• 대한치매학회지
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• 제23권1호
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• pp.1-10
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• 2024

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.

• 한국멀티미디어학회논문지
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• 제18권2호
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• pp.120-127
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• 2015

Mild Cognitive Impairment(MCI) is a prior step to Alzheimer's Disease(AD). It is different from AD which is seriously affecting daily life. Particularly, the hippocampus could be charged a crucial function for forming memory. MCI has a high risk about progress to AD. Our investigated research for a relationship between hippocampus and AD has been studied. The measurement of hippocampus volumetric is one of the most commonly used method. The three dimensional reconstructed medical images could be passible to interpret and its examination in various aspects but the cost of brain research with the medical equipment is very high. In this study, 3D visualization was performed from a series of brain Magnetic Resonance Images(MRI) and we have designed and implemented a competitive software tool based on the open libraries of Visualization ToolKit(VTK). Consequently, our visualization software tool could be useful to various medical fields and specially prognosis and diagnosis for MCI patients.

• Korean Journal of Radiology
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• 제23권1호
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• pp.89-100
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• 2022

Objective: To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods: A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer's disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results: The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer's continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer's disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion: We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.

• 노인정신의학
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• 제16권1호
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• pp.17-23
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• 2012

The diagnosis of Alzheimer's disease (AD) is still obscure even to specialists. To improve the diagnostic accuracy, to find at-risk people as early as possible, to predict the efficacy or adverse reactions of pharmacotherapy on an individual basis, to attain more reliable results of clinical trials by recruiting better defined participants, to prove the disease-modifying ability of new candidate drugs, to establish prognosis-based therapeutic plans, and to do more, is now increasing the need for biomarkers for AD. Among AD-related biochemical markers, cerebrospinal beta-amyloid and tau have been paid the most attention since they are materials directly interfacing the brain interstitium and can be obtained through the lumbar puncture. Level of beta-amyloid is reduced whereas tau is increased in cerebrospinal fluid of AD patients relative to cognitively normal elderly people. Remarkably, such information has been found to help predict AD conversion of mild cognitive impairment. Despite inconsistent findings from previous studies, plasma beta-amyloid is thought to be increased before the disease onset, but show decreasing change as the disease progress. Regarding other peripheral biochemical markers, omics tools are being widely used not only to find useful biomarkers but also to generate novel hypotheses for AD pathogenesis and to lead new personalized future medicine.

• 대한치매학회지
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• 제23권2호
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• pp.107-114
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• 2024

Background and Purpose: Each item in the instrumental activities of daily living (IADL) questionnaire has differential importance to an individual's life functioning based on gender. However, IADL has mostly been utilized for its total score alone, without gender specificity. We identify the impact of each item on the transition from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease dementia (ADD), and determine if the impact of each item differs by gender. Methods: Subjects were aMCI or ADD with a global clinical dementia rating of 0.5 or 1. The sample size was 146 men and 154 women. We used logistic regression analysis to determine the effect of each item of IADL on the transition from aMCI to ADD. Results: The odds ratio (OR) for "remembering recent events" had similar values: 27.2 for men, and 27.7 for women. Gender difference was identified in the item with the highest OR value. For women, the "using transportation" item was 63.3, and for men, "conducting financial affairs" was overwhelmingly high at 89.1. Conclusions: Functional decline on items with relatively higher ORs may indicate higher probability of a transition from aMCI to ADD. The OR of "conducting financial affairs" was relatively higher for both genders. In terms of gender differences, "conducting home repair" for men, and "using transportation" for women, have relatively higher impact. This study demonstrates that during the transition from aMCI to ADD, each item of IADL shows a staggered decline in functioning, and that this decline is gender-specific.

• 한국노년학
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• 제32권3호
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• pp.747-758
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• 2012

본 연구는 임상 현장에서 정상 노인과 변별되는 알츠하이머성 치매 환자의 자발화 과제에서의 수행력을 알아보기 위해 실시되었다. 연구방법은 65세 이상의 정상노인 13명과 알츠하이머성 치매 환자 26명(치매 의심(questionable) 9명, 경도(mild) 치매 9명, 중도(moderate) 치매 8명)을 대상으로 대화하기 및 연속그림 설명하기 과제를 실시하였고, 전체 발화 템포, 주저시간(초단위), 조음 음운 오류, 그리고 문법적 오류를 포함한 네 가지 구어적 요소에 대해 분석, 비교하였다. 연구결과, 네 가지 요소 모두에서 집단 간 차이를 보였다. 특히, 전체 발화 템포는 정상노인과 세 치매 환자군 각각에서 유의한 차이를 보였는데, 주저시간은 중도 치매에서만, 그리고 조음 음운 오류는 경도, 중도 치매군에서만 증가되었다. 문법적 오류는 정상노인과 비교하여 치매의심군과 중도 치매군에서 유의하게 증가되었다. 상기 연구 결과를 통해, 다음과 같은 결론을 제시할 수 있다. 첫째, 정상노인과 세 치매 환자군을 가장 변별력있게 구분할 수 있는 구어적 요소는 전체 발화 템포이다. 둘째, 조음 음운능력은 치매의심군에서는 감소하지 않지만, 경도 및 중도치매군에서는 그 수행력이 떨어진다. 셋째, 문법적 오류는 중도 치매환자군에서부터 확연히 증가한다. 향후, 자발화 과제를 통해 의료기관 외의 장소에서도 치매 선별의 용이성을 더할 수 있으며, 담화 차원의 언어능력에 대한 관찰이 가능하므로 다양한 원인질환을 가진 치매 환자들의 정보를 얻을 수 있을 것으로 기대된다.

• 융합정보논문지
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• 제10권12호
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• pp.216-225
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• 2020

본 연구는 체계적 고찰을 통해 선정된 연구를 질적 분석하여 인지장애가 있는 노인에게 실시된 이중과제의 적용방법, 결과측정방법, 중재효과를 알아보고자 시행되었다. 본 연구는 2010년 1월부터 2019년 12월까지 등록된 연구를 검색하였다. 전자 데이터베이스 PubMed, ProQuest를 이용하였으며, 검색어는'dual task' OR 'multi modal' AND 'mild cognitive impairment' OR 'dementia' OR 'Alzheimer's disease' AND 'intervention' OR 'rehabilitation를 사용하였다. 최종 선정된 연구는 8편이었다. 이중과제는 단독중재로서 적용되기보다 다른 운동중재와 함께 구성된 복합중재의 부분으로 이용되고 있었다. 이중과제의 인지 및 운동과제는 각각 별개의 내용으로 서로 독립적인 과제가 대부분이었다. 평가는 MMSE, CERAD와 같은 전반적인 인지기능평가와 집행기능평가, 기억력평가 등이 포함되었고 이중과제의 직접적인 향상을 보기위하여 Dual task cost를 이용하기도 하였다. 본 연구는 이중과제의 연구 및 임상적 적용을 위한 기초적인 자료로서 이용될 수 있을 것으로 생각된다.

• Journal of Yeungnam Medical Science
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• 제38권1호
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• pp.65-69
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• 2021

Donepezil is a cholinesterase inhibitor used extensively to treat Alzheimer disease. The increased cholinergic activity is associated with adverse effects, therefore gastrointestinal symptoms, including nausea, vomiting, and diarrhea, are common. Hypokalemia is a rare adverse event that occurs in less than 1% of donepezil-treated patients. Although hypokalemia of mild and moderate grade does not present serious signs and symptoms, severe hypokalemia often results in prolonged hospitalization and mortality. Herein, we report a case of hypokalemia developed after the initiation of donepezil therapy for cognitive impairment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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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.

• Genomics & Informatics
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• 제21권3호
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.