Sa Asha, C Sudalaimani, P Devanand, Ps Subodh, Ml Arya, Kumar Devika, Thomas Sanjeev V, Menon Ramshekhar N
Centre For Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala India.
Department of Neurology, R Madhavan Nayar Centre for Comprehensive Epilepsy Care, Sree Chitra Tirunal Institute for Medical Sciences & Technology (SCTIMST), Thiruvananthapuram, Kerala 695011 India.
Cogn Neurodyn. 2024 Oct;18(5):2419-2432. doi: 10.1007/s11571-024-10095-z. Epub 2024 Mar 23.
Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate statistics. We found significant differences in all parameters related to Microstate D (fronto-parietal network) in TLE patients and Microstate B (visual processing) in IGE patients compared to HCs. Occurrence, duration and time coverage of Microstate B was highest in IGE when compared to the other groups. We also found significant deviations in transition probabilities for both epilepsy groups, particularly into Microstate C (salience network) in IGE. Classification accuracy into clinical groups was found to exceed 70% using microstate parameters which improved on incorporating neuropsychological test differences. To the best of our knowledge, the current study is the first to compare and validate the use of microstate features to discriminate between two disparate epilepsy syndromes (TLE, IGE) and HCs using machine learning suggesting that resting state EEG microstates can be used for endophenotyping and to study resting state dysfunction in epilepsy.
The online version contains supplementary material available at 10.1007/s11571-024-10095-z.
基于脑电图（EEG）的微状态分析是一种很有前景且被广泛研究的方法，该方法将自发脑活动分割为亚秒级的准稳定状态并进行分析。目前，由于越来越多的证据表明微状态与认知功能以及功能磁共振成像（fMRI）识别的大规模脑网络之间存在关联，因此该方法正在被广泛探索。在我们使用四种典型微状态（A、B、C和D）的研究中，我们调查了颞叶癫痫（TLE）和特发性全身性癫痫（IGE）患者与健康对照（HC）相比，静息态EEG微状态动力学的变化。应用机器学习来研究使用微状态统计区分不同组别的可行性。我们发现，与健康对照相比，TLE患者中与微状态D（额顶叶网络）相关的所有参数以及IGE患者中与微状态B（视觉处理）相关的所有参数均存在显著差异。与其他组相比，IGE患者中微状态B的出现率、持续时间和时间覆盖率最高。我们还发现两个癫痫组的转换概率均存在显著偏差，尤其是IGE患者向微状态C（突显网络）的转换概率。使用微状态参数发现临床组的分类准确率超过70%，纳入神经心理测试差异后有所提高。据我们所知，当前研究首次使用机器学习比较并验证了微状态特征在区分两种不同癫痫综合征（TLE、IGE）和健康对照中的应用，这表明静息态EEG微状态可用于内表型分析以及研究癫痫中的静息态功能障碍。
在线版本包含可在10.1007/s11571-024-10095-z获取的补充材料。
