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Identification of Brain Circuit Markers for Psychosis in Alzheimer's Disease by Leveraging Big Data and Machine Learning

利用大数据和机器学习识别阿尔茨海默病精神病的脑回路标志物

基本信息

批准号：
10491260
负责人：
Kaustubh Satyendra Supekar
金额：
$ 19.68万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-30 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10491260
关键词：
Affect Alzheimer&apos s Disease Alzheimer&apos s disease patient Artificial Intelligence Big Data Biological Markers Brain Brain imaging Caregivers Classification Clinical Clinical Psychology Cognitive Data Data Set Development Diagnosis Disease Etiology Goals Human Individual Investigation Knowledge Lead Learning Machine Learning Measures Modeling Neurobiology Patients Persons Phenotype Psychiatry Psychoses Public Health Reproducibility of Results Research Research Personnel Sampling Schizophrenia Testing Universities Validation Work big-data science clinically relevant cognitive control cognitive function computer framework connectome deep learning diagnostic strategy early psychosis effective therapy improved innovation learning strategy multidisciplinary neuroimaging neuropsychiatric symptom novel open source personalized diagnostics phenotypic data precision medicine predictive marker psychotic symptoms relating to nervous system transfer learning translational neuroscience treatment strategy

项目摘要

Psychotic symptoms are among the most common and persistent neuropsychiatric symptoms in Alzheimer’s disease, affecting over 50% of patients with Alzheimer’s disease. Yet, the etiology of psychosis in Alzheimer’s disease is still poorly understood and systematic investigations have been hampered by small samples and lack of reproducible findings. Critically, robust biomarkers are needed to understand the origins/progression of psychosis in Alzheimer’s disease, and to identify targets for treatment. Newly available human neurobiological data offer an unprecedented opportunity for developing robust and predictive biomarkers for psychosis in Alzheimer’s disease. The overarching goal of our proposal is to identify robust and predictive biomarkers for psychosis in Alzheimer’s disease using a novel data-driven computational framework. Specifically, we will use a transformative “Big Data” science approach combining exciting recent advances in deep learning and our recent work on quantitative dynamic brain circuit analyses with a wealth of newly available large-scale open- source brain imaging and phenotypic data from multiple consortia, as well as data we have acquired at Stanford University. To achieve these goals, we propose four aims. In Aim 1, we will develop and validate a novel data-driven computational framework for identifying neurobiological features that distinguish between groups, leveraging recent advances in deep learning and brain circuit dynamics. In Aim 2, we will identify neurobiological features that distinguish idiopathic psychosis (schizophrenia) from neurotypical controls, using our validated computational framework and “Big” data from schizophrenia. In Aim 3, we will identify neurobiological features that distinguish Alzheimer’s disease patients with psychosis from Alzheimer’s disease patients without psychosis, using our validated computational framework and data from Alzheimer’s disease and schizophrenia. In Aim 4, we will identify neurobiological features that predict onset of psychosis in Alzheimer’s disease. The proposed studies are highly synergistic with the goals of the PAR-20-159, which aims to “enhance knowledge of mechanisms associated with neuropsychiatric symptoms in persons with Alzheimer’s disease”. Through the successful completion of the work described here, the proposed studies will transform our understanding of brain circuit mechanisms underlying psychosis in Alzheimer’s disease, and crucially, provide a new computational framework for improved mechanistic understanding of other neuropsychiatric symptoms in Alzheimer’s disease. Ultimately, these advances will lead to better diagnosis and more effective treatments for neuropsychiatric symptoms in Alzheimer’s disease and, more broadly, advance precision medicine.

精神病性症状是阿尔茨海默氏症中最常见和最持久的神经精神症状之一疾病，影响超过50%的阿尔茨海默病患者。然而，阿尔茨海默氏症的精神病病因对疾病的了解仍然很少，系统的调查因样本少而受到阻碍，缺乏可重复的发现。至关重要的是，需要强有力的生物标志物来了解肿瘤的起源/进展。阿尔茨海默病中的精神病，并确定治疗目标。新的人类神经生物学数据为开发精神病的可靠和预测性生物标志物提供了前所未有的机会，老年痴呆症我们的建议的首要目标是确定稳健的和预测性的生物标志物，使用一种新的数据驱动的计算框架研究阿尔茨海默病的精神病。具体来说，我们将使用一种变革性的“大数据”科学方法，结合了深度学习和我们的最近的工作定量动态脑回路分析与丰富的新可用的大规模开放，来源脑成像和表型数据来自多个财团，以及我们已经获得的数据，斯坦福大学。为了实现这些目标，我们提出了四个目标。在目标1中，我们将开发和验证一个一种新的数据驱动的计算框架，用于识别区分利用深度学习和脑回路动力学的最新进展。在目标2中，我们将确定神经生物学特征，区分特发性精神病（精神分裂症）从神经典型对照，使用我们经过验证的计算框架和精神分裂症的“大”数据。在目标3中，我们将确定阿尔茨海默病患者与阿尔茨海默病患者的神经生物学特征没有精神病的患者，使用我们经过验证的计算框架和阿尔茨海默病的数据，和精神分裂症在目标4中，我们将确定预测精神病发作的神经生物学特征，老年痴呆症拟议的研究与PAR-20-159的目标高度协同，目的是“提高对与神经精神症状相关的机制的认识，老年痴呆症”。通过成功完成这里所述的工作，拟议的研究将改变我们对阿尔茨海默病精神病潜在脑回路机制的理解，重要的是，提供了一个新的计算框架，以改善对其他生物的机械理解，阿尔茨海默病的神经精神症状最终，这些进步将导致更好的诊断，更有效地治疗阿尔茨海默病的神经精神症状，更广泛地说，精准医疗

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization.

DOI：
10.1073/pnas.2310012121
发表时间：
2024-02
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
S. Ryali;Yuan Zhang;C. de los Angeles;Kaustubh Supekar;Vinod Menon
通讯作者：
S. Ryali;Yuan Zhang;C. de los Angeles;Kaustubh Supekar;Vinod Menon

Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.

DOI：
10.1192/bjp.2022.13
发表时间：
2022-02-15
期刊：
BRITISH JOURNAL OF PSYCHIATRY
影响因子：
10.5
作者：
Supekar, Kaustubh;de los Angeles, Carlo;Ryali, Srikanth;Cao, Kaidi;Ma, Tengyu;Menon, Vinod
通讯作者：
Menon, Vinod

Robust, Generalizable, and Interpretable Artificial Intelligence-Derived Brain Fingerprints of Autism and Social Communication Symptom Severity.