Improving the Measurement of Brain-Behavior Associations in Adolescence

改善青春期大脑行为关联的测量

• 批准号: 10525501
• 负责人: Michael Demidenko
• 金额: $ 6.95万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10525501
• 关键词: Adolescence; Adolescent; Adolescent Risk Behavior; Affect; Affective; Algorithms; Attention; Behavior; Brain; Brain region; Characteristics; Code; Cognitive; Communities; Data; Data Analyses; Data Set; Decision Making; Detection; Development; Dimensions; Fellowship; Freedom; Functional Magnetic Resonance Imaging; Goals; Growth; Intervention; Linear Models; Literature; Machine Learning; Maps; Measurement; Measures; Methodology; Methods; Modeling; Modernization; Motivation; Multivariate Analysis; Negative Valence; Neurosciences; Output; Policies; Positive Valence; Preparation; Process; Protocols documentation; Reproducibility; Reproduction; Research; Research Personnel; Resources; Rewards; Sampling; System; Techniques; Testing; Training; Validation; Variant; Work; adolescent substance use; base; behavior prediction; behavioral phenotyping; brain behavior; brain magnetic resonance imaging; cognitive neuroscience; contrast imaging; design; flexibility; high dimensionality; improved; interest; neuroimaging; neuromechanism; open data; predictive modeling; relating to nervous system; response; substance use; two-dimensional

Project Abstract The effect of analytic flexibility on brain-behavior relationships and predictive models of adolescent socioemotional processing is not well understood. The Maturational Imbalance (or Dual System) Model often lacks reliability and generalizability. Existing work has predominately focused on single task-designs and small samples (median < 50) concentrating on brain-behavior associations using disparate operationalizations of reward and affective processing. The proposed research will integrate three developmental functional magnetic resonance imaging (fMRI) samples (N ~ 105; N ~ 180; N ~ 7,000), with analogous reward and affective paradigms, to investigate key issues related to reproducibility and generalizability: (a) the influence of analytic flexibility on brain-behavior associations and convergence and predictive validity in contrasts within/between task domains; and (b) uncovering task-based fMRI (t-fMRI) brain features (latent neural characteristics) that can serve as the basis for robust brain-behavior prediction models across multiple samples. It is hypothesized that t-fMRI contrasts can be separated across a multidimensional plane of attention and valence, which elicits neural responses leading to approach or avoidance. However, how researchers operationalize positive and negative valence in t-fMRI often varies, and this variability in the decision-making process may influence the underlying neural effects. Aim 1a will examine how brain-behavior associations in a given task change based on analytic decisions relating to fitting general linear models (GLM), contrasts and neural regions. Then, Aim 1b will consider whether changes in brain-behavior associations (as a functional of analytic flexibility) are reflected in changes in construct validity of approach and avoidance within- and between-task domains, such as reward and affective processing. Conversely, traditional univariate GLM approaches show mounting issues in test-retest reliability and express associations that may not support generalizable prediction of behavioral phenotypes. However, the neurodevelopmental literature has proposed that multivariate analyses that leverage dimensionality reduction and machine learning can provide informative brain-behavior prediction models. To test this hypothesis, in Aim 2, dimensionality reduction will be used in a large adolescent t-fMRI sample to generate brain-behavior prediction models and compared across a reward and affective task to consider the influence of constructs. Aim 3 will focus on the dissemination of code and fMRI statistical maps. The fellowship will support the applicant's growth in becoming an independent researcher and leader in the neurodevelopmental neuroscience by providing training in: combining t-fMRI datasets, evaluating the effect of analytic flexibility in fMRI and impact on construct validity, applying dimensionality reduction in neurodevelopmental samples to produce brain-behavior prediction models. This training will support the applicant's long-term goals of understanding of neural mechanisms in adolescent substance use and improving our understanding of traditional and non-traditional measurement models.

项目摘要 分析灵活性对青少年脑-行为关系的影响及预测模型 社会情绪处理还没有被很好地理解。不平衡（或二元系统）模型通常 缺乏可靠性和普遍性。现有的工作主要集中在单一的任务设计和小 样本（中位数< 50）集中在大脑行为协会使用不同的操作， 奖励和情感处理。拟议的研究将整合三个发展功能磁 共振成像（fMRI）样本（N ~ 105; N ~ 180; N ~ 7，000），具有类似的奖励和情感 范式，调查有关的重现性和概括性的关键问题：（a）分析的影响， 脑行为关联的灵活性和任务内/任务间对比的收敛性和预测效度 （B）揭示基于任务的功能磁共振成像（t-fMRI）脑特征（潜在神经特征）， 作为跨多个样本的强大大脑行为预测模型的基础。假设t-fMRI 对比可以在注意力和效价的多维平面上分开，这有助于神经系统的发展。 导致接近或回避的反应。然而，研究人员如何操作积极和消极的 t-fMRI中的效价经常变化，这种决策过程的可变性可能会影响潜在的 神经效应目标1a将研究在给定的任务中，大脑行为的关联是如何根据分析结果而改变的。 与拟合一般线性模型（GLM）、对比度和神经区域相关的决策。目标1b将考虑 大脑行为关联的变化（作为分析灵活性的功能）是否反映在 任务域内和任务域间的接近和回避的结构效度，如奖励和情感 处理.相反，传统的单变量GLM方法在重测信度方面显示出越来越多的问题 并且表达可能不支持行为表型的可概括预测的关联。但 神经发育文献提出，利用降维的多变量分析 机器学习可以提供信息丰富的大脑行为预测模型。为了验证这一假设， 2，降维将用于大型青少年t-fMRI样本中，以生成大脑行为预测 模型，并在奖励和情感任务进行比较，以考虑结构的影响。目标3将重点 关于代码和功能磁共振成像统计图的传播。奖学金将支持申请人的成长， 通过提供培训，成为神经发育神经科学的独立研究者和领导者 在：结合t-fMRI数据集，评估fMRI分析灵活性的效果和对结构效度的影响， 在神经发育样本中应用降维来产生大脑行为预测模型。 这项培训将支持申请人理解青少年神经机制的长期目标。 物质使用和提高我们对传统和非传统测量模型的理解。