Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP.

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.

A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting.

Background: Predicting conversion to probable Alzheimer&s disease (AD) from amnestic mild cognitive impairment (aMCI) is difficult but important. A nomogram was developed previously for determining the risk of 3-year probable AD conversion in aMCI. Objective: To compare the probable AD conversion rates with cognitive and neurodegenerative changes for 2 years from high- and low risk aMCI groups classified using the nomogram. Methods: This prospective, multicenter, observational study was conducted in Korea. A total of patients were classified as high- or low-risk aMCI according to the nomogram and followed-up for 2 years to compare the annual conversion rate to probable AD and brain structure changes between the two groups. Results: In total, 176 (high-risk, 85; low-risk, 91) and 160 (high-risk, 77; low-risk, 83) patients completed the 1-year and 2-year follow-up, respectively. The probable AD conversion rate was significantly higher in the high-risk (Year 1, 28.9%; Year 2, 46.1%) versus low-risk group (Year 1, 0.0%; Year 2, 4.9%, both p < 0.0001). Mean changes from baseline in Seoul Neuropsychological Screening Battery-Dementia Version, Clinical Dementia Rating-Sum of Box, and Korean version of the Instrumental Activities of Daily Living scores and cortical atrophy index at Years 1 and 2 were significantly greater in the high-risk group (p < 0.0001). Conclusions: The high-risk aMCI group, as determined by the nomogram, had a higher conversion rate to probable AD and faster cognitive decline and neurodegeneration change than the low-risk group. These real-world results have clinical implications that help clinicians in accurately predicting patient outcomes and facilitating early decision-making.Trial Registration: ClinicalTrials.gov (NCT03448445).

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.

BACKGROUND: Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. OBJECTIVE: We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. METHODS: The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. RESULTS: The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). CONCLUSIONS: The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Impact of the COVID-19 pandemic on mortality and loss to follow-up among patients with dementia receiving anti-dementia medications.

The coronavirus disease 2019 (COVID-19) pandemic has profoundly impacted vulnerable groups, such as patients with dementia. We examined changes in mortality and loss to follow-up in patients with dementia using data from the Korean National Health Insurance Service research database. Patients with dementia who visited a medical institution with a recorded dementia-related diagnostic code, including Alzheimer's disease, and who received anti-dementia medication between February 2018 and January 2020 were included in this study. We divided patients with dementia receiving anti-dementia medications into two cohorts: those newly diagnosed with dementia between February 2018 and January 2019 (n = 62,631) and those diagnosed between February 2019 and January 2020 (n = 54,494). Then, we conducted a one-year follow-up of their records, tracking the cohort diagnosed between February 2018 and January 2019 from February 2019 to January 2020, as well as the cohort diagnosed between February 2019 and January 2020 from February 2020 to January 2021. There was a significant increase in follow-up loss among patients newly diagnosed with dementia during the COVID-19 outbreak, from 42.04% in 2019 to 45.89% in 2020. Female sex, younger age, fewer comorbidities, diagnosis of dementia at the Department of Neurology or Psychiatry, and higher income were associated with decreased follow-up loss and mortality. This study highlights the importance of paying extra attention to patients with dementia receiving anti-dementia medications, particularly during pandemics, given their increased risk of loss to follow-up.

Effect of Anti-Aging Standard Forest Healing Program With Multiple Visits to a Forest Facility on Cognition in Older Age Patients.

Background and purpose: The anti-aging standard forest healing program (ASFHP), which uses forest therapy, was reported to be effective in improving psychological, physical, and cognitive functions. However, there are several challenges to directly visiting the forest. This study aimed to investigate the impact of multi-session ASFHP with forest visit on the mental and physical health of the older people with visits to forest facilities and compared them with those of the same program conducted indoors. Methods: Individuals aged over 70 years with concerns about cognitive decline were recruited at dementia relief centers and divided into control and experimental groups. A total of 33 people were administered ASFHP under the supervision of a forest therapy instructor. The control group stayed indoors, while the experimental group visited a forest healing center and repeated the program 20 weeks. Results: The multiple-session ASFHP positively affected cognitive impairment screening test (CIST) total scores (p=0.002), memory (p=0.014), Korean version of the Repeatable Battery for the Assessment of Neuropsychological Status total scores (p<0.001), immediate recall (p=0.001), visuospatial/construction (p<0.001), language (p<0.001), forest healing standard questionnaire total scores (p=0.002), and cognitive function (p=0.019), regardless of location. The forest visits during the ASFHP showed positive effects on orientation (p=0.035), delayed recall (p=0.042), emotional stability (p=0.032), physical activity (p=0.005), and health (p=0.022). The CIST scores of the memory domain were the strongest indicator of the multiple-session ASFHP effects. Conclusions: The 20-week multi-session ASFHP with forest visit showed effects on cognitive improvement and physical and emotional stability compared to indoor education.

Three Virtual Reality Environments for the Assessment of Executive Functioning Using Performance Scores and Kinematics: An Embodied and Ecological Approach to Cognition.

Virtual reality (VR) is an attractive technology for cognitive assessment, as it provides a more embodied experience compared with typical test situations, such as those using paper and pencil. In addition, VR can immerse individuals in complex situations similar to real-life ones, thereby improving the ecological validity (i.e., face validity) of the assessment. VR also offers improved scoring of tests as it facilitates the tracking of kinematic information and the temporal tracking of activities. This study assesses the correlation between scores on executive function assessments using standard neuropsychological tasks in paper-and-pencil format, on a tablet, and in three immersive VR environments, each designed to involve specific aspects of executive function. This study also aims to assess the correlation between these performance scores and a set of kinematic measures (speed, duration, and distance traveled by the hand) collected in VR. The outcomes, including performance scores and kinematic measures, correlate both with traditional assessment methods (such as paper and pencil, and computerized 2D tests) and with each other, suggesting their potential usefulness in clinical and research contexts. The discussion focuses on the advantages of embodied, situated, and spatialized tests for cognitive assessment and the benefits of kinematic tracking in VR tests for the quality of this assessment.

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study.

BACKGROUND: With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. OBJECTIVE: We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. METHODS: A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. RESULTS: In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. CONCLUSIONS: Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

Resting-state electroencephalographic characteristics related to mild cognitive impairments.

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.

Gout and the Prevalence of Dementia: A Nationwide Population-Based Study.

BACKGROUND: Hyperuricemia in patients with gout is associated with a low risk of neurodegenerative diseases, including dementia. However, the prevalence of dementia in patients with gout has not yet been reported. OBJECTIVE: To analyze the prevalence of dementia among patients diagnosed with gout by utilizing the Health Insurance and Review Assessment database, a nationwide registry of the South Korean population. METHODS: Data from the Health Insurance and Review Assessment database of patients diagnosed with gout between 2011 and 2018 were extracted. The annual prevalence of dementia according to age and sex was analyzed. We investigated whether there was an association between comorbidities and gout medication in patients with both gout and dementia and in patients with only gout. RESULTS: Between 2011 and 2018, the age-adjusted prevalence of dementia per 100,000 persons ranged from 54.0 (95% confidence interval: 47.7-60.2) to 69.9 (95% confidence interval: 65.3-74.5). Compared to previous studies, the prevalence of dementia was lower in patients with gout than in the general population. Patients with both gout and dementia were more likely to be women, have a wide range of comorbidities, and be prescribed gout-related drugs, including allopurinol, febuxostat, nonsteroidal anti-inflammatory drugs, and steroids than patients with gout without dementia. CONCLUSIONS: This study demonstrated a relatively low prevalence of dementia in patients with gout. Gout, characterized by hyperuricemia, might be associated with a reduced risk of dementia.

Residents need competence not confidence: A retrospective evaluation of the new competency education program for Korean neurology residents.

The objective of our study was to scrutinize the learning experiences of Korean neurology residents, with an emphasis on the implications of the novel competency-based curriculum implemented in 2021. We hypothesized that this revised curriculum could modulate residents' cognitive conduct, primarily the manifestation of overconfidence, in distinctive ways across different stages of training. Our investigative framework was three-fold. Initially, we began with a qualitative inquiry involving in-depth interviews with a purposively selected cohort of eight residents from four training sites. This approach facilitated comprehensive insight into their perceptions of their competence and confidence across the continuum of a four-year residency program. Subsequently, we incorporated the K-NEPA13 assessment instrument, administered to the residents and their overseeing supervisors. This stage aimed to dissect potential cognitive biases, particularly overconfidence and consistency, within the resident population. The final study involved a comprehensive survey administered to a group of 97 Korean neurology residents, allowing us to consolidate and validate our preceding findings. Our findings revealed that junior residents portrayed heightened confidence in their clinical capabilities compared to their senior peers. Intriguingly, junior residents also displayed a stronger inclination towards reevaluating their clinical judgments, a behavior we hypothesize is stimulated by the recently introduced competency-based curriculum. We identified cognitive divergence between junior and senior residents, with the latter group favoring more consistent and linear cause-and-effect reasoning, while the former demonstrated receptiveness to introspection and reconsideration. We speculate this adaptability might be engendered by the supervisor assignment protocol intrinsic to the new curriculum. Our study highlights the essentiality of incorporating cognitive behaviors when devising medical education strategies. Acknowledging and addressing these diverse cognitive biases, and instilling a spirit of adaptability, can nurture a culture that persists in continuous learning and self-reflection among trainee doctors.

Laterally gated ferroelectric field effect transistor (LG-FeFET) using α-In2Se3 for stacked in-memory computing array.

In-memory computing is an attractive alternative for handling data-intensive tasks as it employs parallel processing without the need for data transfer. Nevertheless, it necessitates a high-density memory array to effectively manage large data volumes. Here, we present a stacked ferroelectric memory array comprised of laterally gated ferroelectric field-effect transistors (LG-FeFETs). The interlocking effect of the α-In2Se3 is utilized to regulate the channel conductance. Our study examined the distinctive characteristics of the LG-FeFET, such as a notably wide memory window, effective ferroelectric switching, long retention time (over 3 × 104 seconds), and high endurance (over 105 cycles). This device is also well-suited for implementing vertically stacked structures because decreasing its height can help mitigate the challenges associated with the integration process. We devised a 3D stacked structure using the LG-FeFET and verified its feasibility by performing multiply-accumulate (MAC) operations in a two-tier stacked memory configuration.

Multi-task learning approach for utilizing temporal relations in natural language understanding tasks.

Various studies have been conducted on multi-task learning techniques in natural language understanding (NLU), which build a model capable of processing multiple tasks and providing generalized performance. Most documents written in natural languages contain time-related information. It is essential to recognize such information accurately and utilize it to understand the context and overall content of a document while performing NLU tasks. In this study, we propose a multi-task learning technique that includes a temporal relation extraction task in the training process of NLU tasks such that the trained model can utilize temporal context information from the input sentences. To utilize the characteristics of multi-task learning, an additional task that extracts temporal relations from given sentences was designed, and the multi-task model was configured to learn in combination with the existing NLU tasks on Korean and English datasets. Performance differences were analyzed by combining NLU tasks to extract temporal relations. The accuracy of the single task for temporal relation extraction is 57.8 and 45.1 for Korean and English, respectively, and improves up to 64.2 and 48.7 when combined with other NLU tasks. The experimental results confirm that extracting temporal relations can improve its performance when combined with other NLU tasks in multi-task learning, compared to dealing with it individually. Also, because of the differences in linguistic characteristics between Korean and English, there are different task combinations that positively affect extracting the temporal relations.

Prevalence of motor neuron diseases in gout patients: a nationwide population-based cohort study.

BACKGROUND: Increased uric acid may have a protective effect in motor neuron diseases (MNDs). The association between gout, characterized by hyperuricemia, and MNDs was not investigated previously. To estimate the prevalence of MNDs in gout patients using the Health Insurance and Review Assessment (HIRA) database, a nationwide database of South Korea. METHODS: The current descriptive study was conducted using the HIRA database. Subjects diagnosed with gout from 2011 to 2018 were included in this study. Among them, the annual prevalence of MNDs was analyzed, stratified by age and sex. Comorbidities including the Charlson Comorbidity Index score and type of prescribed gout-related drug were also demonstrated. RESULTS: The age-adjusted prevalence of MNDs per 105 persons ranged from 0.598 (95% confidence interval (CI): - 0.231-1.426) to 2.534 (95% CI: 1.100-3.968) between 2011 and 2018. Compared to previous reports, the prevalence of MNDs, especially amyotrophic lateral sclerosis (ALS), in gout patients was significantly lower than in the general population. None of the female gout patients were diagnosed with MNDs. Cerebrovascular accidents, vascular risk factors including hypertension, dyslipidemia, and diabetic complications, and the use of uric acid-lowering agents were more common in gout patients with MNDs than in those without MNDs. CONCLUSION: This study adds to the evidence of MND prevalence in gout patients. Gout might have a protective effect against the risk of MNDs.

Anti-N-methyl-D-aspartate receptor encephalitis after coronavirus disease 2019: A case report and literature review.

RATIONALE: Coronavirus disease 2019 (COVID-19) has become a global pandemic and COVID-19-associated anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis may occur through an immune-mediated pathomechanism. PATIENT CONCERNS: A 21-year-old woman with a history of COVID-19 presented to our hospital with memory decline and psychiatric symptoms. DIAGNOSIS: The patient was diagnosed with anti-NMDAR encephalitis. INTERVENTION: Intravenous methylprednisolone (1 g/day over 5 days) followed by immunoglobulin (0.4 g/kg/day over 5 days) were administered. The patient underwent laparoscopic salpingo-oophorectomy to remove an ovarian teratoma. OUTCOMES: The patient was discharged with sequelae of short-term memory impairment, without other neuropsychiatric symptoms. LESSONS: Cases of previously reported anti-NMDAR encephalitis with COVID-19 were reviewed and compared with the present case. Clinicians should be aware of the occurrence of anti-NMDAR encephalitis in patients who present with neuropsychiatric complaints during or after exposure to COVID-19. Further studies are required to determine the causal relationship between the 2 diseases and predict the prognosis of anti-NMDAR encephalitis after COVID-19 exposure.

A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Phase IIb Clinical Study to Evaluate the Safety and Efficacy of DHP1401 in Patients with Mild to Moderate Alzheimer's Disease Treated with Donepezil: DHP1401 Randomized Trial in Mild to Moderate Alzheimer's Disease (DRAMA).

BACKGROUND: Preclinical studies in transgenic models of Alzheimer's disease (AD) suggest that DHP1401 has neuroprotective and memory-enhancing effects. OBJECTIVE: To evaluate the efficacy and safety of DHP1401 in AD patients treated with donepezilMethods:Methods: In a double-blind study, patients with mild-to-moderate AD were randomized (1:1:1) to receive a twice daily total dose of 500 mg or 1000 mg DHP1401 or placebo for 24 weeks. Tolerability and safety were monitored at baseline and weeks 12 and 24. RESULTS: total of 180 patients were randomized to Active 1 (500 mg: n = 62), Active 2 (1000 mg: n = 53), and control groups (n = 65) in 16 sites in Korea. There was no significant difference in the Alzheimer's Disease Assessment Scale (ADAS-cog) score, the primary efficacy endpoint, from baseline. However, in the subgroup with mild AD patients (MMSE, 20-26) who received the high dose of DHP1401 and the group that received donepezil 5 mg, the ADAS-cog scores improved. MMSE and K-TMT-e type B were significant in both active groups at week 24. The most frequently observed symptom was dizziness (2.78%), and the most commonly observed reactions were related to metabolism and nutrition disorders (5.00%). No remarkable adverse events were observed for 24 weeks. CONCLUSION: Although the effectiveness of DHP1401 was not proved to be superior as the primary efficacy endpoint, the secondary endpoints, MMSE and K-TMT-e type B, showed significant beneficial effects. Also, the subgroups showed that ADAS-cog scores significantly were improved. DHP1401 could be proven beneficial for the AD treatment by further clinical trials.

Annual Incidence of Dementia from 2003 to 2018 in Metropolitan Seoul, Korea: A Population-Based Study.

National dementia plans were applied in dementia support centers established in Seoul, Korea between 2007 and 2009. However, the annual incidence rates of dementia in Seoul have not been reported. We investigated this annual incidence and the characteristics of incident cases from 2003 to 2018. The customized research database of the Korean National Health Insurance Services was used. The annual crude and age-standardized incidence of dementia patients and their characteristics were analyzed. This study analyzed 108,596 incident dementia cases aged ≥60 years. The incidence rate increased from 2003 to 2011, including a rapid increment from 2007 to 2011. From 2011 to 2018, the crude (age-standardized) incidence per 105 person-years decreased from 641.51 (577.12) to 448.26 (361.23). The proportion of incident dementia cases was highest in the highest income group every year. However, the proportion of incident dementia cases in the lowest income group increased from 10.4% in 2003 to 25.8% in 2011. The annual incidence rate of dementia showed a sharp increase immediately after 2007, the year dementia support centers began to be introduced, and then stabilized after 2011. The characteristics of incident dementia cases have changed, including the proportion in the low-income group.

Five-Year Community Management Rate for Dementia Patients: A Proposed Indicator for Dementia Policies.

BACKGROUND AND PURPOSE: There is no specific indicator for monitoring dementia management. We propose an auxiliary indicator called the community management rate, defined as the proportion of dementia patients who receive informal care from close caregivers or themselves within their community population. The 5-year community management rate is the percentage of dementia patients who are receiving community management at 5 years after they were diagnosed. The aim of this study was to identify how the community management rate has changed over time and how the 5-year community management rate differs according to age, sex, income, residence area, and comorbidities. METHODS: We analyzed customized research database of the Korean National Health Insurance Services from 2003 to 2018. The 5-year community management rate was calculated annually with newly diagnosed dementia patients, and compared among subgroups according to age, sex, income, residence area, and comorbidities. RESULTS: This study analyzed 549,297 patients. Among those newly diagnosed with dementia in 2003, the mean duration of community management during the 15-year follow-up was 5.98 years. The community management rate decreased rapidly from 2003 to 2006, after which it increased. A low 5-year community management rate was associated with older age, higher comorbidity burden, nonmetropolitan residence, and low income. CONCLUSIONS: The community management rate seems to reflect diverse patient factors. Efforts are needed to reduce the comorbidity burden and differences in the 5-year community management rate according to residence area and income. This study indicates the need for further investigations into the use of this indicator to monitor the management of dementia patients.

Examining the Academic Trends in Neuropsychological Tests for Executive Functions Using Virtual Reality: Systematic Literature Review.

BACKGROUND: In neuropsychology, fully immersive virtual reality (VR) has been spotlighted as a promising tool. It is considered that VR not only overcomes the existing limitation of neuropsychological tests but is also appropriate for treating executive functions (EFs) within activities of daily living (ADL) due to its high ecological validity. While fully immersive VR offers new possibilities of neuropsychological tests, there are few studies that overview the intellectual landscape and academic trends in the research related to mainly targeted EFs with fully immersive VR. OBJECTIVE: The objective of this study is to get an overview of the research trends that use VR in neuropsychological tests and to analyze the research trends using fully immersive VR neuropsychological tests with experimental articles. METHODS: This review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were searched in three web databases using keywords related to VR, EFs, and cognitive abilities. The study was conducted in two steps, keyword analysis and in-depth systematic review. In the web database search from 2000 to 2019, 1167 articles were initially collected, of which 234 articles in the eligibility phase were used to conduct keyword analysis and a total of 47 articles were included for systematic review. RESULTS: In keyword analysis, the number of articles focused on dementia including the keywords "MCI," "SCD," and "dementia" were highlighted over the period, rather than other symptoms. In addition, we identified that the use of behavioral and physiological data in virtual environments (VEs) has dramatically increased in recent studies. In the systematic review, we focused on the purpose of study, assessment, treatment, and validation of usability and structure. We found that treatment studies and uncategorized studies including presence and cybersickness issues have emerged in the recent period. In addition, the target symptoms and range of participants were diversified. CONCLUSIONS: There has been a continuously increasing interest in dealing with neuropsychology by using fully immersive VR. Target cognitive abilities have been diversified, as well as target symptoms. Moreover, the concept of embodied cognition was transplanted in this research area.