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Personalized Functional Network Modeling to Characterize and Predict Psychopathology in Youth

用于表征和预测青少年精神病理学的个性化功能网络模型

基本信息

批准号：
10630919
负责人：
Yong Fan
金额：
$ 65.34万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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：
2021-08-02 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10630919
关键词：
Acceleration Address Adolescent Adopted Adoption Anxiety Architecture Attention deficit hyperactivity disorder Award Brain Brain imaging Clinical Data Communication Communities Computer software Computing Methodologies Data Data Analyses Data Set Development Diagnostic Dimensions Engineering Ensure Environment Functional Magnetic Resonance Imaging Future Human Image Individual Learning Magnetic Resonance Imaging Measurement Measures Mental Depression Methods Modeling Nature Neurons Neurosciences Paper Pennsylvania Persons Philadelphia Procedures Psychopathology Psychoses Reproducibility Research Site Source Code Statistical Models Symptoms System Techniques Universities Work Youth analytical tool cognitive development cohort computational platform connectome data harmonization data structure deep learning deep learning algorithm deep neural network human data image processing improved integration site interest large scale data learning strategy model building multiple datasets mutual learning network architecture network models neural neural network architecture neuroimaging neuropsychiatry novel novel diagnostics open source outcome prediction personalized medicine portability predictive modeling programs psychiatric symptom recurrent neural network success tool translational potential translational scientist user-friendly

项目摘要

ABSTRACT Intrinsic functional connectivity magnetic resonance imaging is a powerful tool to study the organization of functional networks (FNs) in the human brain. Rich and accumulating evidence demonstrates that FNs undergo predictable normative development in youth, and that abnormal development is associated with diverse psychopathology. Recent work based on advances in image analytics has established that FNs are in fact person-specific. When paired with large-scale neuroimaging datasets, person-specific FNs provide unprecedented translational opportunities for the development of new diagnostics that could guide personalized treatments for neuropsychiatric illnesses. However, the translational promise of person-specific FNs is at present hindered by several obstacles. First, current methods compute personalized FNs at a specific scale, despite clear evidence that the brain is a multi-scale system with a hierarchical functional organization. Second, to enforce correspondence across different subjects personalized FNs are typically computed under certain constraints, which may yield biased results. Third, deep learning has achieved mixed success in neuroimaging data analysis partially due to the fact that ad-hoc network architecture is typically adopted and feature learning capability is often deprived by adopting pre-engineered rather than learned features. Fourth, to correct site effects of neuroimaging measures from multiple datasets of large-scale neuroimaging studies current methods typically attempt to harmonize data prior to statistical modeling, resulting in loss of valuable information. Fifth, longitudinal neuroimaging and clinical data are increasingly available, but effective analytic tools for longitudinal data are scarce. Last but not least, deep learning algorithms have been developed to analyze fcMRI data but are often released as poorly documented source code, limiting both reproducibility and adoption by translational researchers. In this application, we build on the success of the prior award period to address these limitations by developing, validating, and disseminating tools that characterize brain functional organization at an individual subject level. We will leverage complementary large-scale studies of brain development to validate our methods and delineate how abnormal development of FNs is associated with major dimensions of psychopathology in youth, including depression, anxiety, psychosis, and ADHD-spectrum symptoms. Specifically, we will develop novel methods to 1) accurately identify bias-free personalized FNs with a multiscale hierarchical organization; 2) robustly predict psychiatric symptom dimensions using personalized FNs with optimized deep neural network architecture and integrated site-effect correction, and 3) effectively model longitudinal data of FNs to create predictive models of psychopathology. These tools will be released in a freely available, containerized software package to ensure frictionless portability across computing platforms and full reproducibility.

摘要固有功能连接性磁共振成像是研究脑组织结构的有力工具人脑的功能网络(FN)。大量和不断积累的证据表明，FNS 在青年中进行可预测的规范性发展，而异常发育与不同的精神病态。基于图像分析方面的进展的最新工作已经确定，FN在因人而异的事实。当与大规模神经成像数据集配对时，特定于个人的FN提供前所未有的转换机会，为开发新的诊断技术提供了可能神经精神疾病的个性化治疗。然而，特定于人的翻译承诺 FNS目前受到几个障碍的阻碍。首先，当前方法在特定的位置计算个性化的FN 规模，尽管有明确的证据表明，大脑是一个具有层级功能组织的多尺度系统。其次，为了加强不同主题之间的对应关系，个性化的FN通常在以下项下计算某些限制，这可能会产生有偏见的结果。第三，深度学习在以下领域取得了喜忧参半的成就神经成像数据分析部分是因为通常采用自组织网络架构，并且通过采用预先设计的功能而不是学习的功能，功能学习能力经常被剥夺。第四，到来自大规模神经成像研究的多个数据集的神经成像测量的正确部位效应目前的方法通常试图在统计建模之前协调数据，导致有价值的损失信息。第五，纵向神经影像和临床数据越来越多，但有效的分析用于纵向数据的工具很少。最后但并非最不重要的一点是，深度学习算法已经发展到分析fcMRI数据，但通常以文档记录不佳的源代码形式发布，这限制了可重复性和被翻译研究人员采用。在这项申请中，我们在前一个获奖期的成功基础上，通过开发、验证和传播表征大脑功能的工具来解决这些限制个人学科层面的组织。我们将利用对大脑的补充大规模研究开发以验证我们的方法，并描述FNS的异常发育如何与青少年精神病理学的主要方面，包括抑郁、焦虑、精神病和ADHD谱系症状。具体地说，我们将开发新的方法来1)准确地识别无偏见的个性化FN 多尺度的层级组织；2)使用个性化的方法稳健地预测精神症状维度具有优化的深度神经网络结构和集成的现场效应校正的FNS，以及3)有效对FNS的纵向数据进行建模，以创建精神病理学的预测模型。这些工具将于年发布免费提供的集装箱化软件包，可确保计算平台之间的无缝移植和完全的重复性。

项目成果

期刊论文数量（33）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.

DOI：
10.1111/adb.12644
发表时间：
2019-07
期刊：
Addiction biology
影响因子：
3.4
作者：
Wetherill RR;Rao H;Hager N;Wang J;Franklin TR;Fan Y
通讯作者：
Fan Y

Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity

DOI：
10.1609/aaai.v32i1.11907
发表时间：
2018-02
期刊：
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Xiaofeng Zhu;Hongming Li;Yong Fan
通讯作者：
Xiaofeng Zhu;Hongming Li;Yong Fan

Riccati-Regularized Precision Matrices for Neuroimaging.

DOI：
10.1007/978-3-319-59050-9_22
发表时间：
2017-06
期刊：
Information processing in medical imaging : proceedings of the ... conference
影响因子：
0
作者：
Honnorat N;Davatzikos C
通讯作者：
Davatzikos C

Improving Diagnosis of Autism Spectrum Disorder and Disentangling its Heterogeneous Functional Connectivity Patterns Using Capsule Networks.