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 分析灵活性对脑行为关系和青少年的预测模型的影响 社会情感处理尚不清楚。经常成熟的不平衡（或双重系统）模型 缺乏可靠性和普遍性。现有工作主要集中在单个任务设计和小型上 样本（中位数<50）使用不同 奖励和情感处理。拟议的研究将整合三个发展功能磁性 共振成像（fMRI）样品（n〜105; n〜180; n〜7,000），具有类似的奖励和情感 范式，调查与可重复性和概括性有关的关键问题：（a）分析的影响 任务之间/对比度的对比度的脑行为关联以及收敛性和预测有效性的灵活性 域； （b）可以使用基于任务的fMRI（T-FMRI）大脑特征（潜在神经特征） 作为跨多个样本的强大脑行为预测模型的基础。假设T-FMRI 对比可以在注意力和价值的多维平面上分开，这引起了神经 响应导致方法或避免。但是，研究人员如何运作正面和负面 T-FMRI中的价通常会有所不同，决策过程中的这种可变性可能会影响基础 神经效应。 AIM 1A将检查基于分析的给定任务更改中的脑行为关联如何 与拟合通用线性模型（GLM），对比度和神经区域有关的决定。然后，AIM 1B会考虑 脑行为关联的变化（作为分析灵活性的功能）是否反映在变化中 构建方法的有效性和避免任务内和任务之间的范围，例如奖励和情感 加工。相反，传统的单变量GLM方法显示重新测试可靠性中的越来越多的问题 并表达可能不支持行为表型的可推广预测的关联。但是， 神经发育文献提出，多元分析利用降低维度的分析 机器学习可以提供信息丰富的脑行为预测模型。为了检验这一假设，目的 2，降低降低将在大型青少年T-FMRI样本中用于产生脑行为预测 模型并在奖励和情感任务中进行比较，以考虑结构的影响。 AIM 3将集中精力 关于代码和fMRI统计图的传播。奖学金将支持申请人的增长 通过提供培训，成为神经发育神经科学的独立研究人员和领导者 在：结合T-FMRI数据集，评估fMRI中的分析灵活性和对构建体有效性的影响， 在神经发育样本中施加尺寸降低以产生脑行为预测模型。 该培训将支持申请人理解青少年神经机制的长期目标 使用物质并改善我们对传统和非传统测量模型的理解。