Precision mapping of individualized executive networks in youth

摘要 执行功能（EF）在儿童和青少年时期显着改善，EF失败 与广泛的负面结果和各种精神疾病有关。的脑回路 负责EF的空间分布，包括额顶叶，扣带盖，和突出 系统.这些网络通常使用标准化的网络地图集进行研究，这些地图集假设 在个体之间的结构和功能神经解剖学之间进行简单的映射。然而，在这方面， 最近，使用精确功能映射技术在成人中进行的多项独立研究表明， 表明，有显着的个体间的变化，在功能地形，这是定义为 皮层功能网络的空间分布。这一建议的过度假设是， 功能地形图的个体差异是青年EF的关键决定因素。我们的合作团队 最近发表了第一份报告的个性化功能网络的儿童使用的横截面 样品（Cui等人，神经元2020）。在本提案中，我们将在这一初步工作的基础上， 使用两个具有高分辨率成像的大型横截面数据集来概括这一发现： 脑网络（HBN; n= 5，000）和人类连接组项目：开发（HCP-D，n= 1，300）。关键是， 我们还将利用青少年大脑和认知发展研究的前所未有的资源， (ABCD，n= 11，572）来描绘个性化网络中的受试者内变化。在这份提案中，我们将首先 使用最初为统计而开发的先进技术协调这些海量数据资源 基因组学（Aim 1）。接下来，我们将描述个性化网络如何随年龄（目标2）而演变，并预测EF (Aim 3）。最后，我们将使用机器学习工具来发现如何个性化的功能拓扑结构， 执行网络以数据驱动的方式预测精神病理学的维度（探索目标4）。 在整个过程中，我们将坚持开放科学的最佳实践，以最大限度地提高可重复性，并确保所有 处理后的数据、代码和结果与神经科学界公开共享。在一起，这 研究将确定功能地形图对于理解EF是必不可少的，并将激励 个性化的神经调节疗法

ABSTRACT Executive function (EF) improves dramatically during childhood and adolescence, and failures of EF are associated with both a broad range of negative outcomes and diverse mental illnesses. The brain circuits responsible for EF are spatially distributed, and include the fronto-parietal, cingulo-opercular, and salience systems. These networks have typically been studied using standardized network atlases, which assume a straightforward mapping between structural and functional neuroanatomy across individuals. However, multiple independent efforts in adults using precision functional mapping techniques have recently demonstrated that there is marked inter-individual variation in functional topography, which is defined as the spatial distribution of functional networks on the cortex. The over-arching hypothesis of this proposal is that individual variation in functional topography is a critical determinant of EF in youth. Our collaborative team recently published the first report of individualized functional networks in children using a cross-sectional sample (Cui et al., Neuron 2020). In this proposal, we will build upon this initial work by replicating and generalizing this finding using two large cross-sectional datasets with high-resolution imaging: the Healthy Brain Network (HBN; n=5,000) and Human Connectome Project: Development (HCP-D, n=1,300). Critically, we will also leverage the unprecedented resources of the Adolescent Brain and Cognitive Development Study (ABCD, n=11,572) to delineate within-subject change in personalized networks. In this proposal, we will first harmonize these massive data resources using advanced techniques originally developed for statistical genomics (Aim 1). Next, we will describe how personalized networks evolve with age (Aim 2) and predict EF (Aim 3). Finally, we will use machine learning tools to discover how the functional topography of personalized executive networks predict dimensions of psychopathology in a data-driven manner (Exploratory Aim 4). Throughout, we will adhere to best practices of open science to maximize reproducibility, and ensure that all processed data, code, and results are openly shared with the neuroscience community. Together, this research will establish that functional topography is essential for understanding EF, and will motivate trials of personalized neuromodulatory therapies.