Long-Term Effects of Repeated Social Defeat Stress on Brain Activity during Social Interaction in BALB/c Mice.

Understanding the long-term effects of stress on brain function is crucial for understanding the mechanisms of depression. The BALB/c mouse strain has high susceptibility to stress and is thus an effective model for depression. The long-term effects of repeated social defeat stress (SDS) on BALB/c mice, however, are not clear. Here, we investigated the effects of repeated SDS in male BALB/c mice over the subsequent two weeks. Some defeated mice immediately exhibited social avoidance, whereas anxiety-like behavior was only evident at later periods. Furthermore, defeated mice segregated into two groups based on the level of social avoidance, namely, avoidant and nonavoidant mice. The characteristic of avoidance or nonavoidance in each individual was not fixed over the two weeks. In addition, we developed a semi-automated method for analyzing c-Fos expression in the mouse brain to investigate the effect of repeated SDS on brain activity more than two weeks after the end of the stress exposure. Following social interaction, c-Fos expression was reduced in several brain regions in the defeated mice compared with control mice. The correlation of c-Fos expression among these brain areas, with exception of the medial prefrontal cortex (mPFC) and central amygdala (CeA), was increased in defeated mice, suggesting increased synchrony. Notably, c-Fos expression in the lateral habenula (LHb) was different between mice that exhibited social avoidance from immediately after the repeated SDS and those that exhibited social avoidance only at later periods. These observations provide insight into the long-term effects of social stress on behavior and brain activity.

Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection.

PURPOSE: Dysphagia has a large impact on the society because it is a risk factor of malnutrition and aspiration pneumonia, and therefore, it is necessary to elucidate the entire mechanism of dysphagia. In this study, we propose a segmentation method of cervical intervertebral disks (CIDs) in videofluorography (VF) by use of patch-based convolutional neural network (CNN), our multi-channelization (MC) method and image feature selection. METHODS: Twenty image filters are individually applied to a VF frame image to generate feature images. One color image, called a multi-channelized image, is generated by setting three selected feature images to its red, green and blue channels. Patch-based CNN is applied to the MC image, and the segmentation accuracy of CIDs is evaluated by the pixel-based F-measure. The combination of the three feature images is optimized by the simulated annealing method. RESULTS: The proposed method was applied to actual VF dataset consisting of 19 patients and 39 healthy participants. The segmentation accuracy was 59.3% in the F-measure when Sobel and morphological top-hat filters were selected in MC, whereas it was 56.2% when original frame images were used. CONCLUSION: The experimental results demonstrated that the proposed method was able to segment CIDs from actual VF and also that the MC method was able to increase the segmentation accuracy by approximately 3%. In this study, LeNet was used as CNN. One of our future tasks is to use other CNNs.