A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.

To design an ensemble learning based prediction model using different breast DCE-MR post-contrast sequence images to distinguish two kinds of breast cancer subtypes (luminal and non-luminal). We retrospectively studied preoperative dynamic contrast enhanced-magnetic resonance imaging and molecular information of 266 breast cancer cases with either luminal subtype (luminal A and luminal B) or non-luminal subtype (human epidermal growth factor receptor 2 and triple negative). Then, multiple bounding boxes covering tumor lesions were acquired from three series of post-contrast DCE-MR sequence images which were determined by radiologists. Afterwards, three baseline convolutional neural networks (CNNs) with same architecture were concurrently trained, followed by preliminary prediction of probabilities from the testing database. Finally, the classification and evaluation of breast subtypes were realized by means of fusing predicted results from three CNNs employed via ensemble learning based on weighted voting. Taking advantage of 5-fold cross validation CV, the average prediction specificity, accuracy, precision and area under the ROC curve on testing dataset for the luminal versus non-luminal are 0.958, 0.852, 0.961, and 0.867, respectively, which empirically demonstrate that our proposed ensemble model has highly reliability and robustness. The breast DCE-MR post-contrast sequence image analysis utilizing the ensemble CNN model based on deep learning could show a valuable and extendible practical application on breast molecular subtype identification.