静息状态功能连接性可预测儿童的注意力问题:来自 ABCD 研究的证据

IF 1.6 Q3 CLINICAL NEUROLOGY NeuroSci Pub Date : 2024-10-12 eCollection Date: 2024-12-01 DOI:10.3390/neurosci5040033
Kelly A Duffy, Nathaniel E Helwig
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引用次数: 0

摘要

注意缺陷/多动障碍(ADHD)是一种常见的神经发育障碍,与对照组相比,ADHD 患者的大脑在功能和结构上存在许多差异。本研究使用的数据来自大型流行病学信息青少年大脑认知发展研究的基线样本(样本数=7979),该研究的对象是9-10岁的儿童。交叉验证的泊松弹性网回归模型用于根据网内和网间静息状态相关性以及几个已知的风险因素(如生理性别、社会经济地位以及父母的酗酒和吸毒史)预测多动症症状的维度测量。我们发现,父母吸毒史和生理性别是预测注意力问题的最重要因素。默认模式网络和背侧注意力网络之间的连接是唯一一个被认为对预测注意力问题有重要作用的大脑网络。具体来说,我们发现默认模式网络和背侧注意力网络之间的反相关性降低与儿童注意力问题的增加有关。我们的发现补充并扩展了近期的研究,这些研究将结构性和任务型fMRI的个体差异与多动症症状联系起来,将静息态fMRI的个体差异与多动症诊断联系起来。
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Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study.

Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, and numerous functional and structural differences have been identified in the brains of individuals with ADHD compared to controls. This study uses data from the baseline sample of the large, epidemiologically informed Adolescent Brain Cognitive Development Study of children aged 9-10 years old (N = 7979). Cross-validated Poisson elastic net regression models were used to predict a dimensional measure of ADHD symptomatology from within- and between-network resting-state correlations and several known risk factors, such as biological sex, socioeconomic status, and parental history of problematic alcohol and drug use. We found parental history of drug use and biological sex to be the most important predictors of attention problems. The connection between the default mode network and the dorsal attention network was the only brain network identified as important for predicting attention problems. Specifically, we found that reduced magnitudes of the anticorrelation between the default mode and dorsal attention networks relate to increased attention problems in children. Our findings complement and extend recent studies that have connected individual differences in structural and task-based fMRI to ADHD symptomatology and individual differences in resting-state fMRI to ADHD diagnoses.

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