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自闭症患儿大脑节律加速成熟

Accelerated Infant Brain Rhythm Maturation in Autism.

作者信息

Dickinson Abigail, McDonald Nicole, Dapretto Mirella, Campos Emilie, Senturk Damla, Jeste Shafali

机构信息

Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California, USA.

Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, California, USA.

出版信息

Dev Sci. 2025 Jan;28(1):e13593. doi: 10.1111/desc.13593.

Abstract

Electroencephalography (EEG) captures characteristic oscillatory shifts in infant brain rhythms over the first year of life, offering unique insights into early functional brain development and potential markers for detecting neural differences associated with autism. This study used functional principal component analysis (FPCA) to derive dynamic markers of spectral maturation from task-free EEG recordings collected at 3, 6, 9, and 12 months from 87 infants, 51 of whom were at higher likelihood of developing autism due to an older sibling diagnosed with the condition. FPCA revealed three principal components explaining over 96% of the variance in infant power spectra, with power increases between 6 and 9 Hz (FPC1) representing the most significant age-related trend, accounting for more than 71% of the variance. Notably, this oscillatory change occurred at a faster rate in infants later diagnosed with autism, indicated by a steeper trajectory of FPC1 scores between 3 and 12 months (p < 0.001). Age-related spectral changes were consistent regardless of familial likelihood status, suggesting that differences in oscillatory timing are associated with autism outcomes rather than genetic predisposition. These findings indicate that while the typical sequence of oscillatory maturation is preserved in autism, the timing of these changes is altered, underscoring the critical role of timing in autism pathophysiology and the development of potential screening tools.

摘要

脑电图(EEG)记录了婴儿出生后第一年大脑节律中特征性的振荡变化,为早期功能性脑发育以及检测与自闭症相关的神经差异的潜在标志物提供了独特的见解。本研究使用功能主成分分析(FPCA)从87名婴儿在3、6、9和12个月时采集的静息态EEG记录中得出频谱成熟的动态标志物,其中51名婴儿由于其年长同胞被诊断患有自闭症而患自闭症的可能性更高。FPCA揭示了三个主成分,解释了婴儿功率谱中超过96%的方差,6至9赫兹(FPC1)之间的功率增加代表了最显著的与年龄相关的趋势,占方差的71%以上。值得注意的是,这种振荡变化在后来被诊断为自闭症的婴儿中发生得更快,表现为3至12个月期间FPC1分数的轨迹更陡峭(p < 0.001)。无论家族患病可能性如何,与年龄相关的频谱变化都是一致的,这表明振荡时间的差异与自闭症结局相关,而非遗传易感性。这些发现表明,虽然自闭症中振荡成熟的典型顺序得以保留,但这些变化的时间发生了改变,强调了时间在自闭症病理生理学以及潜在筛查工具开发中的关键作用。

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