved that unilateral hearing loss (UHL) can cause functional connectivity alterations in adults. However, the mechanism of the human brain coping with the challenge of unilateral hearing deprivation at very early developmental phases remains poorly understood. Here, we performed a resting-state functional near-infrared spectroscopy (fNIRS) study on 3- to 10-month-old infants with varying degrees of unilateral hearing loss to investigate the effect of unilateral auditory deprivation in infants. Using network-based statistics, increased functional connectivity was observed in single-sided deafness (SSD) compared with normal hearing infants, and the right middle temporal gyrus was the most involved nodes. In addition, changes in cortical function in infants were related to the degree of hearing loss, with significantly increased functional connectivity in infants with severe to profound unilateral hearing loss compared with the ones with mild to moderate. Moreover, more significant cortical functional recombination changes were found in right-SSD than in left-SSD infants. For the first time, our study provides evidence for the effects of unilateral hearing deprivation on the early cortical development of the human brain, which would also act as a reference for intervention decisions in children with unilateral hearing loss in clinical settings.

Change detection using synthetic aperture radar (SAR) multi-temporal images only detects the change area and generates no information such as change type, which limits its development. This study proposed a new unsupervised application of SAR images that can recognize the change type of the area. First, a regionally restricted principal component analysis k-mean (RRPCA-Kmean) clustering algorithm, combining principal component analysis, k-mean clustering, and mathematical morphology composition, was designed to obtain pre-classification results in combination with change type vectors. Second, a lightweight MobileNet was designed based on the results of the first stage to perform the reclassification of the pre-classification results and obtain the change recognition results of the changed regions. The experimental results using SAR datasets with different resolutions show that the method can guarantee change recognition results with good change detection correctness.