Dementia and neurocognitive disorders最新文献

Background and purpose: According to the amyloid cascade hypothesis, fibrillary amyloid-beta load in the brain causes Alzheimer's disease (AD) with toxic effects. Recently, perivascular spaces (PVSs), fluid-filled cavities around small penetrating arterioles and venules in the brain, and the glymphatic system relationship with type 2 diabetes mellitus (DM2) and AD has been an important research topic from a physiopathological point of view. There are two types of PVSs that are associated with sporadic atherosclerosis and cerebral amyloid angiopathy. In this study, we evaluated the relationship between the number and localization of enlarged PVSs in AD.

Methods: A total of 254 patients with AD and 125 healthy controls were included in this study All the patients were evaluated with neurological and cognitive examinations and magnetic resonance imaging (MRI). PVSs on MRI were graded by recording their number and location. The study was a retrospective study.

Results: In our study, the number of white matter convexity-central semiovale localized PVSs was higher in patients than in the control group. In addition, the number of PVSs in this localization score was higher in patients with DM2. Cerebral PVS counts were higher in patients with AD than in the control group.

Conclusions: These results suggest the important role of cerebral amyloid angiopathy, one of the vascular risk factors, and the glymphatic system in the pathogenesis of AD. In addition, the results of our study suggest that the evaluation of PVSs levels, especially at the (centrum semiovale), using imaging studies in AD is a potential diagnostic option.

Background and purpose: The Montreal Cognitive Assessment (MoCA) has been known as a screening test for detecting mild cognitive impairment (MCI) better than Mini-Mental State Examination (MMSE). However, in previous domestic studies, no significant difference was found in the discriminability between MoCA and MMSE. Researchers have suggested that this might be because older Koreans are less educated than older Westerners. This study was conducted to examine the effect of education on the discriminability of MoCA compared to the MMSE.

Methods: Participants were 123 cognitively normal elderly, 118 with vascular MCI, 108 with amnestic MCI, 121 with vascular dementia, and 113 with dementia of the Alzheimer's type. The Korean-MoCA (K-MoCA) and Korean-MMSE (K-MMSE) were administered. Multiple regression analyses and receiver operating characteristic (ROC) curve analyses were performed.

Results: In all participants, education significantly affected both K-MoCA and K-MMSE scores along with age. The effect of education was re-examined by subgroup analysis after dividing subjects according to the level of education. Effect of education on K-MoCA and K-MMSE was only shown in the group with <9 years of education. ROC curve analyses revealed that the discriminability of K-MoCA to differentiate between vascular MCI and normal elderly was significantly higher than that of K-MMSE. When re-examining subgroups divided by education level, however, this higher discriminability of K-MoCA disappeared in the group with <9 years of education.

Conclusions: These results indicate no difference in discriminating cognitive deficits between K-MoCA and K-MMSE in Korean elderly with <9 years of education.

Background and purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images.

Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores.

Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03).

Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

[This corrects the article on p. 17 in vol. 21, PMID: 35154337.].

[This corrects the article on p. 41 in vol. 17, PMID: 30906391.].