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首页> 外文期刊>Neuroscience and Biobehavioral Reviews >Overlaps and distinctions between attention deficit/hyperactivity disorder and autism spectrum disorder in young adulthood: Systematic review and guiding framework for EEG-imaging research
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Overlaps and distinctions between attention deficit/hyperactivity disorder and autism spectrum disorder in young adulthood: Systematic review and guiding framework for EEG-imaging research

机译：注意力缺陷/多动障碍与青少年闭散型障碍与自闭症谱系障碍的重叠和区别：EEG成像研究的系统综述与指导框架

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Attention deficit/hyperactivity disorders (ADHD) and autism spectrum disorders (ASD) frequently co-occur. However, we know little about the neural basis of the overlaps and distinctions between these disorders, particularly in young adulthood - a critical time window for brain plasticity across executive and socioemotional domains. Here, we systematically review 75 articles investigating ADHD and ASD in young adult samples (mean ages 16-26) using cognitive tasks, with neural activity concurrently measured via electroencephalography (EEG) - the most accessible neuroimaging technology. The majority of studies focused on event-related potentials (ERPs), with some beginning to capitalise on oscillatory approaches. Overlapping and specific profiles for ASD and ADHD were found mainly for four neurocognitive domains: attention processing, performance monitoring, face processing and sensory processing. No studies in this age group directly compared both disorders or considered dual diagnosis with both disorders. Moving forward, understanding of ADHD, ASD and their overlap in young adulthood would benefit from an increased focus on cross-disorder comparisons, using similar paradigms and in well-powered samples and longitudinal cohorts.
机译：注意力缺陷/多动病症（ADHD）和自闭症谱紊乱（ASD）经常共同发生。然而，我们对这些疾病之间的重叠和区别的神经基础知之甚少，特别是在年轻的年龄 - 跨执行和社会间谍结构域的脑子可塑性的关键时间窗口。在这里，我们通过认知任务来系统地评选研究ADHD和ASD的75篇和ASD，使用认知任务，通过脑电图（EEG）同时测量神经活动 - 最可达的神经影像技术。大多数研究专注于事件相关的潜力（ERP），一些开始利用振荡方法。发现ASD和ADHD的重叠和特定型材主要用于四个神经认知域：注意处理，性能监测，面部处理和感官处理。在这个年龄组没有研究直接与两种疾病相比两种疾病或考虑双重诊断。向前迈进，对ADHD的理解，ASD及其在年轻成年中的重叠将受益于使用类似的范例和纵向样品和纵向队列的跨无序比较的重点。

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