Ochoa John F, Ruiz Mariana, Valle Diego, Duque Jon, Tobon Carlos, Alonso Joan F, Hernandez A Mauricio, Mananas Miguel A
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7442-5. doi: 10.1109/EMBC.2015.7320112.
Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.
阿尔茨海默病是痴呆最常见的病因。轻度认知障碍（MCI）被定义为完整认知功能与临床痴呆之间的灰色地带。脑电图（EEG）已被用于识别痴呆中的生物标志物。目前，人们对将研究从原始信号转化为信号发生器有着浓厚兴趣，试图保持与神经生理学的关系。在当前研究中，对MCI受试者和健康对照者在编码任务期间进行了EEG记录。使用组独立成分分析（gICA）对数据进行分解，并使用相位试验间相干性（PIC）和相移试验间相干性（PsIC）对最具神经元特征的成分进行分析。MCI受试者在θ波段的PIC增加，而对照组在α波段的PsIC增加。还发现了PIC和PsIC与临床量表之间的相关性。这些发现表明，基于gICA提出的方法有助于从具有神经生理学意义的EEG记录中提取信息。
