新的神经活动谱潜在的抑制控制缺陷的ADHD临床相关性-从脑电图张量分解的见解。

Negin Gholamipourbarogh, Veit Roessner, Annet Bluschke, Christian Beste
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引用次数: 0

摘要

背景:注意缺陷多动障碍(ADHD)是一种影响认知控制过程的多方面神经发育障碍。虽然神经生理学数据(如脑电图数据)为其潜在机制提供了有价值的见解,但要充分理解ADHD患者认知功能的改变,需要先进的分析方法,能够更有效地捕捉神经生理学数据的高维性质。方法:我们使用标准Go/Nogo任务对N=59名ADHD患者和N=63名神经正常参与者进行测试,以评估反应抑制。我们使用脑电图张量分解来提取与ADHD抑制控制缺陷相关的频谱、时间、空间和试验水平特征。试验水平的特征捕获了个体内部的可变性,然后将其用于机器学习分析,以区分患有ADHD的个体与神经正常的参与者。我们还应用了一种特征选择算法来识别最重要的特征,以便在分类过程中区分两组。结果:我们观察到ADHD患者典型的反应抑制缺陷。与通常的假设相反,额中央θ波带活动似乎并不是ADHD和神经正常个体之间最显著的脑电图特征。相反,最重要的区别特征是反映注意选择时间窗期间后α带活动的张量分量,以及反应选择和控制时间窗期间后θ带活动的张量分量。结论:我们发现了ADHD反应抑制的新的神经生理学方面,使ADHD和神经正常个体的分类成为可能。我们的研究结果表明,adhd相关的缺陷在注意力选择早期就出现了,并在反应控制阶段持续存在。研究结果强调需要完善ADHD神经特性的概念,并相应地调整针对抑制控制缺陷的临床干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD - insights from EEG tensor decomposition.

Background: Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered cognitive functions in ADHD requires advanced analytical approaches capable of capturing the highly dimensional nature of neurophysiological data more effectively.

Methods: We examined N=59 individuals with ADHD and N=63 neurotypical participants using a standard Go/Nogo task to assess response inhibition. We used EEG tensor decomposition to extract spectral, temporal, spatial and trial-level features associated with inhibitory control deficits in ADHD. The trial-level features capture intra-individual variability which is then used in a machine learning analysis to differentiate individuals with ADHD from neurotypical participants. We also applied a feature selection algorithm to identify the most important features for distinguishing between the two groups in the classification process.

Results: We observed typical response inhibition deficits in ADHD. Contrary to common assumptions, fronto-central theta band activity did not appear to be the most distinguishing EEG feature between ADHD and neurotypical individuals. Instead, the most important distinguishing features are tensor components reflecting posterior alpha band activity during attentional selection time windows and posterior theta band activity during response selection and control time windows.

Conclusions: We identified novel neurophysiological facets of response inhibition in ADHD, enabling the classification of ADHD and neurotypical individuals. Our findings suggest that ADHD-related deficits emerge early during attentional selection and persist through response control stages. The findings underscore the need to refine conceptions about neural peculiarities in ADHD and adapt clinical interventions targeting inhibitory control deficits accordingly.

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