Pelland-Goulet Pénélope, Arguin Martin, Brisebois Hélène, Gosselin Nathalie
Psychology Department, Université de Montréal, Montréal, Québec, Canada.
Center for Interdisciplinary Research on Brain and Learning (CIRCA), Montréal, Québec, Canada.
PLoS One. 2025 Sep 2;20(9):e0310605. doi: 10.1371/journal.pone.0310605. eCollection 2025.
ADHD is a neurodevelopmental disorder affecting 3-4% of Canadian adults and 2.6% of adults worldwide. Its symptoms include inattention, hyperactivity and impulsivity. Though ADHD is known to affect several brain functions and cognitive processes, little is known regarding its impact on perceptual oscillations. This study compared the temporal features of visual processing between ADHD and neurotypical individuals in a visual word recognition task through the use of a temporal sampling technique, the outcome of which are classification images reflecting processing effectiveness according to the temporal properties of the stimulus. These temporal features were sufficiently different across groups while at the same time sufficiently congruent across participants of the same group that a machine learning algorithm classified participants in their respective groups with a 91.8% accuracy using only a small portion of the available features. Secondary findings showed that individuals with ADHD could be classified with high accuracy (91.3%) regarding their use of psychostimulant medication. These findings suggest the existence of strong behavioral markers of ADHD as well as of regular medication usage on visual performance which can be uncovered by random temporal sampling.
注意缺陷多动障碍（ADHD）是一种神经发育障碍，影响着3%至4%的加拿大成年人以及全球2.6%的成年人。其症状包括注意力不集中、多动和冲动。尽管已知ADHD会影响多种脑功能和认知过程，但关于其对感知振荡的影响却知之甚少。本研究通过使用时间采样技术，比较了ADHD患者与神经正常个体在视觉单词识别任务中视觉处理的时间特征，其结果是根据刺激的时间特性反映处理效果的分类图像。这些时间特征在不同组之间差异足够大，同时在同一组的参与者之间又足够一致，以至于一种机器学习算法仅使用一小部分可用特征就能以91.8%的准确率将参与者分类到各自的组中。次要发现表明，就使用精神兴奋剂药物而言，ADHD患者能够以较高的准确率（91.3%）被分类。这些发现表明，存在ADHD的强烈行为标志物以及常规药物使用对视觉表现的影响，这些可以通过随机时间采样揭示出来。
