Background: and Purpose: Neurofilament light chain (NfL) has been considered as a biomarker for neurodegenerative diseases including Alzheimer’s disease (AD). We measured plasma NfL levels in older adults with cognitive complaints and evaluated their clinical usefulness in AD. Methods: Plasma levels of NfL, measured by using the single molecule array method, were acquired in a total of 113 subjects consisting of subjective cognitive decline (SCD; n=14), mild cognitive impairment (MCI; n=37), or dementia of Alzheimer type (DAT; n=62). Plasma NfL level was compared among three groups, and its association with cognitive and functional status was also analyzed. Results: After adjusting for age, plasma NfL level was higher in subjects with DAT (65.98±84.96 pg/mL), compared to in subjects with SCD (16.90±2.54 pg/mL) or MCI (25.53±10.42 pg/mL, p=0.004). NfL levels were correlated with scores of the mini-mental state examination (r=−0.242, p=0.021), clinical dementia rating (CDR) (r=0.291, p=0.005), or CDR-sum of boxes (r=0.276, p=0.008). Just for participants who performed amyloid positron emission tomography (PET), the levels were different between subjects with PET (−) (n=17, 25.95±13.25 pg/mL) and PET (+) (n=16, 63.65±81.90 pg/mL, p=0.010). Additionally, plasma NfL levels were different between vascular dementia and vascular MCI, and between Parkinson’s disease- dementia and no dementia. Conclusions This pilot study shows that in subjects with DAT, plasma NfL levels increase.Plasma NfL level correlated with cognitive and functional status. Further longitudinal studies may help to apply the plasma NfL levels to AD, as a potential biomarker for the diagnosis and predicting progression.

Background: and Purpose: Facial emotion recognition deficits impact the daily life, particularly of Alzheimer’s disease patients. We aimed to assess these deficits in the following three groups: subjective cognitive decline (SCD), mild cognitive impairment (MCI), and mild Alzheimer’s dementia (AD). Additionally, we explored the associations between facial emotion recognition and cognitive performance. Methods: We used the Korean version of the Florida Facial Affect Battery (K-FAB) in 72 SCD, 76 MCI, and 76 mild AD subjects. The comparison was conducted using the analysis of covariance (ANCOVA), with adjustments being made for age and sex. The Mini-Mental State Examination (MMSE) was utilized to gauge the overall cognitive status, while the Seoul Neuropsychological Screening Battery (SNSB) was employed to evaluate the performance in the following five cognitive domains: attention, language, visuospatial abilities, memory, and frontal executive functions. Results: The ANCOVA results showed significant differences in K-FAB subtests 3, 4, and 5 (p=0.001, p=0.003, and p=0.004, respectively), especially for anger and fearful emotions. Recognition of ‘anger’ in the FAB subtest 5 declined from SCD to MCI to mild AD. Correlations were observed with age and education, and after controlling for these factors, MMSE and frontal executive function were associated with FAB tests, particularly in the FAB subtest 5 (r=0.507, p<0.001 and r=−0.288, p=0.026, respectively). Conclusions Emotion recognition deficits worsened from SCD to MCI to mild AD, especially for negative emotions. Complex tasks, such as matching, selection, and naming, showed greater deficits, with a connection to cognitive impairment, especially frontal executive dysfunction.

Objective: This study introduces a novel home-based dual-task platform incorporating augmented reality (AR), COGNIMO, aimed at simultaneously enhancing cognition and physical abilities. The purpose of this study was to assess the effectiveness of this intervention in enhancing cognitive and physical abilities in elderly individuals with subjective cognitive decline, mild cognitive impairment (MCI), and mild Alzheimer’s dementia. Methods: A 12-week observational study enrolled 57 participants aged 60–85 years. Primary outcomes included changes in cognitive scores (Korean Mini-Mental State Examination, 2nd edition [K-MMSE-2] and Korean-Montreal Cognitive Assessment [K-MoCA]), while secondary outcomes measured physical parameters and depression scores between baseline and week 12 in the active and the control groups. Results: Of 57 participants, 49 completed the study. The active group (≥12 sessions) exhibited significant improvement in K-MoCA compared to the control group (<12 sessions) (p=0.004), while K-MMSE-2 score changes showed no significant difference (p=0.579). Positive correlations between training sessions and K-MoCA changes were observed (r=0.31, p=0.038), emphasizing a dose-response relationship. Subgroup analyses revealed a distinction in cognitive changes, particularly in the MCI group. Conclusion The COGNIMO platform showed positive effects on cognitive function in MCI patients, suggesting potential benefits for this population. The study highlights the potential of AR-integrated home-based interventions for cognitive enhancement in elderly individuals, underlining the need for further trials in the future.

Objective: This study introduces a novel home-based dual-task platform incorporating augmented reality (AR), COGNIMO, aimed at simultaneously enhancing cognition and physical abilities. The purpose of this study was to assess the effectiveness of this intervention in enhancing cognitive and physical abilities in elderly individuals with subjective cognitive decline, mild cognitive impairment (MCI), and mild Alzheimer’s dementia. Methods: A 12-week observational study enrolled 57 participants aged 60–85 years. Primary outcomes included changes in cognitive scores (Korean Mini-Mental State Examination, 2nd edition [K-MMSE-2] and Korean-Montreal Cognitive Assessment [K-MoCA]), while secondary outcomes measured physical parameters and depression scores between baseline and week 12 in the active and the control groups. Results: Of 57 participants, 49 completed the study. The active group (≥12 sessions) exhibited significant improvement in K-MoCA compared to the control group (<12 sessions) (p=0.004), while K-MMSE-2 score changes showed no significant difference (p=0.579). Positive correlations between training sessions and K-MoCA changes were observed (r=0.31, p=0.038), emphasizing a dose-response relationship. Subgroup analyses revealed a distinction in cognitive changes, particularly in the MCI group. Conclusion The COGNIMO platform showed positive effects on cognitive function in MCI patients, suggesting potential benefits for this population. The study highlights the potential of AR-integrated home-based interventions for cognitive enhancement in elderly individuals, underlining the need for further trials in the future.

Objective: This study introduces a novel home-based dual-task platform incorporating augmented reality (AR), COGNIMO, aimed at simultaneously enhancing cognition and physical abilities. The purpose of this study was to assess the effectiveness of this intervention in enhancing cognitive and physical abilities in elderly individuals with subjective cognitive decline, mild cognitive impairment (MCI), and mild Alzheimer’s dementia. Methods: A 12-week observational study enrolled 57 participants aged 60–85 years. Primary outcomes included changes in cognitive scores (Korean Mini-Mental State Examination, 2nd edition [K-MMSE-2] and Korean-Montreal Cognitive Assessment [K-MoCA]), while secondary outcomes measured physical parameters and depression scores between baseline and week 12 in the active and the control groups. Results: Of 57 participants, 49 completed the study. The active group (≥12 sessions) exhibited significant improvement in K-MoCA compared to the control group (<12 sessions) (p=0.004), while K-MMSE-2 score changes showed no significant difference (p=0.579). Positive correlations between training sessions and K-MoCA changes were observed (r=0.31, p=0.038), emphasizing a dose-response relationship. Subgroup analyses revealed a distinction in cognitive changes, particularly in the MCI group. Conclusion The COGNIMO platform showed positive effects on cognitive function in MCI patients, suggesting potential benefits for this population. The study highlights the potential of AR-integrated home-based interventions for cognitive enhancement in elderly individuals, underlining the need for further trials in the future.

Objective: This study introduces a novel home-based dual-task platform incorporating augmented reality (AR), COGNIMO, aimed at simultaneously enhancing cognition and physical abilities. The purpose of this study was to assess the effectiveness of this intervention in enhancing cognitive and physical abilities in elderly individuals with subjective cognitive decline, mild cognitive impairment (MCI), and mild Alzheimer’s dementia. Methods: A 12-week observational study enrolled 57 participants aged 60–85 years. Primary outcomes included changes in cognitive scores (Korean Mini-Mental State Examination, 2nd edition [K-MMSE-2] and Korean-Montreal Cognitive Assessment [K-MoCA]), while secondary outcomes measured physical parameters and depression scores between baseline and week 12 in the active and the control groups. Results: Of 57 participants, 49 completed the study. The active group (≥12 sessions) exhibited significant improvement in K-MoCA compared to the control group (<12 sessions) (p=0.004), while K-MMSE-2 score changes showed no significant difference (p=0.579). Positive correlations between training sessions and K-MoCA changes were observed (r=0.31, p=0.038), emphasizing a dose-response relationship. Subgroup analyses revealed a distinction in cognitive changes, particularly in the MCI group. Conclusion The COGNIMO platform showed positive effects on cognitive function in MCI patients, suggesting potential benefits for this population. The study highlights the potential of AR-integrated home-based interventions for cognitive enhancement in elderly individuals, underlining the need for further trials in the future.

BACKGROUND: White matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) have been considered as a reliable biomarker of small vessel damages. To evaluate the severity of WMHs, it is vital to develop reliable methods to measure the volume of WMHs. We applied open source software to measure WMH volume in the semi-automated way, and tested the reliability and validity by comparing with the commonly used qualitative rating scale. METHODS: Twenty five subjects with variable WMHs were recruited. ANALYZE 10.0 was used for the image processing and volumetric measurement of WMHs. The inhomogeneity and artifacts of signal were corrected with Insight Segmentation and Registration Toolkit in ANALYZE. For the gold standard of the WMH volumetric measurement, threshold method was applied with consensus of manual editing on each slice of the MRI images by two raters. Histogram of the all slices of the Fluid Attenuated Inversion Recovery (FLAIR) MRI was generated to calculate the optimal voxel intensity of threshold, and the lowest voxel threshold was decided as the mean+1.4 SD. The volumes of WMHs were generated by multiplying the area and the thickness of each slice. Inter- and intrarater reliability of the semi-automated volumetric and Scheltens'methods, and the association between the individual methods were analyzed. RESULTS: The semi-automated WMH volume at the threshold of 1.4 SD as well as the gold standard volume was well correlated with the Scheltens' visual scale (r=0.75, p<0.001). The semi-automated volumetry showed the excellent intra-rater (ICC=0.9929; 95% CI, 0.9840-0.9968) and inter-rater reliability (ICC=0.9830; 95% CI, 0.9620-0.9925), superior to the Scheltens' visual rating scale. CONCLUSIONS: The semi-automated volume measurement of the WMHs with Analyze was a valid and a reliable method to quantify subcortical white matter damages of various etiologies.
Artifacts ; Consensus ; Glycosaminoglycans ; Humans ; Magnetic Resonance Imaging ; Reproducibility of Results

BACKGROUND AND PURPOSE: The "closing-in" phenomenon refers to the tendency to copy near or overlap a model while performing figure-copying tasks. The mechanisms underlying the closing-in phenomenon have not been fully elucidated, and previous studies only investigated the mechanisms through neuropsychological tests. We investigated the neuroanatomical correlates of the closing-in phenomenon using voxel-based morphometry (VBM). METHODS: Thirty-eight patients diagnosed with probable Alzheimer's disease (AD) and 21 normal controls were included. All subjects underwent neuropsychological testing to diagnose dementia and magnetization prepared rapid acquisition gradient echo brain magnetic resonance imaging for the voxel-based statistical analysis. The subjects were asked to copy the modified Luria's alternating squares and triangles to quantify the closing-in phenomenon. We applied SPM8 for the VBM analysis to detect gray matter loss associated with the closing-in phenomenon. RESULTS: The patients with probable AD showed a higher closing-in score than that of the normal control subjects (p<0.0001). The VBM analysis revealed more parietal and temporal atrophy in the patients with AD than that in the normal control group. Moreover, atrophy of the orbito-frontal area was associated with the closing-in phenomenon. CONCLUSIONS: The closing-in phenomenon is dysfunction while performing figure-copying tasks and is more common in patients with AD. The analysis of the orbito-frontal area, which is associated with inhibiting primitive reflexes, revealed that the closing-in phenomenon is an imitation behavior commonly observed in patients with frontal lobe damage.
Alzheimer Disease ; Atrophy ; Brain ; Dementia ; Frontal Lobe ; Humans ; Magnetic Resonance Imaging ; Neuropsychological Tests ; Rabeprazole ; Reflex

OBJECTIVE: Comprehensive neuropsychological tests are important in the diagnosis and follow-up of patients with MCI; however, most were developed without consideration of illiteracy. We developed the Literacy Independent Cognitive Assessment (LICA) as a comprehensive neuropsychological assessment battery applicable to older adults who are either literate or illiterate. This study aimed to assess the reliability and validity of the LICA for diagnosis of MCI. METHODS: Normal controls (n=634) and patients with MCI (n=128) were recruited from 13 centers were included in this study. Participants were divided into illiterate or literate groups, based on their performance on a brief reading and writing test. The LICA, Korean Mini-Mental State Examination (K-MMSE), and Seoul Neuropsychological Screening Battery (SNSB) were administered. RESULTS: Total LICA scores distinguished MCI patients from controls (p<0.001). They were closely and positively correlated to the K-MMSE scores (r=0.632, p<0.001) but negatively correlated to clinical dementia rating (CDR) (r=-0.358, p<0.001) and CDR sum of boxes (r=-0.339, p<0.001). Area under the receiver operating characteristic curve for patients with MCI by total LICA score was 0.827 (0.783-0.870), superior to that presented by the K-MMSE. For the classification of MCI subtypes, inter-method reliability of LICA with the SNSB was good (kappa 0.773; 0.679-0.867, p<0.001). CONCLUSION: The results of this study show that the LICA may be reliably used to distinguish MCI patients from cognitively intact adults, to identify MCI subtypes and monitor progression toward dementia, regardless of illiteracy.
Adult* ; Classification ; Dementia ; Diagnosis ; Literacy ; Follow-Up Studies ; Humans ; Mass Screening ; Mild Cognitive Impairment* ; Neuropsychological Tests ; Reproducibility of Results ; ROC Curve ; Sensitivity and Specificity ; Seoul ; Writing

Background: and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer’s disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer’s disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.