• 대한의생명과학회지
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• 제25권1호
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• pp.7-14
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• 2019

Alzheimer's disease (AD) is highly prevalent in dementia, with no specifically effective treatment having yet been discovered. Amyloid plaques are one of the key hallmarks of AD. Transgenic mouse models exhibiting Alzheimer's disease-like pathology have been widely used to study the pathophysiology of Alzheimer's disease. In this study, we showed an age-dependent correlation between cognitive function, pathological findings, and [F-18] Florbetaben (FBB) PET images. Nineteen transgenic mice (12 with AD, 7 with controls) were used for this study. We observed an increase in ${\beta}$-Amyloid deposition ($A{\beta}$) in brain tissue and [F-18] FBB amyloid PET imaging in the AD group. The [F-18] FBB data showed a mildly negative trend with cognitive function. Pathological findings were negatively correlated with cognitive functions. These finding suggests that amyloid beta deposition can be well-monitored with [F-18] FBB PET and a decline in cognitive function is related to the increase in amyloid plaque burden.

• 대한의생명과학회지
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• 제25권1호
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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.

• 대한의생명과학회지
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• 제24권4호
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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.

• 핵의학기술
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• 제26권1호
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• pp.33-37
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• 2022

¹⁸F-FBB 판독은 회백질과 백질의 신호강도를 육안으로 비교하여 이루어진다. 정량화된 영상분석을 판독과 비교하여 영상분석의 유용성을 평가하고자 한다. 환자는 판독결과를 기준으로 음성과 양성을 100명씩 나누었고 FBB 300 MBq 주입하고 90분 뒤 20분간 촬영했다. 장비는 Discovery 600 (GE Healthcare, MI, USA)을 사용하였다. 제조사에서 제공하는 아밀로이드 판독 기준을 근거하여 4개의 관심영역을 설정하였다. 영상분석은 각 SUVmean을 소뇌로 나누어 SUVr를 산출하고 전체 영역에서의 평균 SUVr로 진행하였다. 통계분석은 ROC Curve를 통한 Cutoff 도출과 독립표본 t-test의 그룹간 차이, 그리고 Kappa test를 통한 판독결과와 일치도를 분석하였다. 전체 영역에서의 평균 SUVr의 Cutoff는 1.23으로 나왔다. Cutoff를 사용한 판독결과와 일치도는 음성에서 95/100 (95 %), 양성에서 92/100 (92 %)로 나왔다. t-test 결과 그룹 간에 통계적으로 유의한 차이가 있었고(P < 0.05) Kappa 통계 결과 0.867로 높은 일치도를 나타냈다(P < 0.05). 영상분석의 결과가 통계적으로 유의하며 판독결과에도 높은 일치도를 보여 주었다. 추가적으로 FBB 영상분석은 아밀로이드가 축적된 부위를 3D 매핑하여 볼 수 있고 위치추정이 가능하며 정량분석 결과를 세분화하여 볼 수 있다. 정량화된 FBB 영상분석을 보조지표로 활용한다면 판독에 도움이 될 것으로 사료된다.

• 대한치매학회지
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• 제22권2호
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• pp.61-68
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• 2023

Background and Purpose: Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer's patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods: A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results: The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions: Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

• 대한치매학회지
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• 제21권4호
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• pp.126-137
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• 2022

Background and Purpose: Early detection of subjective cognitive decline (SCD) due to Alzheimer's disease (AD) is important for clinical research and effective prevention and management. This study examined if quantitative electroencephalography (qEEG) could be used for early detection of AD in SCD. Methods: Participants with SCD from 6 dementia clinics in Korea were enrolled. ¹⁸F-florbetaben brain amyloid positron emission tomography (PET) was conducted for all the participants. qEEG was performed to measure power spectrum and source cortical activity. Results: The present study included 95 participants aged over 65 years, including 26 amyloid PET (+) and 69 amyloid PET (-). In participants with amyloid PET (+), relative power at delta band was higher in frontal (p=0.025), parietal (p=0.005), and occipital (p=0.022) areas even after adjusting for age, sex, and education. Source activities of alpha 1 band were significantly decreased in the bilateral fusiform and inferior temporal areas, whereas those of delta band were increased in the bilateral cuneus, pericalcarine, lingual, lateral occipital, precuneus, posterior cingulate, and isthmus areas. There were increased connections between bilateral precuneus areas but decreased connections between left rostral middle frontal area and bilateral frontal poles at delta band in participants with amyloid PET (+) showed. At alpha 1 band, there were decreased connections between bilateral entorhinal areas after adjusting for covariates. Conclusions: SCD participants with amyloid PET (+) showed increased delta and decreased alpha 1 activity. qEEG is a potential means for predicting amyloid pathology in SCD. Further longitudinal studies are needed to confirm these findings.

• 수면정신생리
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• 제28권2호
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• pp.78-85
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• 2021

목 적 : 노년기 우울증 환자에서 우울증상이 알츠하이머병의 전구 증상으로 나타났는지를 감별하는 것은 중요한 임상적 과제이다. 본 연구에서는 정량화 뇌파(quantitative EEG) 지표가 노년기 우울증 환자의 알츠하이머병 병리를 예측할 수 있는 바이오마커로 기능할 수 있는지 확인하고자 하였다. 방 법 : 치매로 진단 받지 않은 55세 이상의 우울증 환자 63명이 본 연구에 포함되었다(여성 76.2%; 평균 연령 ± 표준편차 73.7 ± 6.87세). 연구 대상자들은 [¹⁸F] florbetabenPET 결과에 따라 아밀로이드 양성(Aβ+, n = 32)과 음성으로(Aβ-, n = 31) 분류하였다. 뇌파는 7분 간의 눈을 감은 상태(eye-closed, EC)와 3분 간의 눈을 뜬 상태(eye-open, EO)로 촬영하였으며, 푸리에 변환(Fourier transform)을 이용하여 스펙트럼 분석을 시행하였다. 선행연구 결과에 따라 안구 개폐 알파파 반응성 지표(EC-to-EO alpha reactivity index)가 노년기 우울증 환자의 아밀로이드 침착을 예측할 수 있는 신경생리학적 마커가 될 수 있는지 검증하였다. 알파 밴드 파워에서 아밀로이드 침착 여부(Aβ+ vs. Aβ-), 안구 개폐 조건(EC vs. EO), 지형학적 요인(laterality, polarity) 간의 상호작용을 확인하고 사후 분석을 시행하였다. 결 과 : Aβ+군과 Aβ-군에서 각 주파수 밴드의 평균 파워 스펙트럼 밀도 중 EO phase의 알파 밴드 파워에서만 유의미한 차이가 관찰되었다(F = 6.258, p = 0.015). 알파 밴드에서의 Group (Aβ+ vs. Aβ-) × Condition (EC vs. EO) × Laterality (Left, midline, or right) 3-way interaction이 연령, 성별, 교육 연수, 전반적 인지 기능, 약물 사용, MRI상 백질 고신호강도를 보정한 뒤에도 유의하였다(F = 3.720, p = 0.030). 하지만 대뇌 관심영역 별로 아밀로이드 침착에 따른 알파파 반응성을 비교한 사후 분석에서는 유의한 수준의 차이가 관찰되지 않았다. 결 론 : 노년기 우울증 환자에서 EO phase의 알파 밴드 파워 증가가 대뇌 아밀로이드 침착과 관련이 있었다. 하지만 본 연구에서 검증하고자 했던 안구개폐 알파파 반응성 지표는 알츠하이머병 병리를 예측하지는 못했다. 보다 많은 대상자를 포함한 추후 연구로 해당 결과를 재검증할 필요가 있다.