静息状态QEEG神经生物标志物对自闭症谱系障碍诊断和治疗计划的影响

A. Al-Salihy
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引用次数: 1

摘要

背景:自闭症谱系障碍(ASD)是一种复杂神经发育障碍的组合。自闭症的早期静息状态脑电图调查未能确定非典型神经活动的一致模式。ASD中u型电生理功率变化的证据主要是支持性的,但需要更多的假设驱动的努力来确认和验证它。研究目的:本研究的主要目的是通过振幅分析来研究静息状态QEEG神经生物标志物作为自闭症儿童的诊断工具,并与正常组在睁眼状态下记录QEEG进行比较。患者和方法:在排除无伪影EEG数据少于1分钟或伪影过多的患者后,最终受试者为自闭症儿童(N = 34)。年龄2 ~ 11岁(平均年龄6.235±SD 2.7198岁),其中男性30例(平均年龄6.1667±SD 2.730岁),女性4例(平均年龄6.75±SD 2.986岁)。qEEG记录使用BrainMaster Discovery 20模块和BrainAvatar 4.0 Discovery(采集软件)。结果:对所有QEEG数据进行计算分析后,将u型功率谱归类并确认为自闭症特征和诊断标志,其特征是低频(delta、theta)和高频(beta、hiBeta)绝对功率过高,中频(alpha)绝对功率降低。结论:最近的文献和我们的研究结果表明,ASD个体存在神经连接障碍。当使用QEEG神经生物标志物作为治疗计划的一部分时,神经反馈(NFB)治疗似乎是调节此类疾病的一种极好的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resting-state QEEG Neuro-Biomarkers for Diagnosis and Treatment Planning of Autism Spectrum Disorders
Background: Autism Spectrum Disorder (ASD) is a combination of complex neurodevelopment disabilities. Early resting-state EEG investigations of autism failed to identify consistent patterns of atypical neural activity. The evidence for the U-shaped profile of electrophysiological power alterations in ASD is primarily supportive, but a more hypothesis-driven effort is needed to confirm and validate it. Aim of study: The primary objective of the present study was to investigate the resting-state QEEG neuro-biomarkers by amplitude analysis as a diagnostic tool for autistic children, compared with a normative group while recording qEEG during an eyes-open condition. Patients and Methods: After excluding those with less than one-minute artifact-free EEG data or too many artifacts, the final participants were (N = 34) autistic children. The age range was 2-11 years (mean age 6.235 ± SD 2.7198 years), including 30 males (mean age 6.1667 ± SD 2.730 years) and four females (mean age 6.75 ± SD 2.986 years). For the qEEG recording, BrainMaster Discovery 20 module and BrainAvatar 4.0 Discovery (Acquisition software) were used. Results: After calculating and analyzing all the QEEG data, the findings were categorized and confirmed the U-shaped power profile as an autism signature and as a diagnostic sign, characterized by excessive absolute power in low-frequencies (delta, theta) and high-frequencies bands (beta, hiBeta) and reduced absolute-power in a midrange frequency band (alpha). Conclusions: Recent literature and our findings have shown that ASD individuals have disturbances of neural connectivity. Neurofeedback (NFB) treatment seems to be an excellent approach to regulating such disorders when using QEEG neuro-biomarkers as a part of treatment planning.
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