Mild cognitive decline: Concept, types, presentation, and management

Multi-stage classification of Alzheimer's disease from 18F-FDG-PET images using deep learning techniques

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), and Alzheimer's disease (AD) from Cognitively Normal (CN), and assess the results. 18F-FDG PET imaging modality for brain were procured from Alzheimer's disease neuroimaging initiative's (ADNI) repository. The ResNet50V2 model layers were utilised for feature extraction, with the final convolutional layers fine-tuned for this dataset's multi-classification objectives. Multiple metrics and feature maps were utilized to scrutinize and evaluate the model's statistical and qualitative inference. The multi-classification model achieved an overarching accuracy of 98.44% and Area under the receiver operating characteristic curve of 95% on the testing set. Feature maps aided in deducing finer aspects of the model's overall operation. This framework helped classifying from the 18F-FDG PET brain images, the subtypes of Mild Cognitive Impairment (MCI) which include EMCI, LMCI, from AD, CN groups and achieved an all-inclusive sensitivity of 94% and specificity of 95% respectively.

Age- and Sex-Specific Prevalence and Modifiable Risk Factors of Mild Cognitive Impairment Among Older Adults in China: A Population-Based Observational Study

Background

Minimal data are available on the prevalence of mild cognitive impairment (MCI) in older Chinese adults. Moreover, the current information on MCI shows important geographical variations.

Objective

We aimed to assess the prevalence and risk factors for MCI by age and sex among older adults in a North Chinese population.

Methods

In this population-based cross-sectional study, we enrolled a random sample of 4,943 adults aged ≥ 60 years between March 2018 and June 2019 in Tianjin, China. Of these, 312 individuals were excluded due to a lack of data (e.g., fasting blood test). As a result, 4,631 subjects were assessed. Individuals with MCI were identified using neuropsychological assessments, including the Mini-Mental State Examination and Activities of Daily Living scale, based on a modified version of the Petersen’s criteria.

Results

The mean (SD) age of the 4,631 participants was 67.6 (4.89) years, and 2,579 (55.7%) were female. The overall age- and sex-standardized prevalence of MCI in our study population was 10.7%. There were significant associations of MCI with age [65–69 vs. 60–64 years, OR = 0.74; 95% confidence interval (CI): 0.58, 0.96], physical activity (≥23.0 vs. <23.0 MET-hours/week, OR = 0.79; 95% CI: 0.64, 0.96), body mass index (BMI) (OR = 0.92; 95% CI: 0.89, 0.95), grip strength (OR = 0.50; 95% CI: 0.38, 0.67), hypertension (yes vs. no, OR = 1.44; 95% CI: 1.18, 1.77), higher levels of sleepiness (OR = 1.80; 95% CI: 1.36, 2.37), and longer sleep duration (OR = 1.40; 95% CI: 1.14, 1.72). The inverse association between BMI and MCI was stronger in older age groups (P for heterogeneity = 0.003). Moreover, the magnitude of association between triglycerides and MCI was different between the sexes (P for heterogeneity = 0.029).

Conclusion

The age- and sex-standardized prevalence of MCI was 10.7% in the study sample. Physical activity, BMI, grip strength, sleepiness, sleep duration, and hypertension were associated with the prevalence of MCI. Additionally, triglycerides and BMI might be differently associated with the presence of MCI for different sexes and age stages, respectively.

Identification of Patterns of Cognitive Impairment for Early Detection of Dementia

Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and cognitively impaired subjects, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2-step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of Mild Cognitive Impairment (MCI), a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.

Potential Fluid Biomarkers for the Diagnosis of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is characterized by a level of cognitive impairment that is lower than normal for a person’s age, but a higher function than that that observed in a demented person. MCI represents a transitional state between normal aging and dementia disorders, especially Alzheimer’s disease (AD). Much effort has been made towards determining the prognosis of a person with MCI who will convert to AD. It is now clear that cerebrospinal fluid (CSF) levels of Aβ40, Aβ42, total tau and phosphorylated tau are useful for predicting the risk of progression from MCI to AD. This review highlights the advantages of the current blood-based biomarkers in MCI, and discusses some of these challenges, with an emphasis on recent studies to provide an overview of the current state of MCI.