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Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor im...

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Autores principales: Muller, Jennifer J., Wang, Ruixuan, Milddleton, Devon, Alizadeh, Mahdi, Kang, Ki Chang, Hryczyk, Ryan, Zabrecky, George, Hriso, Chloe, Navarreto, Emily, Wintering, Nancy, Bazzan, Anthony J., Wu, Chengyuan, Monti, Daniel A., Jiao, Xun, Wu, Qianhong, Newberg, Andrew B., Mohamed, Feroze B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484001/
https://www.ncbi.nlm.nih.gov/pubmed/37694125
http://dx.doi.org/10.3389/fnins.2023.1182509
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author Muller, Jennifer J.
Wang, Ruixuan
Milddleton, Devon
Alizadeh, Mahdi
Kang, Ki Chang
Hryczyk, Ryan
Zabrecky, George
Hriso, Chloe
Navarreto, Emily
Wintering, Nancy
Bazzan, Anthony J.
Wu, Chengyuan
Monti, Daniel A.
Jiao, Xun
Wu, Qianhong
Newberg, Andrew B.
Mohamed, Feroze B.
author_facet Muller, Jennifer J.
Wang, Ruixuan
Milddleton, Devon
Alizadeh, Mahdi
Kang, Ki Chang
Hryczyk, Ryan
Zabrecky, George
Hriso, Chloe
Navarreto, Emily
Wintering, Nancy
Bazzan, Anthony J.
Wu, Chengyuan
Monti, Daniel A.
Jiao, Xun
Wu, Qianhong
Newberg, Andrew B.
Mohamed, Feroze B.
author_sort Muller, Jennifer J.
collection PubMed
description BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. RESULTS: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7–56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7–73.0% accuracy and NODDI with an accuracy of 64.0–72.3%. CONCLUSION: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.
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spelling pubmed-104840012023-09-08 Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging Muller, Jennifer J. Wang, Ruixuan Milddleton, Devon Alizadeh, Mahdi Kang, Ki Chang Hryczyk, Ryan Zabrecky, George Hriso, Chloe Navarreto, Emily Wintering, Nancy Bazzan, Anthony J. Wu, Chengyuan Monti, Daniel A. Jiao, Xun Wu, Qianhong Newberg, Andrew B. Mohamed, Feroze B. Front Neurosci Neuroscience BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. RESULTS: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7–56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7–73.0% accuracy and NODDI with an accuracy of 64.0–72.3%. CONCLUSION: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10484001/ /pubmed/37694125 http://dx.doi.org/10.3389/fnins.2023.1182509 Text en Copyright © 2023 Muller, Wang, Milddleton, Alizadeh, Kang, Hryczyk, Zabrecky, Hriso, Navarreto, Wintering, Bazzan, Wu, Monti, Jiao, Wu, Newberg and Mohamed. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Muller, Jennifer J.
Wang, Ruixuan
Milddleton, Devon
Alizadeh, Mahdi
Kang, Ki Chang
Hryczyk, Ryan
Zabrecky, George
Hriso, Chloe
Navarreto, Emily
Wintering, Nancy
Bazzan, Anthony J.
Wu, Chengyuan
Monti, Daniel A.
Jiao, Xun
Wu, Qianhong
Newberg, Andrew B.
Mohamed, Feroze B.
Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title_full Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title_fullStr Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title_full_unstemmed Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title_short Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
title_sort machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484001/
https://www.ncbi.nlm.nih.gov/pubmed/37694125
http://dx.doi.org/10.3389/fnins.2023.1182509
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