• 음성과학
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• 제15권2호
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• pp.111-118
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• 2008

We identified the characteristic impairmants of linguistic semantic memory in patients with prodromal Alzheimer's disease(AD) and mild AD. To elucidate the earliest changes of semantic language function in subjects with AD, performances on confrontation naming test and verbal fluency task were compared among patients with AD patients (n=20), mild AD patients (n=27) and healthy elderly controls (n=20). Tasks in this study included the confrontation naming test of Test of Lexical Processing in Aphasia(TLPA/Japanese) and one-minute verbal fluency task (semantic/ phonetic categories). The results were as follows: 1) Performances of the prodromal AD group showed the comparable to those of the control group on the confrontation naming test, 2) In the semantic/phonetic verbal fluency tasks, the performances of the control group were better than those of the prodromal AD and mild AD groups, but no significant differences were shown between the prodromal AD and the mild AD group.

• 성인간호학회지
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• 제24권4호
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• pp.398-405
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• 2012

Purpose: The purpose of this study was to develop spaced retrieval training as a nursing intervention for patients having an mild alzheimer's disease and to determine the effects of the program on their memory and cognitive function across training sessions. Methods: A non-equivalent control group pre test-post test design was used in this study. Participants were recruited from a local community: 14 patients were allocated into experimental group and 12 patients were allocated into control group. The experimental group was asked to participate in spaced retrieval training over 4 weeks, with seven times a week and 1 hour a session based. The study was conducted from June 20, 2011 to July 17, 2011. Data was analyzed with descriptive statistics, $x^2$-test and t-test using the SPSS/WIN 19.0 program. Results: After spaced retrieval training, the experimental group showed significant increases in scores for memory (t=12.40, p<.001) and cognitive function (t=7.69, p<.001) in comparison to the control group. Conclusion: Spaced retrieval training was effective in increasing cognitive function and memory of patients having mild alzheimer's disease. Therefore spaced retrieval training could be benefit the mild alzheimer's disease.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.1403-1404
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• 2010

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• 정신신체의학
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• 제26권2호
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• pp.119-126
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• 2018

연구목적 본 연구의 목적은 알츠하이머병 및 경도인지장애 환자에서 백질고강도신호 차이에 따른 신경인지 기능 및 행동심리증상(BPSD)을 비교하는 것이다. 방 법 본 연구는 후향적 연구로서, 알츠하이머병 및 경도인지장애로 진단받은 115명을 대상으로 하였다. 뇌 자기공명영상의 백질 고강도 신호(white matter hyperintensity, WMH)는 표준화된 시각 기반 척도(Fazekas scales)에 의해 평가되었으며, 참가자들은 Fazekas 척도에 따라 두 그룹으로 분류되었다. 신경인지기능은 임상치매평가척도(CERAD-K)에 의해, BPSD는 한국형 신경정신행동검사(K-NPI)로 평가되었다. WMH의 심각도에 따른 신경인지기능 및 BPSD의 차이를 분석하기 위해 독립표본 t-test를 시행하였다. 결 과 WMH의 중증도가 높은 군은 유의하게 낮은 언어 유창성을 나타내었다(p<0.05). 또한 WMH의 중증도가 높은 군은 유의하게 높은 K-NPI 점수를 보였다(p<0.01). 결 론 WMH와 실행기능과 관련된 신경인지검사 간에는 유의한 연관이 있었다. 또한 WMH는 BPSD의 중증도에 영향을 미치는 것으로 보인다. 임상실제에서 WMH를 알츠하이머 병(Alzheimer's disease, AD)및 경도인지장애(Mild cognitive impairment, MCI)환자를 치료하는데 유용한 정보를 제공할 것이다.

• 대한치매학회지
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• 제22권4호
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• pp.158-168
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• 2023

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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3583-3597
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• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

• 말소리와 음성과학
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• 제5권1호
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• pp.27-38
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• 2013

Healthcare providers in Korea are using conservative pharmacological treatment for Alzheimer's disease (AD) to delay the progress of the disease or to mitigate its behavioral and neurological symptoms. However, there is a growing need for interventions using practical non-pharmacologic treatment, as the effects of pharmacological treatments has faced limitations. This research provided a cognitive rehabilitation program to 3 AD patients and used a multiple baseline design across subjects to examine the effects. Performing reality orientation therapy (ROT) for 1 cycle (4 weeks) resulted in a slight increase in accuracy and responsiveness on an orientation task, mainly with patients with mild cases of AD. Also, in the sub-domain of the Korean-Mini Mental Status Examination performed to examine changes in cognitive ability, there were minimal changes in place orientation. In functional communication, however, there were no significant differences before and after the intervention. In conclusion, we found that ROT was an effective intervention for improving accuracy and responsiveness in the orientation of patients with mild cases of AD. In future studies, the effect of non-pharmacological interventions can be evaluated more reliably by examining the interaction effects of sample size, length of the intervention, outcome measurements, and pharmacological intervention.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• 대한의용생체공학회:의공학회지
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• 제35권3호
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• pp.68-74
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• 2014

Recent research on mild cognitive impairment (MCI) has shown that cognitive and memory decline in this disease is accompanied by disruptive changes in the brain functional network. However, there have been no graph-theoretical studies using $^{11}C$-PIB PET data of the Alzheimer's Disease or mild cognitive impairment. In this study, we acquired $^{18}F$-FDG PET and $^{11}C$-PIB PET images of twenty-four normal aging control participants and thirty individuals with MCI from ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Brain networks were constructed by thresholding binary correlation matrices using graph theoretical approaches. Both normal control and MCI group showed small-world property in $^{11}C$-PIB PET images as well as $^{18}F$-FDG PET images. $^{11}C$-PIB PET images showed significant difference between NC (normal control) and MCI over large range of sparsity values. This result will enable us to further analyze the brain using established graph-theoretical approaches for $^{11}C$-PIB PET images.