Multimodal Brain Connectivity, Baseline Characteristics, and Executive Function in Preterm Infants

Overview<br>This dataset comprises multiple components that capture non-negative matrix factorization (NMF) outputs, graph-theoretical metrics, clinical confounders, and executive function (EF) scores in a cohort of very preterm infants (≤32 weeks’ gestation). The data are organized into several files, each reflecting a different level of analysis or data type.1. All SC FeaturesContent: Comprehensive structural connectivity (SC) metrics (e.g., average path length (ASP), betweenness centrality (BC), clustering coefficient (CC), cost (CO), local efficiency (LE), and regional degree (RD)) derived from the diffusion MRI (dMRI) data.Purpose: Enables replication or secondary analysis of the graph-theoretical metrics before feature selection and robust regression modeling.2. All FC FeaturesContent: Comprehensive functional connectivity (FC) metrics for the same graph-theoretical measures (ASP, BC, CC, CO, LE, RD) derived from resting-state fMRI data.Purpose: Similarly provides the complete set of functional connectivity metrics for analyses parallel to the SC data.3. Baseline CharacteristicsContent: Clinical and demographic data for each infant, including key confounders such as gestational age (GA), antenatal corticosteroids, retinopathy of prematurity, bronchopulmonary dysplasia, maternal age, etc.Purpose: Allows researchers to control for or stratify by these confounders when examining the relationship between brain connectivity and developmental outcomes.4. EF ScoreContent: The Minnesota Executive Function Scale (MEFS) standard scores measured at three years corrected age (CA) for each participant.Purpose: Serves as the dependent variable in analyses linking SC/FC metrics to EF outcomes in preterm infants.5. FC Top Vars &amp; SC Top VarsContent: The subsets of functional (FC) and structural (SC) variables that were selected via LASSO for robust regression modeling (e.g., top variables contributing significantly to EF prediction).Purpose: Facilitates focus on the key connectivity metrics (from NMF-derived modes of inter-subject covariation) that emerged as predictive of EF, rather than the full set of metrics.6. Top Variable Scores for Both FC and SCContent: Individual-level data reflecting the highest-contributing FC and SC graph metrics across the main categories (ASP, BC, CC, CO, LE, RD).Purpose: Allows a direct comparison of the crucial graph-theoretical measures— both structural and functional—that appear most relevant to EF outcomes.Usage NotesNMF Outputs: These are “modes of inter-subject covariation,” capturing overlapping patterns of connectivity across participants, not canonical resting-state networks.Confounder Control: Data on baseline characteristics enable robust multivariable modeling.EF Score: The age-corrected MEFS standard scores are suitable for early childhood EF assessment, offering a unidimensional measure of executive function.Reproducibility: Researchers can replicate or extend the original analyses by merging the “All SC Features” / “All FC Features” with the “Baseline Characteristics” and “EF Score,” then focusing on “Top Vars” if they aim to confirm or refine the robust regression results.By sharing these data files, we hope to support secondary analysis, replication of our results, and further exploration of how early brain connectivity relates to later cognitive outcomes in this vulnerable preterm population.

概述<br>本数据集包含多个组件，可捕捉非负矩阵分解（NMF）输出、图论指标、临床混杂因素以及极早产儿（胎龄≤32周）队列中的执行功能（EF）评分。数据被组织为多个文件，每个文件反映不同的分析层次或数据类型。<br>1. 全结构连接特征<br>内容：从扩散磁共振成像（dMRI）数据中提取的全面结构连接（SC）指标（例如平均路径长度（ASP）、介数中心性（BC）、聚类系数（CC）、成本（CO）、局部效率（LE）和区域度（RD））。<br>目的：支持特征选择和稳健回归建模前的图论指标复制或二次分析。<br>2. 全功能连接特征<br>内容：基于静息态功能磁共振成像数据得到的、针对相同图论指标（ASP、BC、CC、CO、LE、RD）的全面功能连接（FC）指标。<br>目的：为与SC数据平行的分析提供完整的功能连接指标集。<br>3. 基线特征<br>内容：每位婴儿的临床和人口统计学数据，包括关键混杂因素如胎龄（GA）、产前皮质类固醇、早产儿视网膜病变、支气管肺发育不良、母亲年龄等。<br>目的：允许研究者在考察脑连接与发育结局关系时控制或按这些混杂因素分层。<br>4. 执行功能（EF）评分<br>内容：每位参与者在矫正年龄3岁时测得的明尼苏达执行功能量表（MEFS）标准分。<br>目的：作为将SC/FC指标与早产儿EF结局关联分析中的因变量。<br>5. 功能连接与结构连接关键变量<br>内容：通过LASSO选择用于稳健回归建模的功能连接（FC）和结构连接（SC）变量子集（例如对EF预测有显著贡献的关键变量）。<br>目的：便于聚焦于（从NMF衍生的受试者间协变模式中）成为EF预测因子的关键连接指标，而非全部指标。<br>6. 功能连接与结构连接的关键变量评分<br>内容：反映各主要类别（ASP、BC、CC、CO、LE、RD）中贡献最高的FC和SC图指标的个体水平数据。<br>目的：允许直接比较与EF结局最相关的关键图论指标——无论是结构还是功能层面的。<br>使用说明<br>NMF输出：这些是“受试者间协变模式”，捕捉参与者间连接的重叠模式，而非典型静息态网络。<br>混杂因素控制：基线特征数据支持稳健多变量建模。<br>EF评分：年龄矫正的MEFS标准分适用于幼儿EF评估，提供执行功能的单维度测量。<br>可重复性：研究者可通过合并“全SC特征”/“全FC特征”与“基线特征”及“EF评分”来复制或扩展原始分析；若旨在验证或优化稳健回归结果，则可聚焦于“关键变量”。<br>通过共享这些数据文件，我们希望支持二次分析、结果复制以及进一步探索极早产儿早期脑连接与后期认知结局的关系。