Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis

Objectives. To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods. Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88±13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females...

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Autores principales:	Xiaoping Luo, Dezhao Lin, Shengwei Xia, Dongyu Wang, Xinmang Weng, Wenming Huang, Hongda Ye
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/8c728b3914294715884ff2a9c68bb180
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spelling	oai:doaj.org-article:8c728b3914294715884ff2a9c68bb1802021-11-29T00:57:10ZMachine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis1875-863010.1155/2021/3015238https://doaj.org/article/8c728b3914294715884ff2a9c68bb1802021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3015238https://doaj.org/toc/1875-8630Objectives. To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods. Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88±13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46±11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. Results. The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. Conclusions. Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.Xiaoping LuoDezhao LinShengwei XiaDongyu WangXinmang WengWenming HuangHongda YeHindawi LimitedarticleMedicine (General)R5-920ENDisease Markers, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Xiaoping Luo Dezhao Lin Shengwei Xia Dongyu Wang Xinmang Weng Wenming Huang Hongda Ye Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
description	Objectives. To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods. Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88±13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46±11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. Results. The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. Conclusions. Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.
format	article
author	Xiaoping Luo Dezhao Lin Shengwei Xia Dongyu Wang Xinmang Weng Wenming Huang Hongda Ye
author_facet	Xiaoping Luo Dezhao Lin Shengwei Xia Dongyu Wang Xinmang Weng Wenming Huang Hongda Ye
author_sort	Xiaoping Luo
title	Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
title_short	Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
title_full	Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
title_fullStr	Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
title_full_unstemmed	Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis
title_sort	machine learning classification of mild traumatic brain injury using whole-brain functional activity: a radiomics analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8c728b3914294715884ff2a9c68bb180
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Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis

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