• 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.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.67-76
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• 2021

Objectives : The aim of this study was to compare activities of daily living (ADLs) according to degenerative changes in brain [i.e., medial temporal lobe atrophy (MTA), white matter hyperintensities] and to examine the association between neurocognitive functions and ADLs in Korean patients with dementia due to Alzheimer's disease (AD) and mild cognitive impairment (MCI). Methods : Participants were 111 elderly subjects diagnosed with AD or MCI in this cross-sectional study. MTA in brain MRI was rated with standardized visual rating scales (Scheltens scale) and the subjects were divided into two groups according to Scheltens scale. ADLs was evaluated with the Korean version of Blessed Dementia Scale-Activity of daily living (BDS-ADL). Neurocognitive function was evaluated with the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease assessment packet (CERAD-K). Independent t-test was performed to compare ADLs with the degree of MTA. Pearson correlation and hierarchical multiple regression analyses were performed to analyze the relationship between ADLs and neurocognitive functions. Results : The group with high severity of the MTA showed significantly higher BDS-ADL scores (p<0.05). The BDS-ADL score showed the strongest correlation with the word list recognition test among sub-items of the CERAD-K test (r=-0.568). Findings from the hierarchical multiple regression analysis revealed that the scores of MMSE-K and word list recognition test were factors that predict ADLs (F=44.611, p<0.001). Conclusions : ADLs of AD and MCI patients had significant association with MTA. Our study, which identifies factors correlated with ADLs can provide useful information in clinical settings. Further evaluation is needed to confirm the association between certain brain structures and ADLs.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.249-255
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• 2022

Alzheimer's disease (AD) represents a major public health concern and has been identified as a research priority. Clinical research evidence supports that the core cerebrospinal fluid (CSF) biomarkers for AD, including amyloid-β (Aβ42), total tau (T-tau), and phosphorylated tau (P-tau), reflect key elements of AD pathophysiology. Nevertheless, advances in the clinical identification of new indicators will be critical not only for the discovery of sensitive, specific, and reliable biomarkers of preclinical AD pathology, but also for the development of tests that facilitate the early detection and differential diagnosis of dementia and disease progression monitoring. The early detection of AD in its presymptomatic stages would represent a great opportunity for earlier therapeutic intervention. The chance of successful treatment would be increased since interventions would be performed before extensive synaptic damage and neuronal loss would have occurred. In this study, the importance of developing an early diagnostic method using cognitive decline biomarkers that can discriminate between normal, mild cognitive impairment (MCI), and AD preclinical stages has been emphasized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.321-327
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• 2013

Impaired naming difficulty is common in normal elderly as well as in patients with neurological impairment. The 60-item Korean version-Boston Naming Test(K-BNT) is one of the most commonly used test for measuring confrontational naming ability. However, age-related cognitive decline may make the elderly difficult concentrating during the 60-item test, therefore, item reduction of the K-BNT would improve test validity and reliability. Thus, the purpose of this study was to develop a short form of the K-BNT based on Item Response Theory(IRT). Considering item-fit index, sex factor, and item difficulty through Rasch analysis, the 15-item K-BNT(i.e., K-BNT-15) was developed. Via administration of the K-BNT-15, we observed age-related decline in naming ability and significantly different performance between the normal elderly and patients with mild cognitive impairment. This study demonstrates the utility of IRT for developing a short-form language evaluation tool. The K-BNT-15 can be effective as a language screening tool to differentiate between normal aging and pathological diseases.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.377-383
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• 2023

Purpose: There is increasing attention to the application of transcranial direct current stimulation (tDCS) for enhancing cognitive functions in subjects to aging, mild cognitive impairment (MCI), and Alzheimer's disease (AD). Despite varying treatment outcomes in tDCS which depend on the amount of current reaching the brain, there is no general information on the impacts of anatomical features associated with AD on tDCS-induced electric field. Objective: The objective of this study is to examine how AD-related anatomical variation affects the tDCS-induced electric field using computational modeling. Methods: We collected 180 magnetic resonance images (MRI) of AD patients and healthy controls from a publicly available database (Alzheimer's Disease Neuroimaging Initiative; ADNI), and MRIs were divided into female-AD, male-AD, female-normal, and male-normal groups. For each group, segmented brain volumes (cerebrospinal fluid, gray matter, ventricle, rostral middle frontal (RMF), and hippocampus/amygdala complex) using MRI were measured, and tDCS-induced electric fields were simulated, targeting RMF. Results: For segmented brain volumes, significant sex differences were observed in the gray matter and RMF, and considerable disease differences were found in cerebrospinal fluid, ventricle, and hippocampus/amygdala complex. There were no differences in the tDCS-induced electric field among AD and normal groups; however, higher peak values of electric field were observed in the female group than the male group. Conclusions: Our findings demonstrated the presence of sex and disease differences in segmented brain volumes; however, this pattern differed in tDCS-induced electric field, resulting in significant sex differences only. Further studies, we will adjust the brain stimulation conditions to target the deep brain and examine the effects, because of significant differences in the ventricles and deep brain regions between AD and normal groups.

• Journal of Oriental Neuropsychiatry
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• v.34 no.4
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• pp.359-368
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• 2023

Objectives: This study aims to establish a Korean medicine doctor's range of services in the dementia relief primary care system based on the previously developed dementia clinical practice guidelines (CPGs). Developing a dementia relief primary care Clinical Pathway (CP) can aid clinically when the Korean medicine primary care doctor conducts treatment. Methods: We analyzed Dementia Korean Medicine Primary Care Model Data and then applied CP Methodology to develop the configuration of the Korean Medicine Primary Care Model. For patients with Alzheimer's dementia (AD), vascular dementia (VD), and mild cognitive impairment (MCI), the Korean Medicine Primary Care Model focuses on improving cognitive function, everyday living abilities and easing symptoms through interventions described in CPGs. The contents of the draft model later include references to already-existing CPs. Results: The study sites were chosen as Korean medical clinics connected to primary care physicians in the dementia-friendly model. The CP used a time task matrix version to arrange the clinical chronology, which included all examinations, diagnoses, and treatment procedures, from the initial appointment to follow-ups and the end of therapy. Conclusions: It anticipates that Korean primary care doctors familiar with dementia can use the offered therapies for the first time by creating the dementia Korean medicine primary care model in this study. This is expected to maximize the range of medical services provided by Korean medicine and improve the standard of medical treatment.

• Dementia and Neurocognitive Disorders
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• v.23 no.1
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• pp.22-29
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• 2024

Background and Purpose: Alzheimer's disease (AD) is a neurodegenerative disease characterized by a progressive decline in cognition and performance of daily activities. Recent studies have attempted to establish the relationship between AD and sleep. It is believed that patients with AD pathology show altered sleep characteristics years before clinical symptoms appear. This study evaluated the differences in sleep characteristics between cognitively asymptomatic patients with and without some amyloid burden. Methods: Sleep characteristics of 76 subjects aged 60 years or older who were diagnosed with subjective cognitive decline (SCD) but not mild cognitive impairment (MCI) or AD were measured using Fitbit^® Alta HR, a wristwatch-shaped wearable device. Amyloid deposition was evaluated using brain amyloid plaque load (BAPL) and global standardized uptake value ratio (SUVR) from fluorine-18 florbetaben positron emission tomography. Each component of measured sleep characteristics was analyzed for statistically significant differences between the amyloid-positive group and the amyloid-negative group. Results: Of the 76 subjects included in this study, 49 (64.5%) were female. The average age of the subjects was 70.72±6.09 years when the study started. 15 subjects were classified as amyloid-positive based on BAPL. The average global SUVR was 1.598±0.263 in the amyloid-positive group and 1.187±0.100 in the amyloid-negative group. Time spent in slow-wave sleep (SWS) was significantly lower in the amyloid-positive group (39.4±13.1 minutes) than in the amyloid-negative group (49.5±13.1 minutes) (p=0.009). Conclusions: This study showed that SWS is different between the elderly SCD population with and without amyloid positivity. How SWS affects AD pathology requires further research.