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Computational ontology of brain systems across the human neuroimaging literature

人类神经影像文献中大脑系统的计算本体论

基本信息

批准号：
10380876
负责人：
Elizabeth Helen Beam
金额：
$ 1.15万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2022-06-12
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10380876
关键词：
Academic Training Adult Affect Base of the Brain Benchmarking Biological Biology Brain Brain region Brain scan Cognitive Custom Data Devices Diagnosis Emotional Environment Expert Opinion Fellowship Foundations Goals Grant Graph Higher Order Chromatin Structure Human Individual Influentials Link Literature Location Machine Learning Magnetic Resonance Imaging Maps Mental disorders Mentors Meta-Analysis Modeling Names National Institute of Mental Health Natural Language Processing Neurobiology Neurosciences Ontology Pattern Performance Physicians Positron-Emission Tomography Prediction of Response to Therapy Preparation Psychiatric Diagnosis Psychiatry Publications Reporting Reproducibility Research Research Domain Criteria Resources Rest Scheme Scientist Sensitivity and Specificity Site Structure Support System Symptoms System Testing Text Training Update Variant Work base brain circuitry career clinically relevant comorbidity computer science computerized tools computing resources deep learning model design diagnostic criteria diagnostic platform improved innovation learning strategy mental function mental state neural network classifier neuroimaging neuroinformatics neuroregulation predictive modeling social system architecture

项目摘要

Project Summary/Abstract Symptom-based diagnoses of mental illness are highly comorbid, biologically heterogeneous, and poorly predictive of treatment response. The National Institute of Mental Health has led efforts to redefine mental illness by its biological causes, establishing the Research Domain Criteria (RDoC) framework as a guide for investigating variation in basic brain systems. RDoC has been influential, named in hundreds of grants and publications, but it has yet to be systematically validated. It is unknown whether circuit-function links underlying the RDoC brain systems are reproducible across studies, and organizing principles remain largely untested. While the structure of RDoC as a modular hierarchy has evidence in resting state analyses, it has not been shown whether this applies to systems that support the diverse mental states affected in psychiatric disease. It is necessary to validate RDoC, and moreover, to establish fundamental principles of organization for systems defined jointly by human brain structure and function. The objective of this proposal is to apply large- scale computational neuroimaging meta-analyses to build a data-driven ontology that will not only serve as a benchmark in evaluating the validity of RDoC but also characterize the architecture of systems for human brain function. The long-term goal is to redefine mental illness by differences from healthy function within the brain systems of a data-driven ontology, facilitating rational targeting of neuromodulation treatments. The proposed meta-analyses will be the most comprehensive in the field with 18,155 MRI and PET studies already collected. The mental functions considered in these studies have been extracted from article texts using natural language processing, and brain circuits will be mapped from the brain coordinate data that were reported. The hypothesis is that brain systems are comprised of reproducible circuit-function links organized into a modular hierarchy, which for some systems will require updates to RDoC. This will be tested by comparing RDoC systems against those of a data-driven ontology. Aim 1: The reproducibility of circuit-function links will be evaluated by the performance of neural network classifiers predicting functions in article texts from circuits in brain scan data, and vice versa. Aim 2: The modularity of brain systems will be evaluated by a graph theoretic approach, and hierarchical structure will be assessed by representational similarity analysis. The impact of this project will be to validate the foremost psychiatry research framework and to characterize human brain systems through an innovative computational strategy. Together with targeted academic training in neurobiology, the fellowship is designed to offer preparation for a career as a physician-scientist leading advances in computational psychiatry. Training will be supported by an environment that combines world-class computing resources with esteemed and engaged mentors in psychiatry, neuroscience, and computer science.

项目总结/摘要基于症状的精神疾病诊断具有高度的共病性、生物异质性，预测治疗反应。美国国家心理健康研究所（National Institute of Mental Health）致力于重新定义心理健康。疾病的生物学原因，建立研究领域标准（RDoC）框架作为指导，研究基本大脑系统的变异RDoC一直很有影响力，在数百个赠款命名，出版物，但尚未得到系统验证。目前尚不清楚底层电路功能链接是否 RDoC大脑系统在不同的研究中是可重复的，组织原理在很大程度上还没有得到验证。虽然RDoC作为模块化层次结构的结构在静息状态分析中有证据，但它还没有被证实。表明这是否适用于支持受精神疾病影响的各种精神状态的系统。有必要验证RDoC，此外，还需要建立组织的基本原则，由人脑结构和功能共同定义的系统。这项建议的目的是应用大- 规模计算神经成像元分析，以建立一个数据驱动的本体，不仅将作为一个它不仅是评估RDoC有效性的基准，也是人脑系统架构的特征功能长期目标是通过大脑内部健康功能的差异来重新定义精神疾病数据驱动的本体系统，促进神经调节治疗的合理靶向。拟议的荟萃分析将是该领域最全面的，涉及18，155项MRI和PET研究已经收集。这些研究中考虑的心理功能是从文章文本中提取的使用自然语言处理，大脑电路将从大脑坐标数据映射，报道该假说认为，大脑系统是由可重复的电路功能链接组成，转换为模块化层次结构，对于某些系统，这将需要更新RDoC。这将由以下人员进行测试：将RDoC系统与数据驱动的本体进行比较。目的1：电路功能的再现性链接将通过神经网络分类器在文章文本中的预测功能的性能进行评估，大脑扫描数据中的电路，反之亦然。目标2：大脑系统的模块性将通过图形进行评估理论方法和层次结构将通过代表性相似性分析进行评估。该项目的影响将是验证最重要的精神病学研究框架并表征人类大脑系统通过创新的计算策略。加上有针对性的学术培训在神经生物学方面，该奖学金旨在为作为一名医生科学家的职业生涯提供准备，计算精神病学的进展培训将得到一个环境的支持，结合世界一流的与精神病学、神经科学和计算机科学领域受人尊敬和投入的导师一起计算资源。