딥러닝 기반 군집화 방법을 이용하여 FDG PET에서 알츠하이머병의 공간적 뇌 대사 패턴의 특징적 아형 분류

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 분자의학 및 바이오제약학과, 2022.2. 이동수.알츠하이머병은 아밀로이드와 타우 침착과 같은 병리학적 특징을 공유함에도 불구하고 광범위한 임상병리학적 특성을 보인다. 본 연구에서는 딥러닝 기반 군집화 방법을 이용하여 FDG PET 영상에서 알츠하이머병 특징적 아형을 분류하여 신경 퇴행의 공간적 뇌 대사 패턴을 이해하고자 하였으며, 공간적 뇌 대사 패턴에 의해 정의된 아형의 임상병리학적 특징을 밝히고자 하였다. Alzheimer’s Disease Neuroimaging Initiative(ADNI) 데이터베이스로부터 첫번째 방문 및 추적 방문을 포함한 알츠하이머병, 경도인지장애, 인지 정상군의 총 3620개의 FDG 뇌 양전자단층촬영(PET) 영상을 수집하였다. 알츠하이머병에서 질병의 진행 외의 뇌 대사 패턴을 나타내는 표현(representation)을 찾기 위하여, 조건부 변이형 오토인코더(conditional variational autoencoder)를 사용하였으며, 인코딩된 표현으로부터 군집화(clustering)를 시행하였다. 알츠하이머병의 뇌 FDG PET (n=838)과 CDR-SB(Clinical Demetria Rating Scale Sum of Boxes) 점수가 cVAE 모델의 입력값으로 사용되었으며, 군집화에는 k-means 알고리즘이 사용되었다. 훈련된 딥러닝 모델은 경도인지장애군 (n=1761)의 뇌 FDG PET에 전이(transfer)되어 각 아형의 서로 다른 궤적(trajectory)과 예후를 밝히고자 하였다. 통계적 파라미터 지도작성법(Statistical Parametric Mapping, SPM)을 이용하여 각 군집의 공간적 패턴을 시각화 하였으며, 각 군집의 임상적 및 생물학적 특징을 비교하였다. 또한 아형 별 경도인지장애로부터 알츠하이머병으로 전환되는 비율을 비교하였다. 딥러닝 기반 군집화 방법으로 4개의 특징적 아형이 분류되었다. (i) S1 (angular): 모이랑(angular gyrus)에서 현저한 대사 저하를 보이며 분산된 피질의 대사 저하 패턴, 남성에서 빈도 높음, 더 많은 아밀로이드 침착, 더 적은 타우 침착, 더 심한 해마 위축, 초기 단계의 인지 저하의 특징을 보였다. (ii) S2 (occipital): 후두엽(occipital) 피질에서 현저한 대사 저하를 보이며 후부 우세한 대사 저하 패턴, 더 적은 연령, 더 많은 타우, 더 적은 해마 위축, 더 낮은 집행 및 시공간 점수, 경도인지장애로부터 알츠하이머병으로의 빠른 전환의 특징을 보였다. (iii) S3(orbitofrontal): 안와전두(orbitofrontal) 피질에서 현저한 대사 저하를 보이며 전방 우세한 대사 저하 패턴, 더 높은 연령, 더 적은 아밀로이드 침착, 더 심한 해마 위축, 더 높은 집행 및 시공간 점수의 특징을 보였다. (iv) S4(minimal): 최소의 대사 저하를 보임, 여성에서 빈도 높음, 더 적은 아밀로이드 침착, 더 많은 타우 침착, 더 적은 해마 위축, 더 높은 인지기능 점수의 특징을 보였다. 결론적으로, 본 연구에서 우리는 서로 다른 뇌 병리 및 임상 특성을 가진 알츠하이머병의 특징적 아형을 분류하였다. 또한 우리 딥러닝 모델은 경도인지장애군에 성공적으로 전이되어 아형 별 경도인지장애로부터 알츠하이머병으로 전환되는 예후를 예측할 수 있었다. 본 결과는 FDG PET에서 알츠하이머병의 특징적 아형은 개인의 임상 결과에 영향을 미칠 수 있고, 병태생리학 측면에서 알츠하이머병의 광범위한 스펙트럼을 이해하는데 단서를 제공할 수 있음을 시사한다.Alzheimer’s disease (AD) presents a broad spectrum of clinicopathologic profiles, despite common pathologic features including amyloid and tau deposition. Here, we aimed to identify AD subtypes using deep learning-based clustering on FDG PET images to understand distinct spatial patterns of neurodegeneration. We also aimed to investigate clinicopathologic features of subtypes defined by spatial brain metabolism patterns. A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal controls (CN) at baseline and follow-up visits were obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. In order to identify representations of brain metabolism patterns different from disease progression in AD, a conditional variational autoencoder (cVAE) was used, followed by clustering using the encoded representations. FDG brain PET images with AD (n=838) and Clinical Demetria Rating Scale Sum of Boxes (CDR-SB) scores were used as inputs of cVAE model and the k-means algorithm was applied for the clustering. The trained deep learning model was also transferred to FDG brain PET image with MCI (n=1761) to identify differential trajectories and prognosis of subtypes. Statistical parametric maps were generated to visualize spatial patterns of clusters, and clinical and biological characteristics were compared among the clusters. The conversion rate from MCI to AD was also compared among the subtypes. Four distinct subtypes were identified by deep learning-based FDG PET clusters: (i) S1 (angular), showing prominent hypometabolism in the angular gyrus with a diffuse cortical hypometabolism pattern; frequent in males; more amyloid; less tau; more hippocampal atrophy; cognitive decline in the earlier stage. (ii) S2 (occipital), showing prominent hypometabolism in the occipital cortex with a posterior-predominant hypometabolism pattern; younger age; more tau; less hippocampal atrophy; lower executive and visuospatial scores; faster conversion from MCI to AD. (iii) S3 (orbitofrontal), showing prominent hypometabolism in the orbitofrontal cortex with an anterior-predominant hypometabolism pattern; older age; less amyloid; more hippocampal atrophy; higher executive and visuospatial scores. (iv) S4 (minimal), showing minimal hypometabolism; frequent in females; less amyloid; more tau; less hippocampal atrophy; higher cognitive scores. In conclusion, we could identify distinct subtypes in AD with different brain pathologies and clinical profiles. Also, our deep learning model was successfully transferred to MCI to predict the prognosis of subtypes for conversion from MCI to AD. Our results suggest that distinct AD subtypes on FDG PET may have implications for the individual clinical outcomes and provide a clue to understanding a broad spectrum of AD in terms of pathophysiology.1. Introduction 1 1.1 Heterogeneity of Alzheimer's disease 1 1.2 FDG PET as a biomarker of Alzheimer's disease 1 1.3 Biologic subtypes of Alzheimer's disease 2 1.4 Dimensionality reduction methods 5 1.5 Variational autoencoder for clustering 8 1.6 Final goal of the study 10 2. Methods 11 2.1 Subjects 11 2.2 FDG PET data acquisition and preprocessing 12 2.3 Deep learning-based model for representations of FDG PET in AD 12 2.4 Clustering method for AD subtypes on FDG PET 17 2.5 Transfer of deep learning-based FDG PET cluster model for MCI subtypes 17 2.6 Visualization of subtype-specific spatial brain metabolism pattern 21 2.7 Clinical and biological characterization 21 2.8 Prognosis prediction of MCI subtypes 22 2.9 Generation of subtype-specific FDG PET images 22 2.10 Statistical analysis 23 3. Results 24 3.1 Deep learning-based FDG PET clusters 24 3.2 Spatial brain metabolism pattern in AD subtypes 27 3.3 Clinical and biological characterization in AD subtypes 32 3.4 Subtype-specific spatial metabolism patterns resemble in MCI 43 3.5 Clinical and biological characterization in MCI subtypes 50 3.6 Prognosis prediction of subtypes for conversion from MCI to AD 56 3.7 Generating FDG PET images of AD subtypes 61 4. Discussion 66 4.1 Limitations of previous subtyping approach 68 4.2 Interpretation of results 68 4.3 Strength of our deep learning-based clustering approach 73 4.4 Strength of our deep learning-based AD subtypes 77 4.5 Limitations and future directions 82 5. Conclusion 83 References 84 Supplementary Figures 99 국문 초록 101박

Vascular neurocognitive disorders and the vascular risk factors

Dementias are clinical neurodegenerative diseases characterized by permanent and progressive transformation of cognitive functions such as memory, learning capacity, attention, thinking, language, passing judgments, calculation or orientation. Dementias represent a relatively frequent pathology, encountered at about 10% of the population of 65-year olds and 20% of the population of 80-year olds. This review presents the main etiological forms of dementia, which include Alzheimer form of dementia, vascular dementia, dementia associated with alpha-synucleionopathies, and mixed forms. Regarding vascular dementia, the risk factors are similar to those for an ischemic or hemorrhagic cerebrovascular accident: arterial hypertension, diabetes mellitus, dyslipidemia, smoking, obesity, age, alcohol consumption, cerebral atherosclerosis/ arteriosclerosis. Several studies show that efficient management of the vascular risk factors can prevent the expression and/ or progression of dementia. Thus, lifestyle changes such as stress reduction, regular physical exercise, decreasing dietary fat, multivitamin supplementation, adequate control of blood pressure and serum cholesterol, and social integration and mental stimulation in the elderly population are important factors in preventing or limiting the symptoms of dementia, a disease with significant individual, social, and economic implications

New insights into atypical Alzheimer's disease in the era of biomarkers

Most patients with Alzheimer's disease present with amnestic problems; however, a substantial proportion, over-represented in young-onset cases, have atypical phenotypes including predominant visual, language, executive, behavioural, or motor dysfunction. In the past, these individuals often received a late diagnosis; however, availability of CSF and PET biomarkers of Alzheimer's disease pathologies and incorporation of atypical forms of Alzheimer's disease into new diagnostic criteria increasingly allows them to be more confidently diagnosed early in their illness. This early diagnosis in turn allows patients to be offered tailored information, appropriate care and support, and individualised treatment plans. These advances will provide improved access to clinical trials, which often exclude atypical phenotypes. Research into atypical Alzheimer's disease has revealed previously unrecognised neuropathological heterogeneity across the Alzheimer's disease spectrum. Neuroimaging, genetic, biomarker, and basic science studies are providing key insights into the factors that might drive selective vulnerability of differing brain networks, with potential mechanistic implications for understanding typical late-onset Alzheimer's disease

Spatial-Temporal Patterns of Amyloid-β Accumulation: A Subtype and Stage Inference Model Analysis

BACKGROUND AND OBJECTIVES: Currently, amyloid-β (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3010 subjects were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios (SUVr) were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion (CVIC) and the most probable subtype/stage classification per scan. The effect of demographics and risk factors on subtype assignment was assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (N=1890, 62.8%), had a mean age of 68.72 (SD=9.1), 42.1% was APOE-ε4 carrier, and 51.8% was female. While a one-subtype model recovered the traditional amyloid accumulation trajectory, SuStaIn identified an optimal of three subtypes, referred to as Frontal, Parietal, and Occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to Frontal (N=415, 52.5%), followed by Parietal (N=199, 25.3%), and Occipital subtypes (N=175, 22.2%). Significant differences across subtypes included distinct proportions of APOE-ε4 carriers (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%), subjects with dementia (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%) and lower age for the Parietal subtype (Frontal/Occipital:72.1y, Parietal:69.3y). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the Frontal subtype, while Parietal and Occipital did not differ. At follow-up, most subjects (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: While a one-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that three subtypes were optimal, showing distinct associations to AD risk factors. Nonetheless, further analyses to determine clinical utility is warranted

Normal aging and Alzheimer's disease : hippocampal and episodic memory differences

Alzheimer’s Disease (AD) and normal aging (NA) are characterized by structural brain changes as well as cognitive changes that appear over the lifespan. The hippocampus is an area susceptible to early atrophy in both AD and NA; however the differential causes of atrophy are not entirely clear. Hippocampal volume loss in AD is attributed to neuronal death due to underlying pathology. AD often is diagnosed years after the onset of pathology and subsequent atrophy. NA is a continuation of cognitive decline that does not become dementia. Episodic memory (EM) is processed within the hippocampus and is one of the first systems to show deficits in conjunction with both patterns of aging. This review focuses on hippocampal volume loss and EM decline in NA and AD.Communication Sciences and Disorder

Spatial-Temporal Patterns of Amyloid-β Accumulation: A Subtype and Stage Inference Model Analysis

BACKGROUND AND OBJECTIVES: Currently, amyloid-β (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data. METHODS: Amyloid-PET data of 3010 subjects were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios (SUVr) were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion (CVIC) and the most probable subtype/stage classification per scan. The effect of demographics and risk factors on subtype assignment was assessed using multinomial logistic regression. RESULTS: Participants were mostly cognitively unimpaired (N=1890, 62.8%), had a mean age of 68.72 (SD=9.1), 42.1% was APOE-ε4 carrier, and 51.8% was female. While a one-subtype model recovered the traditional amyloid accumulation trajectory, SuStaIn identified an optimal of three subtypes, referred to as Frontal, Parietal, and Occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to Frontal (N=415, 52.5%), followed by Parietal (N=199, 25.3%), and Occipital subtypes (N=175, 22.2%). Significant differences across subtypes included distinct proportions of APOE-ε4 carriers (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%), subjects with dementia (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%) and lower age for the Parietal subtype (Frontal/Occipital:72.1y, Parietal:69.3y). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the Frontal subtype, while Parietal and Occipital did not differ. At follow-up, most subjects (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage. DISCUSSION: While a one-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that three subtypes were optimal, showing distinct associations to AD risk factors. Nonetheless, further analyses to determine clinical utility is warranted

Mild cognitive impairment: historical development and summary of research

This review article broadly traces the historical development, diagnostic criteria, clinical and neuropathological characteristics, and treatment strategies related to mild cognitive impairment (MCI), The concept of MCI is considered in the context of other terms that have been developed to characterize the elderly with varying degrees of cognitive impairment Criteria based on clinical global scale ratings, cognitive test performance, and performance on other domains of functioning are discussed. Approaches employing clinical, neuropsychological, neuroimaging, biological, and molecular genetic methodology used in the validation of MCI are considered, including results from cross-sectional, longitudinal, and postmortem investigations. Results of recent drug treatment studies of MCI and related methodological issues are also addressed