通过脑电图复杂度识别注意缺陷/多动障碍
Identifying Attention-Deficit/Hyperactivity Disorder through the electroencephalogram complexity
AI总结：
本文利用 q 统计量分析脑电图复杂度，发现 ADHD 患者在任务和静息期的 (c, q) 参数均高于典型个体，且该参数空间分布有望成为 ADHD 诊断的生物标志物。
摘要：
有理由认为，多种精神障碍可能与神经复杂度（NC）的改变有关。因此，对神经复杂度进行定量分析可能有助于对精神状况进行分类和理解。本文聚焦于一种方法学流程，我们研究了典型年轻个体和患有注意缺陷/多动障碍（ADHD）的年轻个体，使用应用于脑电图（EEG）的 q 统计量评估其神经复杂度。脑电图在受试者执行视觉注意网络测试（ANT）期间以及短暂的测试前静息状态期间记录。从每个受试者的任务和测试前信号中收集超过阈值的脑电图幅值时间区间。数据可以令人满意地用带有幂律前因子（以指数 c 表征）的拉伸 q 指数函数拟合，从而为每个受试者确定最佳的 (c, q)，用以指示其个体复杂度。我们发现，与典型受试者相比，ADHD 受试者在任务期和测试前期均具有更大的 q 和 c 值，且两组在任务期的值均大于静息期。c 参数在相对于 DSM 对注意力不集中的诊断方面具有高度特异性，观察到定义良好的聚类。参数值在 (c, q) 空间中组织为四个定义良好的聚类。正如预期，任务显然诱导了神经功能状态中更大的复杂度，可能伴随更大量的内部信息处理。结果表明，ADHD 受试者的复杂度高于典型配对个体。由 q 统计量导出的 (c, q) 空间中的值分布似乎是一种有前景的 ADHD 诊断生物标志物。
英文摘要：
There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched $q$-exponential including a power-law prefactor(characterized by the exponent c), thus determining the best $(c, q)$ for each subject, indicative of their individual complexity. We found larger values of $q$ and $c$ in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The $c$ parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in $(c, q)$-space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the $(c, q)$-space derived from $q$-statistics seems to be a promising biomarker for ADHD diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
