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Alzheimer’s disease (AD) is the leading cause of dementia, and mild cognitive impairment (MCI) is considered the transitional state to AD dementia (ADD) and other types of dementia, whose symptoms are accompanied by altered eye movement. In this work, we reviewed the existing literature and conducted a meta-analysis to extract relevant eye movement parameters that are significantly altered owing to ADD and MCI. We conducted a systematic review of 35 eligible original publications in saccade paradigms and a meta-analysis of 27 articles with specified task conditions, which used mainly gap and overlap conditions in both prosaccade and antisaccade paradigms. The meta-analysis revealed that prosaccade and antisaccade latencies and frequency of antisaccade errors showed significant alterations for both MCI and ADD. First, both prosaccade and antisaccade paradigms differentiated patients with ADD and MCI from controls, however, antisaccade paradigms was more effective than prosaccade paradigms in distinguishing patients from controls. Second, during prosaccade in the gap and overlap conditions, patients with ADD had significantly longer latencies than patients with MCI, and the trend was similar during antisaccade in the gap condition as patients with ADD had significantly more errors than patients with MCI. The anti-effect magnitude was similar between controls and patients, and the magnitude of the latency of the gap effect varied among healthy controls and MCI and ADD subjects, but the effect size of the latency remained large in both patients. These findings suggest that, using gap effect, anti-effect, and specific choices of saccade paradigms and conditions, distinctions could be made between MCI and ADD patients as well as between patients and controls.

Menopause induces various health problems and is associated with obesity, but the association between menopausal status and obesity is unclear due to several confounding factors, such as aging and reduced physical activity. The objective of this study was to examine the association of menopausal status with anthropometric indices and body composition indices in South Korean women. In this cross-sectional study, a total of 734 subjects (297 postmenopausal women, 437 premenopausal women) from five university hospitals in South Korea were included. A binary logistic regression analysis was performed to examine the association of menopause with anthropometric indices and body composition indices. Height, body mass index, waist-to-height ratio, waist-to-hip ratio, and neck, armpit, chest, rib, waist, iliac, and hip circumferences were associated with menopausal status in the crude analysis, but these associations disappeared in the adjusted models. Among the body composition indices, menopausal status was strongly associated with total body water, skeletal muscle mass, body fat mass, and body fat percentage in the crude analysis. However, the associations with body fat mass and body fat percentage disappeared in the adjusted models. Only the associations with total body water and skeletal muscle mass remained in the adjusted models. Most of the anthropometric indices and body composition indices were not associated with menopausal status, but total body water and skeletal muscle mass were significantly lower in postmenopausal women than in premenopausal women.

To maintain current cognitive function and access greater cognitive reserves, nonpharmacological interventions may be a viable alternative for older adults with or without cognitive impairment. This study aimed to compare different nonpharmacological interventions for enhancing global cognition, including mind–body exercise, physical exercise, non-invasive brain stimulation, cognitive training intervention (CTI), acutherapy (ACU), meditation, and music therapy, by applying a network meta-analysis (NMA). Sixty-one randomized controlled trials evaluating the efficacy of interventions on global cognition in older adults with or without mild cognitive decline were selected. An NMA was conducted to compare the efficacy of different nonpharmacological interventions. The NMA revealed that mind–body exercise (standardized mean difference, 1.384; 95% confidence interval, 0.777–1.992); ACU (1.283; 0.478–2.088); meditation (0.910; 0.097–1.724); non-invasive brain stimulation (1.242; 0.254–2.230); CTI (1.269; 0.736–1.802); and physical exercise (0.977; 0.212–1.742), showed positive effects compared to passive controls. There were no significant differences between the efficacies of other interventions. Nonpharmacological interventions may potentially enhance and maintain global cognition through various pathways, such as memorizing movements and enhancing brain plasticity by reducing stress in the older adult population. Additional studies are needed to clarify the impact of other variables, including intervention methods or psychological variables.

Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data. We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics' odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC). LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840. Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.

Objective : To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia. Methods : We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms. Results : Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy. Conclusion : Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores. Significance : Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

Mild cognitive impairment (MCI) is a cognitive decline syndrome in the elderly, often a precursor to dementia. It is a heterogeneous condition that can signal degenerative disorders like Alzheimer's or non-degenerative conditions such as vascular issues, depression, or poorly managed diabetes. Early detection of MCI is crucial for timely intervention, and differentiating its phenotypes helps in understanding its causes, progression, and treatment. EEG, which records brain electrical activity, consists of rhythmic and arrhythmic components. Examining these inherently overlapping EEG components calls for quantification, ensuring that an appropriate physiological mechanism is attributed to a given neural response. This study explores the interaction between APOE ε4 (APOE4) and cognitive impairment on non-oscillatory EEG activity. We examined aperiodic EEG activity using a parameterized spectral estimation approach in a sample comprising 751, 142, and 279 cognitively normal (CN), non-amnestic (naMCI), and amnestic (aMCI) MCI patients, respectively. The 5-min EEG was recorded using a prefrontal two-channel EEG device in a resting state, eyes closed. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The analyses were performed using various statistical methods, including independent -tests and generalized linear models (GLM) with an identity link function. These analyses investigated the main and interaction effects of the APOE4 status and participants' cognitive states. We found interactions between APOE4 and cognitive states in the aperiodic EEG exponent and the spectral power ratio (SPR). Distinct patterns were observed in the exponent, offset, and SPR between APOE4 non-carriers and carriers across the CN, naMCI, and aMCI. Among the APOE4 carriers, the aMCI individuals exhibited heightened aperiodic activity and a reduced SPR than the naMCI. Furthermore, the CN had a lower SPR compared to the naMCI. However, no differences in the aperiodic component and SPR were observed in the APOE4 non-carriers across the cognitive states. The higher aperiodic component and a reduced SPR observed in aMCI relative to naMCI in APOE4 carriers may indicate an interplay between genetic predisposition, neuropathological changes, and cognitive decline. These aperiodic components, combined with APOE4 status, represent promising neurophysiological markers that may help identify individuals at elevated risk for cognitive decline or progression toward AD.

This study examined the alterations of segmental body composition in individuals with Alzheimer’s pathology (AD), including mild cognitive impairment (MCI) and dementia. A multifrequency bioimpedance analysis (BIA) was used to provide segmental water and impedance variables from 365 cognitively normal (CN), 123 MCI due to AD, and 30 AD dementia participants. We compared the BIA variables between the three groups, examined their correlations with neuropsychological screening test scores, and illustrate their 95% confidence RXc graphs. AD dementia participants were older, more depressive, and had worse cognitive abilities than MCI due to AD and CN participants. Although the BIA variables showed weak partial correlations with the cognitive test scores, we found patterns of an increasing water content in lean mass, increasing extra to intracellular water ratio, and decreasing reactance and phase angle in the lower extremities with effect sizes ranging from 0.26 to 0.51 in the groups of MCI and dementia due to AD compared with CN individuals. The RXc graphs upheld the findings with a significant displacement downward and toward the right, dominantly in the lower extremities. Individuals with AD pathology exhibit a reduced body cell mass or cell strength, an abnormal cellular water distribution, and an overhydration status in lean mass, especially in the lower extremities.

A force plate is mainly used in biomechanics; it aims to measure the ground reaction force in a person’s walking or standing position. In this study, a large-area force mat of the piezoresistance sensing type was developed, and a deep-learning-based weight measurement calibration method was applied to solve the problem in which measurements are not normalized because of physical limitations in hardware and signal processing. The test set was composed of the values measured at each point by weight and the value of the center of the pressure variable, and the measured value was predicted using a deep neural network (DNN) regression model. The calibration verification results show that the average weight errors range from a minimum of 0.06% to a maximum of 3.334%. This is simpler than the previous method, which directly measures the ratio of the resistance value to the measured weight of each sensor and derives an equation.

Background: Mild cognitive impairment (MCI) may occur due to several forms of neurodegenerative diseases and non-degenerative conditions and is associated with cognitive impairment that does not affect everyday activities. For a timely diagnosis of MCI to prevent progression to dementia, a screening tool of fast, low-cost, and easy access is needed. Recent research on eye movement hints it a potential application for the MCI screening. However, the precise extent of cognitive function decline and eye-movement control alterations in patients with MCI is still unclear. Objective: This study examined executive control deficits and saccade behavioral changes in patients with MCI using comprehensive neuropsychological assessment and interleaved saccade paradigms. Methods: Patients with MCI (n=79) and age-matched cognitively healthy controls (HC) (n=170) completed four saccadic eye-movement paradigms: prosaccade (PS)/antisaccade (AS), Go/No-go, and a battery of neuropsychological tests. Results: The findings revealed significantly longer latency in patients with MCI than in HC during the PS task. Additionally, patients with MCI had a lower proportion of correct responses and a marked increase in inhibition errors for both PS/AS and Go/No-go tasks. Furthermore, when patients with MCI made errors, they failed to self-correct many of these inhibition errors. In addition to the increase in inhibition errors and uncorrected inhibition errors, patients with MCI demonstrated a trend toward increased correction latencies. We also showed a relationship between neuropsychological scores and correct and error saccade responses. Conclusions: Our results demonstrate that, similar to patients with Alzheimer’s dementia (AD), patients with MCI generate a high proportion of erroneous saccades toward the prepotent target and fail to self-correct many of these errors, which is consistent with an impairment of inhibitory control and error monitoring. Significance: The interleaved PS/AS and Go/No-go paradigms are sensitive and objective at detecting subtle cognitive deficits and saccade changes in MCI, indicating that these saccadic eye movement paradigms have clinical potential as a screening tool for MCI.

Early screening of elderly individuals who are at risk of dementia allows timely medical interventions to prevent disease progression. The portable and low-cost electroencephalography (EEG) technique has the potential to serve it. We examined prefrontal EEG and event-related potential (ERP) variables in association with the predementia stages of Alzheimer's disease (AD). One hundred elderly individuals were recruited from the GARD cohort. The participants were classified into four groups according to their amyloid beta deposition (A+ or A-) and neurodegeneration status (N+ or N-): cognitively normal (CN; A-N-, = 27), asymptomatic AD (aAD; A + N-, = 15), mild cognitive impairment (MCI) with AD pathology (pAD; A+N+, = 16), and MCI with non-AD pathology (MCI(-); A-N+, = 42). Prefrontal resting-state eyes-closed EEG measurements were recorded for five minutes and auditory ERP measurements were recorded for 8 min. Three variables of median frequency (MDF), spectrum triangular index (STI), and positive-peak latency (PPL) were employed to reflect EEG slowing, temporal synchrony, and ERP latency, respectively. Decreasing prefrontal MDF and increasing PPL were observed in the MCI with AD pathology. Interestingly, after controlling for age, sex, and education, we found a significant negative association between MDF and the aAD and pAD stages with an odds ratio (OR) of 0.58. Similarly, PPL exhibited a significant positive association with these AD stages with an OR of 2.36. Additionally, compared with the MCI(-) group, significant negative associations were demonstrated by the aAD group with STI and those in the pAD group with MDF with ORs of 0.30 and 0.42, respectively. Slow intrinsic EEG oscillation is associated with MCI due to AD, and a delayed ERP peak latency is likely associated with general cognitive impairment. MCI individuals without AD pathology exhibited better cortical temporal synchronization and faster EEG oscillations than those with aAD or pAD. The EEG/ERP variables obtained from prefrontal EEG techniques are associated with early cognitive impairment due to AD and non-AD pathology. This result suggests that prefrontal EEG/ERP metrics may serve as useful indicators to screen elderly individuals' early stages on the AD continuum as well as overall cognitive impairment.