Suppr超能文献

静息态功能连接可预测儿童的注意力问题:来自青少年大脑认知发展研究(ABCD研究)的证据。

Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study.

作者信息

Duffy Kelly A, Helwig Nathaniel E

机构信息

Department of Psychology, University of Minnesota, 75 E River Road, Minneapolis, MN 55455, USA.

School of Statistics, University of Minnesota, 224 Church Street SE, Minneapolis, MN 55455, USA.

出版信息

NeuroSci. 2024 Oct 12;5(4):445-461. doi: 10.3390/neurosci5040033. eCollection 2024 Dec.

Abstract

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 ( = 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.

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,已在ADHD患者的大脑中发现了众多功能和结构差异。本研究使用了来自大型、具有流行病学依据的9至10岁儿童青少年大脑认知发展研究基线样本的数据(n = 7979)。交叉验证的泊松弹性网络回归模型用于根据网络内和网络间静息状态相关性以及几个已知风险因素(如生物性别、社会经济地位以及父母有问题的酒精和药物使用史)来预测ADHD症状的维度测量值。我们发现父母的药物使用史和生物性别是注意力问题的最重要预测因素。默认模式网络与背侧注意网络之间的连接是唯一被确定对预测注意力问题很重要的脑网络。具体而言,我们发现默认模式网络与背侧注意网络之间反相关程度的降低与儿童注意力问题的增加有关。我们的研究结果补充并扩展了最近将基于结构和任务的功能磁共振成像中的个体差异与ADHD症状学以及静息态功能磁共振成像中的个体差异与ADHD诊断联系起来的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/327c/11503400/2f87102614b0/neurosci-05-00033-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验