Does Empirically Derived Classification of Individuals with Subjective Cognitive Complaints Predict Dementia?

Cognitive decline complaints scale (CDCS): validation and normative studies for the adults and older adults Portuguese population

OBJECTIVES

Subjective Cognitive Complaints, which result from the self-perception of Subjective Cognitive Decline, are frequently reported by older adults. The Cognitive Decline Complaints Scale (CDCS) assesses subjective complaints of cognitive decline in several cognitive domains through three levels of severity. This study aims to psychometrically validate this instrument considering the Classical Test Theory, and to establish preliminary normative data of the CDCS for adults and older adults of the Portuguese population.

METHODS

The community-based sample consisted of 199 cognitively healthy Portuguese participants, aged 50 years or older, stratified according to several sociodemographic variables. In addition to the CDCS, all participants responded to an extensive neuropsychological assessment protocol.

RESULTS

The psychometric characteristics of the CDCS were generally adequate for this community sample (e.g. Cronbach's alpha = .936). As for the sociodemographic variables analyzed, only the geographic region showed differences in the CDCS scores, which were more significant in the Azores. There were no significant correlations or differences between the CDCS scores and age and educational level and, therefore, normative data were explored considering the total sample.

CONCLUSION

As a scale, the CDCS allows for the detailed assessment of subjective cognitive complaints and the determination of whether or not such complaints are considered normative, which will facilitate an empirically based understanding of this dimension of psychological functioning and also provide indications as to the need for a more extensive neuropsychological assessment.

Diagnosing Mild Cognitive Impairment Among Racially Diverse Older Adults: Comparison of Consensus, Actuarial, and Statistical Methods

BACKGROUND

Actuarial and statistical methods have been proposed as alternatives to conventional methods of diagnosing mild cognitive impairment (MCI), with the aim of enhancing diagnostic and prognostic validity, but have not been compared in racially diverse samples.

OBJECTIVE

We compared the agreement of consensus, actuarial, and statistical MCI diagnostic methods, and their relationship to race and prognostic indicators among diverse older adults.

METHODS

Participants (N = 354; M age = 71; 68% White, 29% Black) were diagnosed with MCI or normal cognition (NC) according to clinical consensus, actuarial neuropsychological criteria (Jak/Bondi), and latent class analysis (LCA). We examined associations with race/ethnicity, longitudinal cognitive and functional change, and incident dementia.

RESULTS

MCI rates by consensus, actuarial criteria, and LCA were 44%, 53%, and 41%, respectively. LCA identified three MCI subtypes (memory; memory/language; memory/executive) and two NC classes (low normal; high normal). Diagnostic agreement was substantial, but agreement of the actuarial method with consensus and LCA was weaker than the agreement between consensus and LCA. Among cases classified as MCI by actuarial criteria only, Black participants were over-represented, and outcomes were generally similar to those of NC participants. Consensus diagnoses best predicted longitudinal outcomes overall, whereas actuarial diagnoses best predicted longitudinal functional change among Black participants.

CONCLUSION

Consensus diagnoses optimize specificity in predicting dementia, but among Black older adults, actuarial diagnoses may be more sensitive to early signs of decline. Results highlight the need for cross-cultural validity in MCI diagnosis and should be explored in community- and population-based samples.

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.