Neural Mechanisms of Irritability: A Dimensional Approach with Integration of Multi-Voxel Pattern Analysis and Univariate Analysis

烦躁的神经机制：多体素模式分析和单变量分析相结合的维度方法

• 批准号: 9986132
• 负责人: Wan-Ling Tseng
• 金额: $ 24.9万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9986132
• 关键词: Address; Adult; Affect; Affective; Amygdaloid structure; Anxiety Disorders; Area; Attention; Award; Behavior; Behavioral Sciences; Biological Markers; Biological Sciences; Brain; Categories; Child Psychiatry; Childhood; Chronic; Classification; Clinical; Communities; Complex; Data; Depressive disorder; Development; Developmental Course; Diagnosis; Diagnostic; Dimensions; Ensure; Environmental Risk Factor; Faculty; Feedback; Frustration; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Future; Goals; Health; Impairment; Individual; Individual Differences; Inferior frontal gyrus; Knowledge; Linear Models; Link; Longevity; Machine Learning; Measures; Mental disorders; Methods; Modeling; Monitor; Movement; Neuronal Dysfunction; Neurosciences; Parents; Parietal; Pattern; Preventive Intervention; Psychopathology; Public Health; Reporting; Reproducibility of Results; Research; Research Domain Criteria; Research Personnel; Research Training; Rest; Sample Size; Sampling; Source; Suicide; Symptoms; System; Techniques; Time; Training; United States National Institutes of Health; Universities; Work; Youth; analytical method; base; career; clinical phenotype; design; experience; imaging modality; insight; learning strategy; member; neural circuit; neural correlate; neuromechanism; novel; outcome forecast; personalized predictions; precision medicine; programs; relating to nervous system; skills; social; teacher; time use; treatment response

Chronic, clinically-impairing irritability is a serious health problem that affects 3-5% of youth and predicts both adult depressive and anxiety disorders and long-term impairment. Current studies on the neural mechanisms of irritability in youth employ traditional univariate analyses and have limitations: (1) they examine activation patterns in particular regions, thus using only a small portion of the data and (2) they tend to find small to moderate effect sizes. In addition, little is known about functional connectivity at rest and its association with irritability or the ability of neural measures to predict changes in irritability over time. These limitations could be addressed through research using multi-voxel pattern analysis (MVPA), i.e., by applying machine learning methods to both resting state and task-based fMRI data. MVPA exploits the full spatial pattern of brain activity and thus has increased sensitivity compared to conventional univariate, individual-voxel-based general linear model (GLM) approaches. However, univariate analyses may provide complementary or unique information relative to MVPA, given its sensitivity to variability and global engagement of task effects between subjects. The specific aims of this project are to identify patterns of whole-brain activation (Aim 1 & 2a, 2b) and functional connectivity at rest (Aim 2a, 2b) using a combination of MVPA and univariate approaches to predict (1) individual differences in irritability in a clinical sample of youth and in healthy youth with varying degrees of irritability, and (2a) individual differences in irritability and (2b) changes in irritability 6 months later in a community sample of youth with varying degrees of irritability. This work will provide new insights into the pathophysiology of multiple mental disorders for which irritability is an important symptom, and it has the potential to identify biomarkers that can be used to predict the course and prognosis of clinically-impairing irritability. The long-term goal of the PI is to become an independent NIH-funded investigator with a neuroscience-focused research program targeting developmental trajectories of irritability. To achieve this goal, the PI aims to expand her previous training to support both advanced, independent research on resting state and task-based fMRI and the use of pattern classification techniques, as well as their integration with univariate methods, applied in samples with varying degrees of irritability. This training objective directly supports the PI’s research objective, which is to identify the neural mechanisms of irritability and neural correlates that predict changes in irritability over time using a combination of MVPA and univariate imaging methods applied to task-based and resting state fMRI data. The central hypothesis, based on the PI’s pilot data, is that activity patterns during a frustration task, along with connectivity patterns at rest, will predict levels of, and changes in, irritability. Both univariate and MVPA approaches will converge on similar findings; however, MVPA will identify additional activation and connectivity patterns predictive of irritability or changes in irritability. The training objectives for this award are to enhance the PI’s research and professional skills and knowledge in three areas: (1) phenotyping clinically-impairing irritability; (2) task-based and resting state fMRI methods, designs, preprocessing, and advanced MVPA techniques, i.e., machine learning for analyzing fMRI data, and their integration with univariate analyses; and (3) professional skills essential for a successful research career as a faculty member at a research university. By completing the proposed research and training, the PI expects to obtain sufficient training to function as an independent developmental affective neuroscientist, and sufficient pilot data to submit a competitive R01 application designed to examine predictors of developmental changes in pediatric irritability across multiple levels of analysis from neural circuits, behaviors, social experiences, to environmental factors.

慢性、临床损害性易怒是一种严重的健康问题，影响3-5%的青年， 成人抑郁症和焦虑症以及长期损害。神经机制的研究现状 青少年易怒的研究采用传统的单变量分析，有局限性：（1）他们研究激活 在特定区域的模式，因此只使用一小部分的数据和（2）他们往往会发现小到 中等效应量。此外，人们对静息状态下的功能连接及其与 易怒或神经测量预测易怒随时间变化的能力。这些限制可能是 通过使用多体素模式分析（MVPA）的研究来解决，即，通过将机器学习应用 静息状态和基于任务的fMRI数据的方法。MVPA利用大脑活动的完整空间模式 因此与传统的单变量、基于个体素的一般线性 模型（GLM）方法。然而，单变量分析可以提供补充或独特的信息 相对于MVPA，考虑到其对受试者之间任务效应的变异性和全局参与的敏感性。 该项目的具体目标是确定全脑激活的模式（目标1和2a，2b）， 使用MVPA和单变量方法的组合预测静息时的功能连接（目标2a，2b） (1)在青年和健康青年不同程度的临床样本中， 易怒，（2a）易怒的个体差异和（2b）6个月后易怒的变化， 社区样本的青年不同程度的易怒。这项工作将提供新的见解， 多精神障碍的病理生理学，其中易怒是一个重要的症状，它具有 潜在的生物标志物，可用于预测临床损害的过程和预后， 易怒PI的长期目标是成为一名独立的NIH资助的研究者， 以神经科学为重点的研究计划，针对易怒的发展轨迹。实现这一 PI的目标是扩大她以前的培训，以支持先进的，独立的研究休息 状态和任务为基础的功能磁共振成像和模式分类技术的使用，以及他们的整合， 单变量方法，适用于不同程度的刺激性样本。这一培训目标直接 支持PI的研究目标，这是确定易怒和神经的神经机制， 使用MVPA和单变量成像的组合预测烦躁随时间的变化的相关性 方法适用于基于任务和静息状态的fMRI数据。核心假设，基于PI的飞行员 数据，是在挫折任务期间的活动模式，沿着休息时的连接模式，将预测水平 和变化，变化。单变量和MVPA方法将收敛于类似的结果; 然而，MVPA将识别预测易怒或 易怒该奖项的培训目标是提高PI的研究和专业技能， 三个领域的知识：（1）临床损害易怒的表型;（2）基于任务和静息状态的功能磁共振成像 方法、设计、预处理和先进的MVPA技术，即，机器学习用于分析fMRI 数据，以及它们与单变量分析的整合;（3）成功的专业技能 在研究型大学担任教员的研究生涯。通过完成拟议的研究和 培训，PI期望获得足够的培训，以作为独立的发展情感 神经科学家，以及足够的试点数据提交竞争R 01申请，旨在检查预测 儿童易激惹的发展变化在神经回路的多层次分析中， 行为、社会经验、环境因素。