Differential effects of aging and cognitive decline on visual exploration behavior in the elderly

Less spatial exploration is associated with poorer spatial memory in midlife adults

Introduction

Despite its importance for navigation, very little is known about how the normal aging process affects spatial exploration behavior. We aimed to investigate: (1) how spatial exploration behavior may be altered early in the aging process, (2) the relationship between exploration behavior and subsequent spatial memory, and (3) whether exploration behavior can classify participants according to age.

Methods

Fifty healthy young (aged 18-28) and 87 healthy midlife adults (aged 43-61) freely explored a desktop virtual maze, learning the locations of nine target objects. Various exploration behaviors (object visits, distance traveled, turns made, etc.) were measured. In the test phase, participants navigated from one target object to another without feedback, and their wayfinding success (% correct trials) was measured.

Results

In the exploration phase, midlife adults exhibited less exploration overall compared to young adults, and prioritized learning target object locations over maze layout. In the test phase, midlife adults exhibited less wayfinding success when compared to the young adults. Furthermore, following principal components analysis (PCA), regression analyses indicated that both exploration quantity and quality components were associated with wayfinding success in the midlife group, but not the young adults. Finally, we could classify participants according to age with similar accuracy using either their exploration behavior or wayfinding success scores.

Discussion

Our results aid in the understanding of how aging impacts spatial exploration, and encourages future investigations into how pathological aging may affect spatial exploration behavior.

Conceptualizing healthy cognitive aging: the role of time and variability

The interest in healthy cognitive aging (HCA) has increased substantially over the past decade. Researchers are interested in exploring how health can be promoted and cognitive decline mitigated when pathology is not present. Identifying the necessary strategies is crucial as the gradual accumulation of small declines can lead to negative effects on quality of life over time. However, the conceptualization of HCA is not agreed upon. In fact, authors often turn to the use of traditional pathology screeners in the context of HCA because of their clear threshold results and their wide use in the different fields. This leads to the assumption that individuals are either cognitively unhealthy and therefore may have some form of dementia or are dementia-free and cognitively healthy. We believe that this view is an overly simplistic approach to the understanding of the aging process. In this work, we explore how HCA has been defined and conceptualized within the different fields. We further discuss how time and variability are key concepts that are often missing when studying HCA and propose a definition that aims to unify the findings from the multidisciplinary research that studies HCA and simplify the translation of knowledge. Incorporating these two novel dimensions to the study of HCA has already been proposed methodologically but has yet been discussed at the conceptual level. We believe that the proposed new approach will allow the identification of individual factors that cause changes in cognitive health and will help build new cognitive health strategies and mitigate further declines.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

Adaptive Aging Safety of Guidance Marks in Rail Transit Connection Systems Based on Eye Movement Data

The aim of this study was to meet the visual cognition needs of the elderly population for the guidance marks and safety guidance marks of the rail transit connection system. Based on the visual characteristics of the elderly population, this paper firstly determined the visual field and sight range of the marks of the elderly population from three aspects—visual angle, visual distance, and height of the elderly population—and constructed the visual recognition space of the elderly population. Then, from the perspective of the setting position, the setting height, and the deflection angle, an adaptive aging safety design method for the guidance marks in the rail transit connection system is proposed. Then, based on the eye movement data of fixation duration, initial fixation duration, and the number of visits, a visual behavior index model is constructed to iteratively optimize the adaptive aging safety design of guidance marks in a rail transit connection system. A radar map is used to calculate the comprehensive index of visual behavior to determine the optimal scheme. Finally, taking the traffic connection system of Shanghai Songjiang University Town Station as an example, the eye movement data of 37 participants were collected, according to the principle that each connection path should only be taken once per person; the above method was used to design 7 connection path guidance marks for an adaptive aging safety design. The results showed that the comprehensive index of visual behavior of different paths had different degrees of improvement of up to 14.00%, which verified the effectiveness of the design method. The research results have certain theoretical significance and application value for the adaptive aging safety design and retrofit of guidance marks of rail transit connection systems.