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Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments

Repetitive mild traumatic brain injury (rmTBI) is a potentially debilitating condition with long-term sequelae. Animal models are used to study rmTBI in a controlled environment, but there is currently no established standard battery of behavioral tests used. Primarily, we aimed to identify the best...

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Autores principales: Lassarén, Philipp, Conley, Grace, Boucher, Masen L., Conley, Ashley N., Morriss, Nicholas J., Qiu, Jianhua, Mannix, Rebekah C., Thelin, Eric Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Mary Ann Liebert, Inc., publishers 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458377/
https://www.ncbi.nlm.nih.gov/pubmed/36738227
http://dx.doi.org/10.1089/neu.2022.0486
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author Lassarén, Philipp
Conley, Grace
Boucher, Masen L.
Conley, Ashley N.
Morriss, Nicholas J.
Qiu, Jianhua
Mannix, Rebekah C.
Thelin, Eric Peter
author_facet Lassarén, Philipp
Conley, Grace
Boucher, Masen L.
Conley, Ashley N.
Morriss, Nicholas J.
Qiu, Jianhua
Mannix, Rebekah C.
Thelin, Eric Peter
author_sort Lassarén, Philipp
collection PubMed
description Repetitive mild traumatic brain injury (rmTBI) is a potentially debilitating condition with long-term sequelae. Animal models are used to study rmTBI in a controlled environment, but there is currently no established standard battery of behavioral tests used. Primarily, we aimed to identify the best combination and timing of behavioral tests to distinguish injured from uninjured animals in rmTBI studies, and secondarily, to determine whether combinations of independent experiments have better behavioral outcome prediction accuracy than individual experiments. Data from 1203 mice from 58 rmTBI experiments, some of which have already been published, were used. In total, 11 types of behavioral tests were measured by 37 parameters at 13 time points during the first 6 months after injury. Univariate regression analyses were used to identify optimal combinations of behavioral tests and whether the inclusion of multiple heterogenous experiments improved accuracy. k-means clustering was used to determine whether a combination of multiple tests could distinguish mice with rmTBI from uninjured mice. We found that a combination of behavioral tests outperformed individual tests alone when distinguishing animals with rmTBI from uninjured animals. The best timing for most individual behavioral tests was 3–4 months after first injury. Overall, Morris water maze (MWM; hidden and probe frequency) was the behavioral test with the best capability of detecting injury effects (area under the curve [AUC] = 0.98). Combinations of open field tests and elevated plus mazes also performed well (AUC = 0.92), as did the forced swim test alone (AUC = 0.90). In summary, multiple heterogeneous experiments tended to predict outcome better than individual experiments, and MWM 3–4 months after injury was the optimal test, also several combinations also performed well. In order to design future pre-clinical rmTBI trials, we have included an interactive application available online utilizing the data from the study via the Supplementary URL.
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spelling pubmed-104583772023-08-27 Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments Lassarén, Philipp Conley, Grace Boucher, Masen L. Conley, Ashley N. Morriss, Nicholas J. Qiu, Jianhua Mannix, Rebekah C. Thelin, Eric Peter J Neurotrauma Original Articles Repetitive mild traumatic brain injury (rmTBI) is a potentially debilitating condition with long-term sequelae. Animal models are used to study rmTBI in a controlled environment, but there is currently no established standard battery of behavioral tests used. Primarily, we aimed to identify the best combination and timing of behavioral tests to distinguish injured from uninjured animals in rmTBI studies, and secondarily, to determine whether combinations of independent experiments have better behavioral outcome prediction accuracy than individual experiments. Data from 1203 mice from 58 rmTBI experiments, some of which have already been published, were used. In total, 11 types of behavioral tests were measured by 37 parameters at 13 time points during the first 6 months after injury. Univariate regression analyses were used to identify optimal combinations of behavioral tests and whether the inclusion of multiple heterogenous experiments improved accuracy. k-means clustering was used to determine whether a combination of multiple tests could distinguish mice with rmTBI from uninjured mice. We found that a combination of behavioral tests outperformed individual tests alone when distinguishing animals with rmTBI from uninjured animals. The best timing for most individual behavioral tests was 3–4 months after first injury. Overall, Morris water maze (MWM; hidden and probe frequency) was the behavioral test with the best capability of detecting injury effects (area under the curve [AUC] = 0.98). Combinations of open field tests and elevated plus mazes also performed well (AUC = 0.92), as did the forced swim test alone (AUC = 0.90). In summary, multiple heterogeneous experiments tended to predict outcome better than individual experiments, and MWM 3–4 months after injury was the optimal test, also several combinations also performed well. In order to design future pre-clinical rmTBI trials, we have included an interactive application available online utilizing the data from the study via the Supplementary URL. Mary Ann Liebert, Inc., publishers 2023-08-01 2023-08-16 /pmc/articles/PMC10458377/ /pubmed/36738227 http://dx.doi.org/10.1089/neu.2022.0486 Text en © Philipp Lassarén et al., 2023; Published by Mary Ann Liebert, Inc. https://creativecommons.org/licenses/by/4.0/This Open Access article is distributed under the terms of the Creative Commons License (CC-BY) (http://creativecommons.org/licenses/by/4.0 (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Original Articles
Lassarén, Philipp
Conley, Grace
Boucher, Masen L.
Conley, Ashley N.
Morriss, Nicholas J.
Qiu, Jianhua
Mannix, Rebekah C.
Thelin, Eric Peter
Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title_full Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title_fullStr Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title_full_unstemmed Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title_short Optimizing Choice and Timing of Behavioral Outcome Tests After Repetitive Mild Traumatic Brain Injury: A Machine Learning-Based Approach on Multiple Pre-Clinical Experiments
title_sort optimizing choice and timing of behavioral outcome tests after repetitive mild traumatic brain injury: a machine learning-based approach on multiple pre-clinical experiments
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458377/
https://www.ncbi.nlm.nih.gov/pubmed/36738227
http://dx.doi.org/10.1089/neu.2022.0486
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