Between- and Within-Person Heterogeneity in Adolescent Resting State Networks: Associations with Internalizing Psychopathology

青少年静息状态网络中人与人之间的异质性：与内化精神病理学的关联

• 批准号: 10749362
• 负责人: Matthew Mattoni
• 金额: $ 3.6万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10749362
• 关键词: 12 year old; Address; Adolescence; Adolescent; Anxiety; Applied Research; Behavioral; Biological Markers; Brain; Clinical; Collaborations; Control Groups; Data; Data Aggregation; Dependence; Depressed mood; Development; Diagnosis; Diffuse; Disease; Educational workshop; Foundations; Functional Magnetic Resonance Imaging; Goals; Health; Heterogeneity; Individual; Individual Differences; Intervention; Life; Machine Learning; Mental Depression; Methods; Modeling; Neurobiology; Neurophysiology - biologic function; Neurosciences; Outcome; Pattern; Persons; Process; Prognosis; Property; Psychopathology; Research; Research Activity; Research Personnel; Research Proposals; Resources; Rest; Rewards; Risk; Sample Size; Sampling; Scanning; Subgroup; Testing; Time; Training; Work; biomarker identification; career; child depression; clinical diagnosis; clinical translation; cognitive development; functional disability; imaging study; improved; interest; machine learning method; network models; neural; outcome prediction; person centered; societal costs; tool; trait; translational barrier

PROJECT SUMMARY/ABSTRACT Adolescence is a key risk period for depression and anxiety, and adolescent-onset psychopathology is predictive of poorer health and life outcomes. Functional connectivity (FC) networks, which reflect trait-like brain functioning, have emerged as a promising biomarker to inform diagnosis and intervention of psychopathology. However, despite findings of FC differences between clinical and control groups, particularly in resting state (RS) networks, there has been minimal clinical translation. A key limitation is that qualitative network heterogeneity between- and within-individuals threatens the ability to draw inferences valid at the individual level. At the between-person level, qualitatively distinct networks across individuals limit the ability of group-averaged networks to validly reflect each individual. If group-level networks do not reflect individuals, behavioral inferences drawn from them will not apply to the individual. Our preliminary work and previous precision imaging studies provide evidence of this limitation by demonstrating FC network heterogeneity across individuals. However, the generalizability of group networks to individuals is yet to be tested empirically. This proposal will assess group-to-individual generalizability of adolescent RS networks and examine the ability of data-driven subgroups of similar individuals to address the limitation of heterogeneity (Aim 1). At the within-person level, FC variability across a single scan also threatens the validity of FC networks. If FC means and covariances vary with time across a scan (i.e., are not stationary), a static (time-invariant) network would not validly reflect network processes across the scan. This proposal will estimate dynamic FC to assess stationarity of adolescent RS networks to determine the validity of static networks (Aim 2). For both aims, this proposal will use a large 11–12-year-old sample from the Adolescent Brain Cognitive Development study. The proposal will determine the levels of data aggregation (group, subgroup, or individual) and time precision (static or dynamic) necessary for individual-level inferences from FC networks. Findings will be critical for the ultimate goal of using FC networks for clinical translation, which requires individual-level prediction. We will then use RS network features that are precise to individuals to predict depression and anxiety outcomes in adolescents (Aim 3), building a foundation with increased potential for clinical translation. The training plan to achieve the proposed project was developed in collaboration with a team of relevant experts that consists of formal coursework, workshops, and applied research activities. Specifically, I will develop expertise in fMRI research and analysis methods, machine learning approaches for subgroup identification, and idiographic (person- centered) methods necessary to complete the research proposal. Training will emphasize development toward my goal of becoming an independent investigator in clinical neuroscience who studies individually precise associations between neural functioning and adolescent psychopathology.

项目概要/摘要 青春期是抑郁和焦虑的关键危险期，青少年发病的精神病理学是 预测较差的健康和生活结果，反映了类似特征的功能连接（FC）网络。 大脑功能已成为一种有前途的生物标志物，可为诊断和干预提供信息 然而，尽管临床组和对照组之间的 FC 存在差异，特别是 在静息状态（RS）网络中，临床上的翻译很少，一个关键的限制是定性的。 个体之间和个体内部的网络异质性威胁到在个体中做出有效推论的能力 在个人层面上，不同个体之间的网络质量不同，限制了个体的能力。 群体平均网络能够有效地反映每个个体如果群体层面的网络不能反映个体， 从他们身上得出的行为推论不适用于我们的初步工作和之前的工作。 精密成像研究通过证明 FC 网络异质性提供了这种局限性的证据 然而，群体网络对个人的普遍性还有待实证检验。 该提案将评估青少年 RS 网络的群体到个人的普遍性，并检查 类似个体的数据驱动子组解决异质性限制的能力（目标 1）。 在人内层面，单次扫描的 FC 变异性也威胁着 FC 网络的有效性。 并且协方差随着扫描时间的推移而变化（即，不是静止的），静态（时不变）网络将 无法有效地反映整个扫描中的网络进程。该提案将估计动态 FC 来评估。 青少年 RS 网络的平稳性来确定静态网络的有效性（目标 2）。 该提案将使用青少年大脑认知发展研究中的 11-12 岁儿童大样本。 提案将确定数据聚合的级别（组、子组或个体）和时间精度（静态 或动态）对于 FC 网络的个体层面的推论是必要的。结果对于最终的结果至关重要。 使用 FC 网络进行临床翻译的目标，这需要个体级别的预测。 RS 网络特征可精确预测青少年的抑郁和焦虑结果 （目标 3），建立具有更大临床转化潜力的基础 培训计划，以实现这一目标。 拟议的项目是与相关专家小组合作开发的，该小组由正式的 具体来说，我将发展功能磁共振成像研究方面的专业知识。 和分析方法、用于亚组识别的机器学习方法以及具体的（个人） 中心）完成研究计划所需的方法，培训将强调朝着发展方向发展。 我的目标是成为临床神经科学领域的独立研究者，研究个体精确性 神经功能与青少年精神病理学之间的关联。