Multiethnic machine learning brain signatures of ADRD

ADRD 的多种族机器学习大脑特征

基本信息

批准号：
10693310
负责人：
Mohamad Habes
金额：
$ 70.62万
依托单位：
UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10693310
关键词：
Acceleration Address Adult Affect Age Aging Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease pathology Alzheimer&apos s disease related dementia Alzheimer’s disease biomarker Amyloid beta-42 Atherosclerosis Risk in Communities Atrophic Attenuated Biological Biological Markers Black American Black Populations Blood Brain Brain imaging CHI3L1 gene Caring Cerebrovascular Trauma Clinical Cognition Cohort Studies Communities Computer software Data Data Set Dementia Development Diabetes Mellitus Dimensions Disease Disparity Dyslipidemias Ethnic Origin Ethnic Population Exposure to Framingham Heart Study Functional Magnetic Resonance Imaging Funding Future Genetic Glial Fibrillary Acidic Protein Health Care Costs Heart Heterogeneity Hispanic Americans Hispanic Populations Hypertension Impaired cognition Incidence Individual Insulin Resistance Intervention Intervention Trial Knowledge Light Longevity Longitudinal cohort Machine Learning Magnetic Resonance Imaging Medical Metabolic syndrome Microvascular Dysfunction Morbidity - disease rate Multi-Ethnic Study of Atherosclerosis Neuroanatomy Neurodegenerative Disorders Not Hispanic or Latino Obesity Onset of illness Participant Pathogenesis Pathologic Processes Pathway interactions Pattern Population Preventive Race Research Rest Smoking Stage at Diagnosis Structure Training United States United States National Institutes of Health Validation advanced analytics aging brain aging demographic aging demography aging population analytical method biomarker validation blood-based biomarker brain based cardiovascular health cardiovascular risk factor clinical diagnosis clinically relevant cognitive function cohort comorbidity dementia risk disorder risk ethnic difference ethnic diversity ethnic minority ethnoracial functional decline genomic epidemiology health disparity high dimensionality lifestyle data machine learning algorithm machine learning method machine learning model machine learning prediction mild cognitive impairment mortality multi-ethnic multimodal neuroimaging neurofilament neuroimaging neuroimaging marker novel pre-clinical predictive signature public health priorities racial difference racial diversity racial minority racial population sex socioeconomics tau-1 therapy development trend vascular cognitive impairment and dementia vascular risk factor

项目摘要

PROJECT SUMMARY / ABSTRACT The underlying pathology of Alzheimer's disease and related dementias (ADRDs) accumulates gradually over decades, making the identification of non-invasive, sensitive biomarkers in the preclinical stage a critical public health priority. Harnessing advanced analytic methods, our team and others have established neuroimaging signatures of advanced brain aging (Spatial Pattern of Atrophy Recognition of Brain Aging, SPARE-BA) and functional decline (fSPARE-BA), and ADRDs (SPARE-AD and SPARE-Small vessel disease), which predict incident cognitive decline. Unfortunately, most research to date has been conducted in predominantly non- Hispanic white populations, which limits the ability to generalize results to the diverse ethnoracial makeup of the United States' growing aging demographic. If current trends continue, machine learning models will primarily be trained in ethnically imbalanced datasets, leading to biases that may affect clinical relevance. Thus, the primary aims of the current proposal are to: leverage an ethnically diverse neuroimaging consortium to build new machine learning models trained by data from ethnically well-balanced populations, derive sensitive and specific neuroimaging signatures of brain aging and ADRD, and evaluate whether they can be practical non-invasive biomarkers of incident cognitive decline, mild cognitive impairment (MCI), and dementia across ethnoracial groups. We propose to leverage the rich clinical and neuroimaging (structural MRI and resting-state functional MRI) data within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, including the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Genetics of Brain Structure and Function Study (GOBS), the Framingham Heart Study (FHS), the Vascular Contributions to Cognitive Impairment and Dementia consortium (MARK-VCID) and the Multi-Ethnic Study of Atherosclerosis (MESA). We will leverage a collaborative research framework across existing longitudinal cohorts to address unanswered questions contributing to disparities in ADRD burden. Machine learning algorithms will be applied to brain imaging data of over 7,200 non-Hispanic Whites, 1,400 Blacks, and 1,425 Hispanics to address our Specific Aims: 1) Generate and evaluate clinical utility of machine learning-based signatures of brain aging and ADRD for each race/ethnic group and uncover multidimensional heterogeneity in aging across groups; 2) Examine associations of vascular risk factors with the derived machine learning-based brain signatures of ADRD by race/ethnicity, and 3) Explore blood-based biomarker predictors of these machine learning-based brain signatures by ethnoracial group to elucidate underlying biological mechanisms. Further, we will share our robust machine learning models together with implementation software with the scientific community. This project will develop and validate neuroimaging markers with robust predictive utility for incident cognitive decline and to identify underlying pathophysiologic pathways, expanding opportunities for novel intervention development across diverse ethnoracial cohorts. ii

项目摘要 /摘要阿尔茨海默氏病和相关痴呆症（ADRD）的潜在病理逐渐积累几十年来，在临床前阶段识别非侵入性，敏感的生物标志物是关键的公众健康优先级。利用高级分析方法，我们的团队和其他人建立了神经影像学晚期脑衰老的签名（脑衰老的萎缩识别的空间模式，备用BA）和功能下降（FSPARE-BA）和ADRDS（备用AD和备用小血管疾病），可预测事件认知能力下降。不幸的是，迄今为止的大多数研究主要是在非 - 西班牙裔白人种群，这限制了将结果推广到各种民族种族化构成的能力美国不断增长的衰老人群。如果当前趋势继续下去，机器学习模型将主要是接受了种族失衡数据集的培训，导致可能影响临床相关性的偏见。因此，主要当前建议的目的是：利用种族多样化的神经感知财团来建造新机器通过来自种族平衡良好的数据训练的学习模型，得出敏感和特定脑衰老和ADRD的神经影像学特征，并评估它们是否可以实用性非侵入性事件认知下降，轻度认知障碍（MCI）和痴呆症的生物标志物组。我们建议利用丰富的临床和神经成像（结构MRI和静止状态功能 MRI）在基因组流行病学（电荷）联盟中心脏和衰老研究中的数据中的数据，包括社区研究中的动脉粥样硬化风险（ARIC），心血管健康研究（CHS），大脑结构和功能研究的遗传学（GOB），弗雷明汉心脏研究（FHS），血管对认知障碍和痴呆财团（Mark-VCID）的贡献以及多民族研究动脉粥样硬化（MESA）。我们将利用现有纵向的协作研究框架同时解决尚未解决的问题，导致差异负担。机器学习算法将应用于超过7,200个非西班牙裔白人，1,400个黑人和1,425的脑成像数据解决我们的特定目标的西班牙裔：1）生成和评估基于机器学习的临床实用性每个种族/种族群体的大脑衰老和ADRD的签名，并发现多维异质性跨群体衰老； 2）检查血管危险因素与基于机器学习的相关性 ADRD的大脑签名按种族/种族签名，以及3）探索这些机器的血液生物标志物预测指标民族群体基于学习的大脑签名，以阐明潜在的生物学机制。此外，我们将与科学的机器学习模型以及实施软件共享我们的强大机器学习模型社区。该项目将开发并验证具有强大预测性的事件的神经影像标记认知能力下降并识别潜在的病理生理途径，扩大新颖的机会各种民族人群的干预发展。 ii