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.

抽象的内在功能连接磁共振成像是研究组织结构的有力工具人脑中的功能网络（FN）。丰富且不断积累的证据表明，FN 青少年时期经历可预测的规范发展，并且异常发展与多样化的精神病理学。最近基于图像分析进展的工作已经证实 FN 正在事实因人而异。当与大规模神经影像数据集配对时，特定于人的 FN 提供开发新诊断方法的前所未有的转化机会可以指导神经精神疾病的个性化治疗。然而，特定于人的转化承诺目前，FNs 受到一些障碍的阻碍。首先，当前的方法计算特定的个性化 FN 尽管有明确的证据表明大脑是一个具有分层功能组织的多尺度系统。其次，为了加强不同主体之间的通信，个性化 FN 通常是在以下条件下计算的：某些限制可能会产生有偏差的结果。第三，深度学习在以下方面取得了不同的成功：神经影像数据分析部分原因是通常采用自组织网络架构，采用预先设计的而不是学习的特征通常会剥夺特征学习能力。四、至从大规模神经影像研究的多个数据集中纠正神经影像测量的位点效应当前的方法通常尝试在统计建模之前协调数据，从而导致有价值的数据丢失信息。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，深度学习算法已经被开发出来分析 fcMRI 数据，但通常以记录不完整的源代码形式发布，限制了再现性和被转化研究人员采用。在此申请中，我们以前一奖励期的成功为基础通过开发、验证和传播表征大脑功能的工具来解决这些局限性单个学科层面的组织。我们将利用互补的大规模大脑研究开发来验证我们的方法并描述 FN 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、精神病和多动症谱系症状。具体来说，我们将开发新方法来 1）准确识别无偏见的个性化 FN 多尺度的层级组织； 2）使用个性化方法稳健地预测精神症状维度具有优化深度神经网络架构和集成位点效应校正的 FN，以及 3) 有效对 FN 的纵向数据进行建模，以创建精神病理学的预测模型。这些工具将在免费提供的容器化软件包，确保跨计算平台的无障碍移植和完全的再现性。

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

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

DOI：
10.1109/isbi45749.2020.9098524
发表时间：
2020-04
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
0
作者：
Jiao Z;Li H;Fan Y
通讯作者：
Fan Y

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

sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain.