Moradi Faraz, van den Berg Monica, Mirjebreili Morteza, Kosten Lauren, Verhoye Marleen, Amiri Mahmood, Keliris Georgios A
Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium.
iScience. 2023 Jul 22;26(8):107454. doi: 10.1016/j.isci.2023.107454. eCollection 2023 Aug 18.
The hippocampus plays a vital role in navigation, learning, and memory, and is affected in Alzheimer's disease (AD). This study investigated the classification of AD-transgenic rats versus wild-type littermates using electrophysiological activity recorded from the hippocampus at an early, presymptomatic stage of the disease (6 months old) in the TgF344-AD rat model. The recorded signals were filtered into low frequency (LFP) and high frequency (spiking activity) signals, and machine learning classifiers were employed to identify the rat genotype (TG vs. WT). By analyzing specific frequency bands in the low frequency signals and calculating distance metrics between spike trains in the high frequency signals, accurate classification was achieved. Gamma band power emerged as a valuable signal for classification, and combining information from both low and high frequency signals improved the accuracy further. These findings provide valuable insights into the early stage effects of AD on different regions of the hippocampus.
海马体在导航、学习和记忆中起着至关重要的作用，并且在阿尔茨海默病（AD）中会受到影响。本研究使用TgF344-AD大鼠模型，在疾病的早期、症状前阶段（6个月大）从海马体记录的电生理活动，对AD转基因大鼠与野生型同窝仔鼠进行分类。记录的信号被过滤为低频（LFP）和高频（尖峰活动）信号，并使用机器学习分类器来识别大鼠基因型（转基因与野生型）。通过分析低频信号中的特定频段并计算高频信号中尖峰序列之间的距离度量，实现了准确分类。伽马波段功率成为一种有价值的分类信号，并且结合低频和高频信号的信息进一步提高了准确性。这些发现为AD对海马体不同区域的早期影响提供了有价值的见解。
