• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.507-512
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• 2008

This study investigated the navigation ability of patients with MCI in Virtual Environments(VE) and on the visual functioning. The participants consisted of elderly adults with/without MCI. Neuropsychological tests(RCFT, BVRT, TMT, and Digit Span), the Groton Maze Learning Test(12trials), and the VE navigation learning task(6 trials) were performed. As a result, there were significant group differences for the RCFT and BVRT, but not for the GMLT. For the VE task, there was a significant difference between the MCI and normal group and no interactions between the groups and trials were found. The VE task was correlated with The RCFT, the BVRT, and the GMLT and omnibus the RCFT and the BVRT accounted for 45% of VE performances. Thus, we concluded that patients with MCI are inferior to VE navigation and visual retention/memory play a role in navigation abilities.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.587-595
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• 2015

The purpose of this study was to demonstrate exploring the field application of combined cognitive-motor learning program based on proprioceptive neuromuscular facilitation and Bartenieff Fundamental program. The combined cognitive-motor learning program(CC) was applied to the 10 MCI elder while 10 elder in occupational therapy(OT) took cognitive learning class. MMSE-K, Time up & go test(TUG), Tandem gait test(TA), GQOL-D were measured and analyzed. The collected data were analyzed by Independent & Paired T-test. The results were as follows: Both groups showed similar learning effect in MMSE-K test. However, in TA & GQOL-D test, CC group showed significant learning effect than OT group. From these result, we conclude that combined cognitive-motor learning program is valuable as alternative program for cognitive development and social development as well as physical development of MCI elder.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.115-123
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• 2012

Purpose : To evaluate white matter abnormalities on diffusion tensor imaging (DTI) in patients with mild Alzheimer disease (AD) and mild cognitive impairment (MCI), using tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM). Materials and Methods: DTI was performed in 21 patients with mild AD, in 13 with MCI and in 16 old healthy subjects. A fractional anisotropy (FA) map was generated for each participant and processed for voxel-based comparisons among the three groups using TBSS. For comparison, DTI data was processed using the VBM method, also. Results: TBSS showed that FA was significantly lower in the AD than in the old healthy group in the bilateral anterior and right posterior corona radiata, the posterior thalamic radiation, the right superior longitudinal fasciculus, the body of the corpus callosum, and the right precuneus gyrus. VBM identified additional areas of reduced FA, including both uncinates, the left parahippocampal white matter, and the right cingulum. There were no significant differences in FA between the AD and MCI groups, or between the MCI and old healthy groups. Conclusion: TBSS showed multifocal abnormalities in white matter integrity in patients with AD compared with old healthy group. VBM could detect more white matter lesions than TBSS, but with increased artifacts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.765-767
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• 2016

Since the population aging process occurs very quickly, and the population who has a dementia is also increasing significantly faster. Because there is no complete cure of dementia it is very important to detect the disease at an early stage and prevent the spread of disease through evaluation of MCI. As the assessment of MCI conducts only in the form of hand-written data, there are some limitations in using derived data. Therefore it requires a system of analysis and storage of the data able to integrate and manage the data. The research conducted in this paper is aimed to develop a system to analyze and store the MCI evaluation data.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.85-90
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• 2019

The purpose of this study was to investigate the effects of individual board game program (IBGP) on cognitive function and depression level in 7 elderly people with mild cognitive impairment(MCI). We used the mini-mental state examination korean version (MMSE-K), montreal cognitive assessment korean version (MoCA-K), and korean form of geriatric depression scale(KGDS). The results showed significant differences in MMSE-K before, after, and follow-up(p<0.05), and there were differences of orientation for time, place, and object and attention in before, after, and follow-up(p<0.05). MoCA-K showed differences in before, after, and follow-up assessments(p<0.01), and showed differences in visual construction skill, orientation, and short-term memory(p<0.05). Finally, there was a difference in depression level before, after, and follow-up of KGDS(p<0.01). Therefore, IBGP for the elderly can help improve the cognitive function, and based on this, it is expected that an advanced IBGP will be applied to improve orientation for time and place in the elderly.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.101-111
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• 2023

This study conducted systematic review and meta-analysis to analyze the effectiveness of a dual-task for cognitive function in patients with MCI in Korea. A search was conducted using eight databases, and the search terms were MCI, cognition, and dual task. This study includes RCT and nonRCT published from January 2013 to July 2023. A total of 682 studies were searched, and 8 studies that fulfilled the inclusion and exclusion criteria were finally analyzed. Methodological quality was assessed with the RoB, RoBANS. The meta-analysis used CMA 4.0 ver. As a result of the analysis, the overall effect size of the dual task was medium effect size. The effect size according to the outcome variables was large for orientation and executive function, and medium effect size for global cognitive function, visuospatial function, memory, and attention. As a result of analysis according to the intervention period, the effect was greater when applied for 4 to 8 weeks, and the effect size was larger when applied for 24 to 30 sessions. This study presented clinical evidence on the effectiveness and application method of a dual-task applied to improve cognitive function in patients with MCI.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• Animal cells and systems
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• v.12 no.1
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• pp.11-14
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• 2008

Finding a way to strengthen human cognitive functions, such as learning and memory, has been of great concern since the moment people realized that these functions can be affected and even altered by certain chemicals. Since then, plenty of endeavors have been made to look for safe ways of improving cognitive performances without adverse side-effects. Unfortunately, most of these efforts have turned out to be unsuccessful until now. In this study, we examine the effect of a natural compound, idesolide, on hippocampus-dependent recognition memory. We demonstrate that idesolide is effective in the enhancement of recognition memory, as measured by a novel object recognition task. Thus, idesolide might serve as a novel therapeutic medication for the treatment of memoryrelated brain anomalies such as mild cognitive impairment(MCI) and Alzheimer's disease.

• Therapeutic Science for Rehabilitation
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• v.12 no.2
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• pp.85-98
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• 2023

Objective : We investigated the efficacy of an artificial intelligence computerized cognitive training program using motion capture to identify changes in cognition, depression, and quality of life in older adults with mild cognitive impairment. Methods : A total of seven older adults (experimental group = 4, control group = 3) participated in this study. During the COVID-19 period from October to December 2021, we used a program, "MOOVE Brain", that we had developed. The experimental group performed the program 30 minutes 3×/week for 1 month. We analyzed patients scores from the Korean version of the Mini-Mental State Examination-2, the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet for Daily Life Evaluation, the short form Geriatric Depression Scale, and Geriatric Quality of Life Scale. Results : We observed positive changes in the mean scores of the Stroop Color Test (attention), Stroop Color/Word Test (executive function), SGDS-K (depression), and GQOL (QoL). However, these changes did not reach statistical significance for each variable. Conclusion : The study results from "MOOVE Brain" can help address cognitive and psychosocial issues in isolated patients with MCI during the COVID-19 pandemic or those unable to access in-person medical services.