Background: Cerebral white matter hyperintensities (WMH) are prevalent incident findings on brain MRI scans among elderly people and have been consistently implicated in cognitive dysfunction. However, differential roles of WMH by region in cognitive function are still unclear. The aim of this study was to ascertain the differential role of regional WMH in predicting progression from mild cognitive impairment (MCI) to different subtypes of dementia. Methods: Participants were recruited from the Clinical Research Center for Dementia of South Korea (CREDOS) study. A total of 622 participants with MCI diagnoses at baseline and follow-up evaluations were included for the analysis. Initial MRI scans were rated for WMH on a visual rating scale developed for the CREDOS. Differential effects of regional WMH in predicting incident dementia were evaluated using the Cox proportional hazards model. Results: Of the 622 participants with MCI at baseline, 139 patients (22.3%) converted to all-cause dementia over a median of 14.3 (range 6.0–36.5) months. Severe periventricular WMH (PWMH) predicted incident all-cause dementia (Hazard ratio (HR) 2.22; 95% confidence interval (CI) 1.43–3.43) and Alzheimer's disease (AD) (HR 1.86; 95% CI 1.12–3.07). Subcortical vascular dementia (SVD) was predicted by both PWMH (HR 16.14; 95% CI 1.97–132.06) and DWMH (HR 8.77; 95% CI 1.77–43.49) in more severe form (≥ 10 mm). Conclusions: WMH differentially predict dementia by region and severity. Our findings suggest that PWMH may play an independent role in the pathogenesis of dementia, especially in AD.

Background: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods: Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results: The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions: The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

Kim JH. Dement Neurocognitive Disord. 2018 Dec;17(4):131-136. https://doi.org/10.12779/dnd.2018.17.4.131

Background: Personality may predispose family caregivers to experience caregiving differently in similar situations and influence the outcomes of caregiving. A limited body of research has examined the role of some personality traits for health-related quality of life (HRQoL) among family caregivers of persons with dementia (PWD) in relation to burden and depression. Methods: Data from a large clinic-based national study in South Korea, the Caregivers of Alzheimer's Disease Research (CARE), were analyzed (N = 476). Path analysis was performed to explore the association between family caregivers’ personality traits and HRQoL. With depression and burden as mediating factors, direct and indirect associations between five personality traits and HRQoL of family caregivers were examined. Results: Results demonstrated the mediating role of caregiver burden and depression in linking two personality traits (neuroticism and extraversion) and HRQoL. Neuroticism and extraversion directly and indirectly influenced the mental HRQoL of caregivers. Neuroticism and extraversion only indirectly influenced their physical HRQoL. Neuroticism increased the caregiver's depression, whereas extraversion decreased it. Neuroticism only was mediated by burden to influence depression and mental and physical HRQoL. Conclusions: Personality traits can influence caregiving outcomes and be viewed as an individual resource of the caregiver. A family caregiver's personality characteristics need to be assessed for tailoring support programs to get the optimal benefits from caregiver interventions.

Rhee SY, et al. J Clin Neurol. 2021 Oct;17(4):524-533. https://doi.org/10.3988/jcn.2021.17.4.524

Objective Neuropsychiatric symptoms of dementia are prevalent and extremely burdening for the patient and caregivers, but the underlying mechanism of these symptoms has not been investigated. This study aimed to investigate the relationship between neuropsychiatric symptoms and default-mode functional connectivity in Alzheimer’s disease.Methods Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory. Functional magnetic resonance imaging was conducted on seventy patients with Alzheimer’s disease during rest. We conducted a seed-based functional connectivity analysis to identify anterior and posterior default-mode networks (DMN). Seeds were the medial prefrontal cortex (Montreal Neurological Institute 12, 51, 36; seed radius=3 mm) for the anterior DMN and the precuneus (Montreal Neurological Institute -6, -63, 27; seed radius=3 mm) for the posterior DMN We then correlated the scores on neuropsychiatric inventory syndromes (apathy, hyperactivity, affective, and psychosis syndrome) with maps of connectivity in the default-mode network.Results There was a significant correlation between decreased connectivity in the medial prefrontal cortex of the anterior defaultmode network and hyperactivity (agitation, irritability, aberrant motor behavior, euphoria, and disinhibition) syndrome (p<0.05, family wise error cluster-level corrected).Conclusion Our study demonstrated that hyperactivity syndrome is related to hypoconnected default-mode network in Alzheimer’s disease. This finding suggests that specific network alterations are associated with certain neuropsychiatric syndromes.