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Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity
Translational Psychiatry ( IF 5.8 ) Pub Date : 2021-09-18 , DOI: 10.1038/s41398-021-01604-3
Sungkean Kim 1 , Ji Hyun Baek 2 , Young Joon Kwon 3 , Hwa Young Lee 3 , Jae Hyun Yoo 4 , Se-Hoon Shim 3 , Ji Sun Kim 3
Affiliation  

Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01% accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN reflects the adult ADHD patients’ pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD.



中文翻译:

使用错配负性对未接受过药物治疗的成年注意力缺陷多动障碍患者进行基于机器学习的诊断

关于成人注意力缺陷/多动障碍 (ADHD) 的神经生理学研究相对较少。失配负性(MMN)是代表前注意听觉处理的事件相关电位成分,与认知状态密切相关。我们研究了 MMN 特征作为生物标志物,以对未接受过药物治疗的成年 ADHD 患者和健康对照者 (HCs) 进行分类。MMN 的传感器级特征(振幅和潜伏期)和源级特征(源激活)进行了研究,并使用被动听觉古怪范式在 34 名 ADHD 患者和 45 名 HC 的脑电图之间进行了比较。分析了 MMN 特征与 ADHD 症状之间的相关性。最后,我们应用机器学习来使用 MMN 的传感器级和源级特征来区分这两个组。成年 ADHD 患者的额中央电极 MMN 振幅显着降低,额叶、颞叶和边缘叶的 MMN 源激活减少,这与 MMN 发生器和 ADHD 病理生理学密切相关。源活动与 ADHD 症状显着相关。基于 MMN 源活动特征,成人 ADHD 患者和 HC 的最佳分类性能显示出 81.01% 的准确度、82.35% 的灵敏度和 80.00% 的特异性。我们的研究结果表明,异常的 MMN 反映了成人 ADHD 患者的病理生理特征,并可能在临床上作为成人 ADHD 的神经标志物。这与 MMN 发生器和 ADHD 病理生理学密切相关。源活动与 ADHD 症状显着相关。基于 MMN 源活动特征,成人 ADHD 患者和 HC 的最佳分类性能显示出 81.01% 的准确度、82.35% 的灵敏度和 80.00% 的特异性。我们的研究结果表明,异常的 MMN 反映了成人 ADHD 患者的病理生理特征,并可能在临床上作为成人 ADHD 的神经标志物。这与 MMN 发生器和 ADHD 病理生理学密切相关。源活动与 ADHD 症状显着相关。基于 MMN 源活动特征,成人 ADHD 患者和 HC 的最佳分类性能显示出 81.01% 的准确度、82.35% 的灵敏度和 80.00% 的特异性。我们的研究结果表明,异常的 MMN 反映了成人 ADHD 患者的病理生理特征,并可能在临床上作为成人 ADHD 的神经标志物。

更新日期:2021-09-19
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