Transcriptome analysis reveals potential marker genes for diagnosis of Alzheimer's disease and vascular dementia

Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

In Silico Analysis Reveals the Modulation of Ion Transmembrane Transporters in the Cerebellum of Alzheimer's Disease Patients

Alzheimer's disease (AD) is the most common neurodegenerative disorder. AD hallmarks are extracellular amyloid β (Aβ) plaques and intracellular neurofibrillary tangles in the brain. It is interesting to notice that Aβ plaques appear in the cerebellum only in late stages of the disease, and then it was hypothesized that it can be resistant to specific neurodegenerative mechanisms. However, the role of cerebellum in AD pathogenesis is not clear yet. In this study, we performed an in silico analysis to evaluate the transcriptional profile of cerebellum in AD patients and non-AD subjects in order to deepen the knowledge on its role in AD. The analysis evidenced that only the molecular function (MF) "active ion transmembrane transporter activity" was overrepresented. Regarding the 21 differentially expressed genes included in this MF, some of them may be involved in the ion dyshomeostasis reported in AD, while others assumed, in the cerebellum, an opposite regulation compared to those reported in other brain regions in AD patients. They might be associated to a protective phenotype, that may explain the initial resistance of cerebellum to neurodegeneration in AD. Of note, this MF was not overrepresented in prefrontal cortex and visual cortex indicating that it is a peculiarity of the cerebellum.