Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Juriquilla, Querétaro 76230, Mexico.
Instituto de Física La Plata (IFLP), CONICET-Universidad Nacional de la Plata, La Plata, Diagonal 113 entre 63y 64, La Plata 1900, Argentina.
J Neurosci Methods. 2022 Jul 1;376:109608. doi: 10.1016/j.jneumeth.2022.109608. Epub 2022 Apr 26.
Previous work showed that elderly with excess in theta activity in their resting state electroencephalogram (EEG) are at higher risk of cognitive decline than those with a normal EEG. By using event-related potentials (ERP) during a counting Stroop task, our prior work showed that elderly with theta excess have a large P300 component compared with normal EEG group. This increased activity could be related to a higher EEG signal energy used during this task.
By wavelet analysis applied to ERP obtained during a counting Stroop task we quantified the energy in the different frequency bands of a group of elderly with altered EEG.
In theta and alpha bands, the total energy was higher in elderly subjects with theta excess, specifically in the stimulus categorization window (258-516 ms). Both groups solved the task with similar efficiency.
The traditional ERP analysis in elderly compares voltage among conditions and groups for a given time window, while the frequency composition is not usually examined. We complemented our previous ERP analysis using a wavelet methodology. Furthermore, we showed the advantages of wavelet analysis over Short Time Fourier Transform when exploring EEG signal during this task.
The higher EEG signal energy in ERP might reflect undergoing neurobiological mechanisms that allow the elderly with theta excess to cope with the cognitive task with similar behavioral results as the normal EEG group. This increased energy could promote a metabolic and cellular dysregulation causing a greater decline in cognitive function.
先前的研究表明，静息态脑电图（EEG）中θ频段活动过多的老年人比正常 EEG 的老年人认知能力下降的风险更高。通过在计数斯特鲁普任务中使用事件相关电位（ERP），我们之前的工作表明，θ频段过多的老年人与正常 EEG 组相比，P300 成分较大。这种增加的活动可能与在任务中使用更高的 EEG 信号能量有关。
通过应用于计数斯特鲁普任务中获得的 ERP 的小波分析，我们对一组改变了 EEG 的老年人的不同频带中的能量进行了量化。
在θ和α频段中，θ频段过多的老年受试者的总能量更高，特别是在刺激分类窗口（258-516 ms）中。两组都以相似的效率解决了任务。
传统的老年 ERP 分析在给定的时间窗口中比较条件和组之间的电压，而通常不检查频率组成。我们使用小波方法补充了我们之前的 ERP 分析。此外，我们展示了在执行此任务时，与短时间傅里叶变换相比，小波分析在探索 EEG 信号方面的优势。
ERP 中更高的 EEG 信号能量可能反映了正在进行的神经生物学机制，这些机制使θ频段过多的老年人能够以与正常 EEG 组相似的行为结果来应对认知任务。这种增加的能量可能会导致代谢和细胞失调，从而导致认知功能下降更大。
