Brain Network Maturation and Executive Dysfunction Spanning Diagnostic Categories of Psychopathology

脑网络成熟和执行功能障碍跨越精神病理学的诊断类别

• 批准号: 10394757
• 负责人: Dale Zhou
• 金额: $ 3.41万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10394757
• 关键词: Address; Adolescent; Affect; Age; Anterior; Attention; Attention deficit hyperactivity disorder; Brain; Brain region; Categories; Clinical assessments; Communication; Computer Models; Data; Data Analyses; Data Set; Development; Developmental Delay Disorders; Diagnosis; Diagnostic; Disease; Dorsal; Ensure; Executive Dysfunction; Future; Gaussian model; Goals; Health; Individual; Individual Differences; Information Theory; Insula of Reil; Learning; Link; Longitudinal Studies; Machine Learning; Mathematics; Measures; Memory; Mental disorders; Mentors; Mentorship; Methods; Microscopic; Modeling; Nature; Neurons; Neurosciences; Parietal Lobe; Participant; Pathway interactions; Pattern; Personal Satisfaction; Persons; Philadelphia; Population Study; Prefrontal Cortex; Process; Psychiatry; Psychopathology; Psychoses; Risk; Sampling; Schizophrenia; Science; Speed; Structure; Synaptic Transmission; Techniques; Testing; Time; Training; Variant; Visit; Youth; age related; base; brain abnormalities; career; cingulate cortex; cognitive ability; cognitive neuroscience; cognitive testing; cohort; connectome; executive function; individualized medicine; inter-individual variation; large scale data; machine learning model; machine learning prediction; mental development; neurodevelopment; neuroimaging; neuropsychiatric disorder; neuropsychiatry; neurotransmission; novel; novel strategies; precision medicine; predictive modeling; relating to nervous system; theories; training opportunity; transmission process; white matter

PROJECT SUMMARY In the healthy brain, white matter connections coordinate the speed and reliability of communication among spatially distributed regions. The transmission of neural information with fine temporal precision is crucial for executive function (EF), defined as the coordination of attention, memory, and inhibitory control for goal-directed actions crucial for survival, health, and well-being. Notably, EF undergoes protracted development throughout youth, and the development of executive deficits is present in many psychiatric illnesses, including schizophrenia and ADHD. However, the degree to which transdiagnostic executive deficits arise due to abnormal development of white matter networks remains unknown. Existing neuroimaging studies of transdiagnostic executive deficits have been limited by 1) inadequate methods to assess neurodevelopmental patterns of spatially distributed brain regions, and 2) inadequate theories of neural communication at the level of macro-scale structural networks. Here we address the latter challenge by generalizing long-established theoretical principles and empirical observations of efficient neurotransmission at the microscale neuron level to the macroscale structural connectome by calculating transmission fidelity. Transmission fidelity models how efficiently a brain region embedded in the structural network can transmit messages to a target region with individually differing speed and reliability. Moreover, we address the former challenge by capitalizing on advances in network science and machine learning. Utilizing these methods is a promising direction for investigating abnormal brain development. In this proposal, we describe the application of a novel analysis of structural network maturation to study the manifestation of transdiagnostic executive deficits. Using a large sample of youth who completed cross-sectional neuroimaging as part of the Philadelphia Neurodevelopmental Cohort (n=1,042), our preliminary analyses demonstrate previously uncharacterized relationships between brain structural connectivity and EF across age. We will use an additional cross-sectional dataset enriched with youths presenting with psychopathology acquired through the Healthy Brain Network study (n=5,000) to generalize our findings to executive dysfunction. To quantify developmental norms, we will capitalize on our large-scale datasets to train a machine learning model that predicts expected ranges of healthy brain development. Furthermore, understanding within-person changes in brain structure and EF necessitates longitudinal data. We will longitudinally assess youths diagnosed with ADHD (n=50) or schizophrenia (n=50), and typically developing comparators (n=40). In this proposal, we aim to 1) delineate how age-related variation of brain network transmission fidelity is associated with executive dysfunction across psychiatric disorders, and 2) determine how within-individual development in transmission fidelity is associated with executive dysfunction in ADHD and the psychosis spectrum. A better understanding of atypical inter-individual and within-individual developmental patterns of brain networks provides a framework towards precision medicine in neuropsychiatry.

项目摘要 在健康的大脑中，白色物质的连接协调了大脑之间的通信速度和可靠性。 空间分布区域。具有良好时间精度的神经信息传输对于 执行功能（EF），定义为协调注意力，记忆和抑制控制目标导向 对生存、健康和福祉至关重要的行动。值得注意的是，EF经历了长期的发展， 青少年，和执行缺陷的发展是目前在许多精神疾病，包括精神分裂症 ADHD。然而，在何种程度上跨诊断执行缺陷出现由于异常发展， 白色物质网络的结构仍然未知。跨诊断执行缺陷的现有神经影像学研究 1）评估空间分布大脑的神经发育模式的方法不足 区域，和2）在宏观尺度结构网络水平上的神经通信理论不足。 在这里，我们通过概括长期建立的理论原则和经验， 从微观神经元水平到宏观结构水平的有效神经传递的观察 计算传输保真度。传输保真度模拟了大脑区域 嵌入在结构网络中的信息可以以各自不同的速度传输到目标区域 和可靠性此外，我们通过利用网络科学的进步来应对前一个挑战， 机器学习利用这些方法是研究异常脑发育的一个有前途的方向。 在这个建议中，我们描述了一种新的结构网络成熟分析的应用，以研究 跨诊断执行缺陷的表现。使用了一个大样本的青年谁完成了横截面 我们的初步分析显示，神经影像学作为费城神经发育队列（n= 1，042）的一部分 证明了大脑结构连接和EF之间在年龄上的关系。 我们将使用一个额外的横断面数据集，其中包含患有精神病理学的青少年， 通过健康大脑网络研究（n= 5，000）将我们的发现推广到执行功能障碍。到 量化发展规范，我们将利用我们的大规模数据集来训练机器学习模型 预测健康大脑发育的预期范围。此外，了解个人内部的变化 脑结构和EF需要纵向数据。我们将纵向评估被诊断为 ADHD（n=50）或精神分裂症（n=50），以及典型的对照药物（n=40）。在本建议中，我们的目标是 1)描述大脑网络传输保真度的年龄相关变化如何与执行 精神疾病之间的功能障碍，以及2）确定如何在个体内发展， 传输保真度与ADHD和精神病谱系中的执行功能障碍有关。一 更好地理解大脑网络的非典型个体间和个体内发展模式 为神经精神病学的精准医疗提供了一个框架。