• The Korean Journal of Pain
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• v.33 no.1
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• pp.90-96
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• 2020

Background: Parkinson's disease (PD) is a neurodegenerative disorder that is the second most common disorder after Alzheimer's disease. PD includes both "motor" and "non-motor" symptoms, one of which is pain. The aim of this study was to investigate the clinical characteristics of pain in patients with PD. Methods: This cross-sectional study included 250 patients diagnosed with PD, 70% of which had mild to moderate PD (stages 2/3 of Hoehn and Yahr scale). The average age was 67.4 years, and the average duration since PD diagnosis was 7.1 years. Relevant data collected from PD patients were obtained from their personal medical history. Results: The prevalence of pain was found to be high (82%), with most patients (79.2%) relating their pain to PD. Disease duration was correlated with the frequency of intense pain (R: 0.393; P < 0.05). PD pain is most frequently perceived as an electrical current (64%), and two pain varieties were most prevalent (2.60 ± 0.63). Our findings confirm links between pain, its evolution over time, its multi-modal character, the wide variety of symptoms of PD, and the female sex. Conclusions: Our results demonstrated that the pain felt by PD patients is mainly felt as an electrical current, which contrasts with other studies where the pain is described as burning and itching. Our classification is innovative because it is based on anatomy, whereas those of other authors were based on syndromes.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• 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.

• 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.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.2
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• pp.184-190
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• 2016

Objectives : This study analyzed the differences of body mass index(BMI) in Korean patients with Alzheimer's diseases(AD), Mild Cognitive Impairment(MCI), and healthy controls to verify whether low BMI is associated with cognitive impairment. Furthermore, this study also sought to examine any association between BMI and Mini Mental State Examination-Korean version(MMSE-K), Clinical Dementia Rating(CDR), and Global Deterioration Scale(GDS). Methods : A total of 257 subjects were included in the study. History taking, mental status examination, physical examination and neurocognitive function test were carried out for the diagnosis of AD and MCI. The subjects' demographic data and presence of diseases were also surveyed. The overall cognitive function and severity of diseases were assessed using MMSE-K, GDS, and CDR. Results : The order of BMI was found to be healthy controls>MCI>AD, with statistically significant differences among the groups. The order of MMSE-K scores was similar, with healthy controls>MCI>AD in statistically significant differences. The healthy controls had the lowest CDR and GDS scores, and AD patients had the highest scores. There was a significant positive correlation between BMI and MMSE scores(r=0.238, p=0.000). BMI was negatively correlated with CDR(r=-0.174, p=0.008) as well as with GDS(r=-0.233, p= 0.000). Conclusions : Measuring BMI of patients with AD or MCI is expected to be meaningful in that BMI could be a clinical indicator of AD. We expect this to be beneficial for the diagnosis, prevention, and therapeutic approach of AD and also expect large-scale, long-term longitudinal studies to follow.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Journal of People, Plants, and Environment
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• v.23 no.1
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• pp.35-46
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• 2020

The purpose of this study was to investigate the effects of the horticultural therapy program on patients with mild cognitive impairment and mild dementia depending on the frequency and duration of the interventions. We developed the same 15-session program to improve cognitive functions and life satisfaction and alleviate depression of the elderly women with mild cognitive impairment or mild dementia. Subjects in Longer Treatment group participated in the program once a week for 15 weeks and subjects in Shorter Tratmet group participated twice a week for 7½ weeks. This study conducted pretest-posttest verification of both groups using quasi-experimental design involving 21 subjects. Elderly life satisfaction, Geriatric Depression Scale (short form), and the Korean Version of Consortium to Establish a Registry of Alzheimer's Disease (CERAD-K) were used in the evaluation. As a result, both groups showed an increase in life satisfaction, and a decrease in depression. However, there was a significant difference in the changes of the CERAD-K scores between the two groups (p < .05). In Longer Treatment group, life satisfaction increased significantly (p < .001), and depression decreased at a marginally significant level (p = .068), but no statistically significant change was observed in neurocognitive function. In Shorter Treatment group, life satisfaction increased at a marginally significant level (p = .059), and depression and CERAD-K scores decreased significantly (p < .05). However, in the case of Mini-Mental State Examination (MMSE-K), there was no significant change in both groups. According to these results, when planning a horticultural therapy program for persons with mild cognitive impairment or mild dementia, it is effective to organize and execute the program by determining the duration of intervention as 3 to 4 months or longer, even if this reduces the number of interventions per week.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.405-414
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• 2021

Objective: To compare two clinically available MR volumetry software, NeuroQuant^® (NQ) and Inbrain^® (IB), and examine the inter-method reliabilities and differences between them. Materials and Methods: This study included 172 subjects (age range, 55-88 years; mean age, 71.2 years), comprising 45 normal healthy subjects, 85 patients with mild cognitive impairment, and 42 patients with Alzheimer's disease. Magnetic resonance imaging scans were analyzed with IB and NQ. Mean differences were compared with the paired t test. Inter-method reliability was evaluated with Pearson's correlation coefficients and intraclass correlation coefficients (ICCs). Effect sizes were also obtained to document the standardized mean differences. Results: The paired t test showed significant volume differences in most regions except for the amygdala between the two methods. Nevertheless, inter-method measurements between IB and NQ showed good to excellent reliability (0.72 < r < 0.96, 0.83 < ICC < 0.98) except for the pallidum, which showed poor reliability (left: r = 0.03, ICC = 0.06; right: r = -0.05, ICC = -0.09). For the measurements of effect size, volume differences were large in most regions (0.05 < r < 6.15). The effect size was the largest in the pallidum and smallest in the cerebellum. Conclusion: Comparisons between IB and NQ showed significantly different volume measurements with large effect sizes. However, they showed good to excellent inter-method reliability in volumetric measurements for all brain regions, with the exception of the pallidum. Clinicians using these commercial software should take into consideration that different volume measurements could be obtained depending on the software used.

• Biomedical Science Letters
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• v.25 no.1
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• Biomedical Science Letters
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• v.24 no.4
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• pp.418-425
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• 2018

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.