Profiles of cognitive impairment in the continuum from normal cognition to Alzheimer's clinical syndrome: Contributions of the short-term memory binding tests

A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer's Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA's potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.

Targeting the function of the transentorhinal cortex to identify early cognitive markers of Alzheimer's disease

Initial neuropathology of early Alzheimer's disease accumulates in the transentorhinal cortex. We review empirical data suggesting that tasks assessing cognitive functions supported by the transenthorinal cortex are impaired as early as the preclinical stages of Alzheimer's disease. These tasks span across various domains, including episodic memory, semantic memory, language, and perception. We propose that all tasks sensitive to Alzheimer-related transentorhinal neuropathology commonly rely on representations of entities supporting the processing and discrimination of items having perceptually and conceptually overlapping features. In the future, we suggest a screening tool that is sensitive and specific to very early Alzheimer's disease to probe memory and perceptual discrimination of highly similar entities.

Deficits in short-term memory binding are detectable in individuals with brain amyloid deposition in the absence of overt neurodegeneration in the Alzheimer's disease continuum

The short-term memory binding (STMB) test involves the ability to hold in memory the integration between surface features, such as shapes and colours. The STMB test has been used to detect Alzheimer's disease (AD) at different stages, from preclinical to dementia, showing promising results. The objective of the present study was to verify whether the STMB test could differentiate patients with distinct biomarker profiles in the AD continuum. The sample comprised 18 cognitively unimpaired (CU) participants, 30 mild cognitive impairment (MCI) and 23 AD patients. All participants underwent positron emission tomography (PET) with Pittsburgh compound-B labelled with carbon-11 ([¹¹C]PIB) assessing amyloid beta (Aβ) aggregation (A) and 18fluorine-fluorodeoxyglucose ([¹⁸F]FDG)-PET assessing neurodegeneration (N) (A-N- [n = 35]); A+N- [n = 11]; A+ N+ [n = 19]). Participants who were negative and positive for amyloid deposition were compared in the absence (A-N- vs. A+N-) of neurodegeneration. When compared with the RAVLT and SKT memory tests, the STMB was the only cognitive task that differentiated these groups, predicting the group outcome in logistic regression analyses. The STMB test showed to be sensitive to the signs of AD pathology and may represent a cognitive marker within the AD continuum.

Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia.

OBJECTIVE

Compare machine learning (ML) architectures classifying and predicting aMCI and asset the contribution of cognitive measures including binding function in distinction and prediction of aMCI.

METHODS

We conducted a two-year follow-up assessment of a sample of 154 subjects with a comprehensive multidomain neuropsychological battery. Statistical analysis was proposed using complete ML architectures to compare subjects' performance to classify and predict aMCI. Additionally, permutation importance and Shapley additive explanations (SHAP) routines were implemented for feature importance selection.

RESULTS

AdaBoost, gradient boosting, and XGBoost had the highest performance with over 80%success classifying aMCI, and decision tree and random forest had the highest performance with over 70%success predictive routines. Feature importance points, the auditory verbal learning test, short-term memory binding tasks, and verbal and category fluency tasks were used as variables with the first grade of importance to distinguish healthy cognition and aMCI.

CONCLUSION

Although neuropsychological measures do not replace biomarkers' utility, it is a relatively sensitive and specific diagnostic tool for aMCI. Further studies with ML must identify cognitive performance that differentiates conversion from average MCI to the pathological MCI observed in AD.