Publication
Title
Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model
Author
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.
Language
English
Source (journal)
iScience
Publication
Elsevier , 2023
ISSN
2589-0042
DOI
10.1016/J.ISCI.2023.107454
Volume/pages
26 :8 (2023) , p. 1-20
Article Reference
107454
ISI
001147388500001
Pubmed ID
37599835
Medium
E-only publicatie
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Multimodal Imaging of cholinergic neuromodulation during specific memory phases in the rodent brain.
Improved classification of Alzheimer's disease assessed from the slowly propagating waves of BOLD intensity, the Quasi-Periodic patterns, observed in dynamic resting-state fMRI in a AD rat model at rest and upon sensory stimulation.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 03.10.2023
Last edited 25.04.2024
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