This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

中文翻译：

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动态滞后分析揭示了自闭症谱系障碍中的非典型大脑信息流。

这项研究通过应用一种称为动态滞后分析（DLA）的新技术，研究了神经型（NT）个体和自闭症谱系障碍（ASD）个体之间的全脑动态滞后模式变化。重复时间= 100毫秒的3D磁共振脑电图数据的使用可以对大脑网络之间的活动分布进行高度准确的分析。在NT个体（n = 20）和ASD个体（n= 20）进行了分析。使用DLA研究了每个RSN对之间的动态滞后模式变化，该方法测量了每个RSN对组合之间的时间滞后变化，并统计地定义了这些滞后模式如何在ASD和NT组之间改变。DLA分析表明，在120个RSN对中，有10.8％具有统计学意义（P-值<0.003）动态滞后模式差异在替代数据阈值校正后仍可幸存。滞后模式的变化主要集中在显着，执行，视觉和默认模式网络上，从而支持了在ASD中这些区域大脑连接能力受损的早期发现。分别有92.3％和84.6％的显着RSN分别显示ASD和NT的平均时滞和中位时滞较短。两者合计，这些结果表明，改变的滞后模式表明大型功能性脑网络之间的活动的非典型扩散可能有助于ASD表型。Autism Res 2020，13：244-258。©2019作者。由Wiley Periodicals，Inc.出版的国际自闭症研究协会出版的《自闭症研究》。