Department of Computer Engineering, Işık University, Istanbul, Turkey.
Neurology Department, Acıbadem Hastanesi Kadıköy, Istanbul, Turkey.
Appl Neuropsychol Child. 2023 Jul-Sep;12(3):214-220. doi: 10.1080/21622965.2022.2074298. Epub 2022 May 15.
Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.
学习阅读障碍的亚型可能有助于缩短康复过程，并更专注于阅读障碍儿童的相关特殊教育或饮食。为此，对 112 名 7 至 11 岁的阅读障碍儿童（84 名男性，28 名女性）进行了 96 次平均每次 2 分钟的睁眼静息态 QEEG 测量数据收集。为每个频段的功率和每个通道计算 z 分数，然后剔除异常值。使用 K-均值聚类方法，确定了三个不同的聚类。聚类 1（占病例的 19%）在所有通道中，θ、α、β-1、β-2 和γ 频段的功率均有正 z 分数。聚类 2（占病例的 76%）在所有通道中，θ、α、β-1、β-2 和γ 频段的功率均有负 z 分数。聚类 3（占病例的 5%）在 AF3、F3、FC5 和 T7 通道中，θ、α、β-1、β-2 和γ 频段的功率具有正 z 分数，而在其他通道中 z 分数大多为负。聚类 3 中存在时间中断，这是阅读障碍的典型描述。聚类 1 中存在普遍的脑炎症，因为在同一通道中检测到慢波和快波。聚类 2 中存在脑成熟延迟和轻度炎症。经过 Auto Train Brain 训练后，大多数病例更类似于聚类 2，这可能意味着炎症减轻，脑成熟延迟显现出来，这可能是炎症的结果。此外，在 60 次训练后，聚类 2 的大脑后部中心值向这些通道的平均值转移。这意味着，Auto Train Brain 训练改善了阅读障碍儿童的大脑后部，这是阅读障碍最需要加强的区域。
