Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model

Faraz Moradi; Monica van den Berg; Morteza Mirjebreili; Lauren Kosten; Marleen Verhoye; Mahmood Amiri; Georgios A Keliris

doi:10.1016/j.isci.2023.107454

Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model

iScience. 2023 Jul 22;26(8):107454. doi: 10.1016/j.isci.2023.107454. eCollection 2023 Aug 18.

Authors

Faraz Moradi¹, Monica van den Berg^{2

3}, Morteza Mirjebreili⁴, Lauren Kosten^{2

3}, Marleen Verhoye^{2

3}, Mahmood Amiri⁵, Georgios A Keliris^{2

3

6}

Affiliations

¹ Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
² Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium.
³ μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
⁴ Institute for Cognitive Science Studies, Tehran, Iran.
⁵ Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
⁶ Institute of Computer Science, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.

Abstract

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.

Keywords: Biocomputational method; Neuroscience.