Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of in-cipientAlzheimer'sDisease(AD)dementiainmildcognitiveimpairment(MCI).Focusedonprovidinganearlierand more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over therecentyears,mostofthemlearningtheirnon-diseasepatternsfromMCIthatremainedstableover2–3years.Inthis work, we analyzed whether these stable MCI over short-term periods are actually appropriate trainingexamples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5years offollow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures pri-marily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated interms ofthe areaunder the ROCcurve(AUC), sensitivity,andspecificity inatrialsample of248 MCIpatientsfollowed-up over 5years. We further compared the sensitivity in those MCI that converted before 2years andthose that converted after 2years. Our results indicate that 23% of the stable MCI at 2years progressed in thenextthreeyearsandthatMRIvolumetricmeasuresaregoodpredictorsofconversiontoADdementiaevenatthemid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampusand entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC=73% at2yearsvs.AUC=84%at5years),aswellasforspecificity(56%vs.71%).Sensitivityshowedanon-significantslightdecrease(81%vs.78%).Remarkably,theperformanceofthismodelwascomparabletomachinelearningmodels at the same follow-up times. MRI correctly identified most of the patients that converted after 2years(with sensitivity>60%), and these patients showed a similar degree of abnormalities to those that convertedbefore 2years. This implies that most of the MCI patients that remained stable over short periods and subse-quentlyprogressedtoADdementiahadevidentatrophiesatbaseline.Therefore,machinelearningmodelsthatuse these patients to learn non-disease patterns are including an important fraction of patients with evidentpathological changes related to the disease, something that might result in reduced performance and lack ofbiological interpretability.This work was partially supported by the project PI16/01416(ISCIIIco-fundedFEDER) and RYC-2015/17430 (RamónyCajal,Pablo Aguiar). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI)(National Institutes of Health Grant U01AG024904) and DODADNI (Department of Defense award number W81XWH-12-2-0012)S

Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD

Prediction of Cognitive Decline in Healthy Older Adults using fMRI

Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three neuropsychological measures. Using logistic regression, demographic variables (age, years of education, gender) and family history of dementia did not predict future cognitive decline. Greater fMRI activity, absence of an APOE ε4 allele, and larger hippocampal volume were associated with reduced likelihood of cognitive decline. The most effective combination of predictors involved fMRI brain activity and APOE ε4 status. Brain activity measured from task-activated fMRI, in combination with APOE ε4 status, was successful in identifying cognitively intact individuals at greatest risk for developing cognitive decline over a relatively brief time period. These results have implications for enriching prevention clinical trials designed to slow AD progression

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Large-scale neuroimaging in Alzheimer’s disease and normal aging

Large-scale neuroimaging data is becoming increasingly available, providing a rich data source with which to study neurological conditions. In this thesis, I demonstrate the utility of large-scale neuroimaging as it applies to Alzheimer’s disease (AD) and normal aging, using univariate parametric mapping, regional analysis, and advanced machine learning. Specifically, this thesis covers: 1) validation and extension of prior studies using large-scale datasets; 2) AD diagnosis and normal aging evaluation empowered by large-scale datasets and advanced deep learning algorithms; 3) enhancement of cerebral blood volume (CBV) fMRI utility with retrospective CBV-fMRI technique. First, I demonstrated the utility of large-scale datasets for validating and extending prior studies using univariate analytics. I presented a study localizing AD-vulnerable regions more reliably and with better anatomical resolution using data from more than 350 subjects. Following a similar approach, I investigated the structural characteristics of healthy APOE ε4 homozygous subjects screened from a large-scale community-based study. To study the neuroimaging signatures of normal aging, we performed a large-scale joint CBV-fMRI and structural MRI study covering age 20-70s, and a structural MRI study of normal aging covering the full age-span, with the elder group screened from a large-scale clinic-based study ensuring no evidence of AD using both longitudinal follow-up and cerebrospinal fluid (CSF) biomarkers evidences. Second, I performed deep learning neuroimaging studies for AD diagnosis and normal aging evaluation, and investigated the regionality associated with each task. I developed an AD diagnosis method using a 3D convolutional neural network model trained and evaluated on ~4,600 structural MRI scans and further investigated a series of novel regionality analyses. I further extensively studied the utility of the structural MRI summary measure derived from the deep learning model in prodromal AD detection. This study constitutes a general analytic framework, which was followed to evaluate normal aging by performing deep learning-based age estimation in cognitively normal population using more than 6,000 scans. The deep learning neuroimaging models classified AD and estimated age with high accuracy, and also revealed regional patterns conforming to neuropathophysiology. The deep learning derived MRI measure demonstrated potential clinical utility, outperforming other AD pathology measures and biomarkers. In addition, I explored the utility of deep learning on positron emission tomography (PET) data for AD diagnosis and regionality analyses, further demonstrating the broad utility and generalizability of the method. Finally, I introduced a technique enabling CBV generation retrospectively from clinical contrast-enhanced scans. The derivation of meaningful functional measures from such clinical scans is only possible through calibration to a reference, which was built from the largest collection of research CBV-fMRI scans from our lab. This method was validated in an epilepsy study and demonstrated the potential to enhance the utility of CBV-fMRI by enriching the CBV-fMRI dataset. This technique is also applicable to AD and normal aging studies, and potentially enables deep learning based analytic approaches applied on CBV-fMRI with similar pipelines used in structural MRI. Collectively, this thesis demonstrates how mechanistic and diagnostic information on brain disorders can be extracted from large-scale neuroimaging data, using both classical statistical methods and advanced machine learning

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

Multimodal brain imaging reveals structural differences in Alzheimer's disease polygenic risk carriers: A study in healthy young adults

Background Recent genome-wide association studies have identified genetic loci that jointly make a considerable contribution to risk of developing Alzheimer’s disease (AD). Because neuropathological features of AD can be present several decades before disease onset, we investigated whether effects of polygenic risk are detectable by neuroimaging in young adults. We hypothesized that higher polygenic risk scores (PRSs) for AD would be associated with reduced volume of the hippocampus and other limbic and paralimbic areas. We further hypothesized that AD PRSs would affect the microstructure of fiber tracts connecting the hippocampus with other brain areas. Methods We analyzed the association between AD PRSs and brain imaging parameters using T1-weighted structural (n = 272) and diffusion-weighted scans (n = 197). Results We found a significant association between AD PRSs and left hippocampal volume, with higher risk associated with lower left hippocampal volume (p = .001). This effect remained when the APOE gene was excluded (p = .031), suggesting that the relationship between hippocampal volume and AD is the result of multiple genetic factors and not exclusively variability in the APOE gene. The diffusion tensor imaging analysis revealed that fractional anisotropy of the right cingulum was inversely correlated with AD PRSs (p = .009). We thus show that polygenic effects of AD risk variants on brain structure can already be detected in young adults. Conclusions This finding paves the way for further investigation of the effects of AD risk variants and may become useful for efforts to combine genotypic and phenotypic data for risk prediction and to enrich future prevention trials of AD