Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model
Summary: 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, p...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223015316 |
| _version_ | 1827868361565929472 |
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| author | Faraz Moradi Monica van den Berg Morteza Mirjebreili Lauren Kosten Marleen Verhoye Mahmood Amiri Georgios A. Keliris |
| author_facet | Faraz Moradi Monica van den Berg Morteza Mirjebreili Lauren Kosten Marleen Verhoye Mahmood Amiri Georgios A. Keliris |
| author_sort | Faraz Moradi |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-12T15:30:36Z |
| format | Article |
| id | doaj.art-81df3e93c7ab4ea68a96564a747326ed |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-12T15:30:36Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-81df3e93c7ab4ea68a96564a747326ed2023-08-10T04:34:48ZengElsevieriScience2589-00422023-08-01268107454Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat modelFaraz Moradi0Monica van den Berg1Morteza Mirjebreili2Lauren Kosten3Marleen Verhoye4Mahmood Amiri5Georgios A. Keliris6Faculty of Engineering, University of Ottawa, Ottawa, ON, CanadaBio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, BelgiumInstitute for Cognitive Science Studies, Tehran, IranBio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, BelgiumBio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, BelgiumMedical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran; Corresponding authorBio-Imaging Lab, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium; Institute of Computer Science, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004223015316NeuroscienceBiocomputational method |
| spellingShingle | Faraz Moradi Monica van den Berg Morteza Mirjebreili Lauren Kosten Marleen Verhoye Mahmood Amiri Georgios A. Keliris Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model iScience Neuroscience Biocomputational method |
| title | Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model |
| title_full | Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model |
| title_fullStr | Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model |
| title_full_unstemmed | Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model |
| title_short | Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model |
| title_sort | early classification of alzheimer s disease phenotype based on hippocampal electrophysiology in the tgf344 ad rat model |
| topic | Neuroscience Biocomputational method |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223015316 |
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