Dementia and neurocognitive disorders最新文献

Alzheimer's disease (AD), one of the most representative neurodegenerative diseases, has diverse neurobiological and pathophysiological mechanisms. Treatment strategies targeting a single mechanism have repeated faced failures because the mechanism of neuronal cell death is very complex that is not fully understood yet. Since complex mechanisms exist to explain AD, a variety of diagnostic biomarkers for diagnosing AD are required. Moreover, standardized evaluations for comprehensive diagnosis using neuropsychological, imaging, and laboratory tools are needed. In this review, we summarize the latest clinical, neuropsychological, imaging, and laboratory evaluations to diagnose patients with AD based on our own experience in conducting a prospective study.

Attention is being paid to diagnosis and treatment of mild cognitive impairment (MCI) because early diagnosis and preventive management can slow down the progression of Alzheimer's disease. In particular, in the present era, the use of biomarkers for predicting conversion into dementia is permitted in medical practice. Therefore, authors aimed to propose additional considerations when updating guidelines for the management of MCI, including predictable biomarkers, revising treatment option after additional clinical trials for cholinesterase inhibitors, and detailed regimes for lifestyle interventions. After reviewing 3 patients with MCI by detailed evaluation, we realized that cholinesterase inhibitors were not recommended. In addition, regular exercise and cognitive training were only possible recommendations for patients according to current guidelines, although all 3 patients had evidence of β-amyloid accumulation and related neurodegeneration. Furthermore, caregivers for all 3 patients were worried whether patients could keep doing regular exercise and cognitive training by themselves and asked about the economic training system which monitors patients so that they can keep training. Therefore, we propose that guidelines for managing MCI need to be updated in the present era when the use of biomarkers for predicting conversion into dementia is permitted in medical practice.

Background and purpose: Medication adherence is essential for effective medical treatment. However, it is challenging for cognitively impaired patients. We investigated whether an automated telephone reminder service improves medication adherence and reduces the decline of cognitive function in isolated patients with cognitive impairment.

Methods: This was a single-center, randomized clinical trial. We enrolled mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients who lived alone or with a cognitively impaired spouse. We provided an automated telephone reminder service for taking medication to the intervention group for 6 months. The control group was provided with general guidelines for taking the medication every month. The participants underwent neuropsychological assessment at the beginning and end of the study. Statistical significance was tested using nonparametric Wilcoxon rank sum and Wilcoxon matched-pairs signed-rank tests.

Results: Thirty participants were allocated randomly to groups, and data for 29 participants were analyzed. The mean age was 79.6 (standard deviation, 6.0) years and 79.3% of the participants were female. There was no significant difference in medication adherence between the 2 groups. However, a subgroup analysis among participants with more than 70% response rates showed better medication adherence compared to the control group (intervention: 94.6%; control: 90.2%, p=0.0478). There was no significant difference in the change in cognitive function between the 2 groups.

Conclusions: If a patient's compliance is good, telephone reminders might be effective in improving medication adherence. It is necessary to develop reminder tools that can improve compliance for cognitively impaired patients.

Background and purpose: Early detection of subjective cognitive decline (SCD) due to Alzheimer's disease (AD) is important for clinical research and effective prevention and management. This study examined if quantitative electroencephalography (qEEG) could be used for early detection of AD in SCD.

Methods: Participants with SCD from 6 dementia clinics in Korea were enrolled. ¹⁸F-florbetaben brain amyloid positron emission tomography (PET) was conducted for all the participants. qEEG was performed to measure power spectrum and source cortical activity.

Results: The present study included 95 participants aged over 65 years, including 26 amyloid PET (+) and 69 amyloid PET (-). In participants with amyloid PET (+), relative power at delta band was higher in frontal (p=0.025), parietal (p=0.005), and occipital (p=0.022) areas even after adjusting for age, sex, and education. Source activities of alpha 1 band were significantly decreased in the bilateral fusiform and inferior temporal areas, whereas those of delta band were increased in the bilateral cuneus, pericalcarine, lingual, lateral occipital, precuneus, posterior cingulate, and isthmus areas. There were increased connections between bilateral precuneus areas but decreased connections between left rostral middle frontal area and bilateral frontal poles at delta band in participants with amyloid PET (+) showed. At alpha 1 band, there were decreased connections between bilateral entorhinal areas after adjusting for covariates.

Conclusions: SCD participants with amyloid PET (+) showed increased delta and decreased alpha 1 activity. qEEG is a potential means for predicting amyloid pathology in SCD. Further longitudinal studies are needed to confirm these findings.

Background and purpose: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline with normal performance on objective neuropsychological tests. SCD, which is the first help-seeking stage and the last stage before the clinical disease stage, can be considered to be the most appropriate time for prevention and treatment. This study aimed to compare characteristics between the amyloid positive and amyloid negative groups of SCD patients.

Methods: A cohort study to identify predictors for the clinical progression to mild cognitive impairment (MCI) or dementia from subjective cognitive decline (CoSCo) study is a multicenter, prospective observational study conducted in the Republic of Korea. In total, 120 people aged 60 years or above who presented with a complaint of persistent cognitive decline were selected, and various risk factors were measured among these participants. Continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the χ² test or Fisher's exact test. Logistic regression models were used to assess the predictors of amyloid positivity.

Results: The multivariate logistic regression model indicated that amyloid positivity on PET was related to a lack of hypertension, atrophy of the left temporal lateral and entorhinal cortex, low body mass index, low waist circumference, less body and visceral fat, fast gait speed, and the presence of the apolipoprotein E ε4 allele in amnestic SCD patients.

Conclusions: The CoSCo study is still in progress, and the authors aim to identify the risk factors that are related to the progression of MCI or dementia in amnestic SCD patients through a two-year follow-up longitudinal study.

Background and purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images.

Methods: In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library.

Results: The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R²) of 3 and 0.3 years, respectively, in predicting the age of the new subjects compared to other regression methods. The results of feature importance analysis showed that the right pallidum, white matter hypointensities on T1-MRI scans, and left hippocampus comprise some of the essential features in predicting brain age.

Conclusions: The MAE and R² accuracies of the BR model predicting brain age gap in the East Asian population showed that the model could reduce the dimensionality of neuroimaging data to provide a meaningful biomarker for individual brain aging.

Background and purpose: The effects of high-intensity interval training (HIIT) interventions on functional brain changes in older adults remain unclear. This preliminary study aimed to explore the effect of physical exercise intervention (PEI), including HIIT, on cognitive function, physical performance, and electroencephalogram patterns in Korean elderly people.

Methods: We enrolled six non-dementia participants aged >65 years from a community health center. PEI was conducted at the community health center for 4 weeks, three times/week, and 50 min/day. PEI, including HIIT, involved aerobic exercise, resistance training (muscle strength), flexibility, and balance. Wilcoxon signed rank test was used for data analysis.

Results: After the PEI, there was improvement in the 30-second sit-to-stand test result (16.2±7.0 times vs. 24.8±5.5 times, p=0.027), 2-minute stationary march result (98.3±27.2 times vs. 143.7±36.9 times, p=0.027), T-wall response time (104.2±55.8 seconds vs.71.0±19.4 seconds, p=0.028), memory score (89.6±21.6 vs. 111.0±19.1, p=0.028), executive function score (33.3±5.3 vs. 37.0±5.1, p=0.046), and total Literacy Independent Cognitive Assessment score (214.6±30.6 vs. 241.6±22.8, p=0.028). Electroencephalography demonstrated that the beta power in the frontal region was increased, while the theta power in the temporal region was decreased (all p<0.05).

Conclusions: Our HIIT PEI program effectively improved cognitive function, physical fitness, and electroencephalographic markers in elderly individuals; thus, it could be beneficial for improving functional brain activity in this population.