Neuropsychological tests in Alzheimer's disease

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Metrological properties of neuropsychological tests for measuring cognitive change in individuals with prodromal Alzheimer's disease

OBJECTIVES

In Alzheimer's Disease (AD) research, choosing appropriate method for measuring change in cognitive function over time can be challenging. The aim for this study was to examine the sensitivity of four neuropsychological tests used to measure cognition during the transition from mild cognitive impairment (MCI) to AD, and the impacts of associated covariates.

METHODS

We enrolled 223 patients with MCI who progressed to AD and had completed multiple follow-up assessments in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We constructed nonlinear mixed model for multivariate longitudinal data assuming that multiple neuropsychological tests would exhibit nonlinear transformation of a common factor in the latent cognitive process underlying the progression from MCI to AD.

RESULTS

The Clinical Dementia Rating-Sum of the Boxes (CDR-SB) and Alzheimer's Disease Assessment Scale (11 items; ADAS-11) were more sensitive to cognitive changes in individuals with higher cognitive function, the Functional Activities Questionnaire (FAQ) was more sensitive to cognitive changes in individuals with middle cognitive function, and the Mini-Mental State Examination (MMSE) was more sensitive to cognitive changes in individuals with lower cognitive function. Gender (p = 0.0139) and educational level (p = 0.0094) had varying effects on different tests, such that men performed better on the FAQ and CDR-SB, and individuals with higher educational level tended to perform better on the FAQ and MMSE.

CONCLUSIONS

When choosing appropriate neuropsychological tests in cognitive measurements, the cognitive functional level of the patient as well as the impacts of covariates should be considered.

The traveling salesman problem as a new screening test in early Alzheimer's disease: an exploratory study. Visual problem-solving in AD

BACKGROUND AND AIMS

In the clinical setting, brief general mental status tests tend to detect early-stage Alzheimer's disease (AD) less well than more specific cognitive tests. Some preliminary information was collected on the diagnostic accuracy of the Traveling Salesman Problem (TSP) compared with the Mini-Mental State Examination (MMSE) in recognizing early AD from normal aging.

METHODS

Fifteen AD outpatients (mean +/- SD MMSE: 24.45 +/- 2.61) and 30 age- and education-matched controls were submitted in a single blind protocol to a paper-and-pencil visually-presented version of the TSP, containing a random array of 30 points (TSP30). The task consisted of drawing the shortest continuous path, passing through each point once and only once, and returning to the starting point. Path lengths for subjects' solutions were computed and compared with the optimal solution given by a specific evolutionary algorithm called GenD.

RESULTS

TP30 discriminated significantly better between AD subjects and controls (ROC curve AUC = 0.976; 95% CI 0.94-1.01) compared with the MMSE corrected for age and education (ROC curve AUC = 0.877; 95% CI 0.74-1.005). A path length of 478.2354, taken as "cut-off point", classified correctly subjects with a sensitivity of 93.3% and a specificity of 99.3%, whereas a score corrected for age and education of 25.85 on the MMSE had a sensitivity of 73.3% and a specificity of 96.7%.

CONCLUSIONS

The TSP seems to be particularly sensitive to early AD and independent of patient's age and educational level. The high diagnostic ability, simplicity, and independence of age and education make the TSP promising as a screening test for early AD.