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Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis

BACKGROUND: Auditory dysfunction, including central auditory hyperactivity, hearing loss and hearing in noise deficits, has been reported in 5xFAD Alzheimer’s disease (AD) mice, suggesting a causal relationship between amyloidosis and auditory dysfunction. Central auditory hyperactivity correlated i...

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Autores principales: Na, Daxiang, Yang, Yidan, Xie, Li, Piekna-Przybylska, Dorota, Bunn, Dominic, Shamambo, Maleelo, White, Patricia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571613/
https://www.ncbi.nlm.nih.gov/pubmed/37841847
http://dx.doi.org/10.21203/rs.3.rs-3370200/v1
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author Na, Daxiang
Yang, Yidan
Xie, Li
Piekna-Przybylska, Dorota
Bunn, Dominic
Shamambo, Maleelo
White, Patricia
author_facet Na, Daxiang
Yang, Yidan
Xie, Li
Piekna-Przybylska, Dorota
Bunn, Dominic
Shamambo, Maleelo
White, Patricia
author_sort Na, Daxiang
collection PubMed
description BACKGROUND: Auditory dysfunction, including central auditory hyperactivity, hearing loss and hearing in noise deficits, has been reported in 5xFAD Alzheimer’s disease (AD) mice, suggesting a causal relationship between amyloidosis and auditory dysfunction. Central auditory hyperactivity correlated in time with small amounts of plaque deposition in the inferior colliculus and medial geniculate body, which are the auditory midbrain and thalamus, respectively. Neuroinflammation has been associated with excitation to inhibition imbalance in the central nervous system, and therefore has been proposed as a link between central auditory hyperactivity and AD in our previous report. However, neuroinflammation in the auditory pathway has not been investigated in mouse amyloidosis models. METHODS: Machine learning was used to classify the previously obtained auditory brainstem responses (ABRs) from 5xFAD mice and their wild type (WT) littermates. Neuroinflammation was assessed in six auditory-related regions of the cortex, thalamus, and brainstem. Cochlear pathology was assessed in cryosection and whole mount. Behavioral changes were assessed with fear conditioning, open field testing and novel objection recognition. RESULTS: Reliable machine learning classification of 5xFAD and WT littermate ABRs were achieved for 6M and 12M, but not 3M. The top features for accurate classification at 6 months of age were characteristics of Waves IV and V. Microglial and astrocytic activation were pronounced in 5xFAD inferior colliculus and medial geniculate body at 6 months, two neural centers that are thought to contribute to these waves. Lower regions of the brainstem were unaffected, and cortical auditory centers also displayed inflammation beginning at 6 months. No losses were seen in numbers of spiral ganglion neurons (SGNs), auditory synapses, or efferent synapses in the cochlea. 5xFAD mice had reduced responses to tones in fear conditioning compared to WT littermates beginning at 6 months. CONCLUSIONS: Serial use of ABR in early AD patients represents a promising approach for early and inexpensive detection of neuroinflammation in higher auditory brainstem processing centers. As changes in auditory processing are strongly linked to AD progression, central auditory hyperactivity may serve as a biomarker for AD progression and/or stratify AD patients into distinct populations.
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spelling pubmed-105716132023-10-14 Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis Na, Daxiang Yang, Yidan Xie, Li Piekna-Przybylska, Dorota Bunn, Dominic Shamambo, Maleelo White, Patricia Res Sq Article BACKGROUND: Auditory dysfunction, including central auditory hyperactivity, hearing loss and hearing in noise deficits, has been reported in 5xFAD Alzheimer’s disease (AD) mice, suggesting a causal relationship between amyloidosis and auditory dysfunction. Central auditory hyperactivity correlated in time with small amounts of plaque deposition in the inferior colliculus and medial geniculate body, which are the auditory midbrain and thalamus, respectively. Neuroinflammation has been associated with excitation to inhibition imbalance in the central nervous system, and therefore has been proposed as a link between central auditory hyperactivity and AD in our previous report. However, neuroinflammation in the auditory pathway has not been investigated in mouse amyloidosis models. METHODS: Machine learning was used to classify the previously obtained auditory brainstem responses (ABRs) from 5xFAD mice and their wild type (WT) littermates. Neuroinflammation was assessed in six auditory-related regions of the cortex, thalamus, and brainstem. Cochlear pathology was assessed in cryosection and whole mount. Behavioral changes were assessed with fear conditioning, open field testing and novel objection recognition. RESULTS: Reliable machine learning classification of 5xFAD and WT littermate ABRs were achieved for 6M and 12M, but not 3M. The top features for accurate classification at 6 months of age were characteristics of Waves IV and V. Microglial and astrocytic activation were pronounced in 5xFAD inferior colliculus and medial geniculate body at 6 months, two neural centers that are thought to contribute to these waves. Lower regions of the brainstem were unaffected, and cortical auditory centers also displayed inflammation beginning at 6 months. No losses were seen in numbers of spiral ganglion neurons (SGNs), auditory synapses, or efferent synapses in the cochlea. 5xFAD mice had reduced responses to tones in fear conditioning compared to WT littermates beginning at 6 months. CONCLUSIONS: Serial use of ABR in early AD patients represents a promising approach for early and inexpensive detection of neuroinflammation in higher auditory brainstem processing centers. As changes in auditory processing are strongly linked to AD progression, central auditory hyperactivity may serve as a biomarker for AD progression and/or stratify AD patients into distinct populations. American Journal Experts 2023-09-27 /pmc/articles/PMC10571613/ /pubmed/37841847 http://dx.doi.org/10.21203/rs.3.rs-3370200/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Na, Daxiang
Yang, Yidan
Xie, Li
Piekna-Przybylska, Dorota
Bunn, Dominic
Shamambo, Maleelo
White, Patricia
Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title_full Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title_fullStr Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title_full_unstemmed Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title_short Neuroinflammation in a Mouse Model of Alzheimer’s Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis
title_sort neuroinflammation in a mouse model of alzheimer’s disease versus auditory dysfunction: machine learning interpretation and analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571613/
https://www.ncbi.nlm.nih.gov/pubmed/37841847
http://dx.doi.org/10.21203/rs.3.rs-3370200/v1
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