PDF(Pubmed)
Abstract：
UNASSIGNED: Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs).
UNASSIGNED: We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.
UNASSIGNED: SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.
UNASSIGNED: Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
UNASSIGNED: We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model.
UNASSIGNED: SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity.
UNASSIGNED: Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
摘要：
■感觉神经性听力损失（SNHL）是最常见的感觉剥夺形式，通常无法被患者识别，不仅诱导听觉，而且诱导非听觉症状。结合神经静态和动态成像特征的数据驱动分类器建模可以有效地用于对SNHL个体和健康对照（HC）进行分类。
■我们进行了听力评估，110例SNHL患者和106例HC的神经学量表测试和静息态MRI。从MRI数据中提取了1267个静态和动态成像特征，并计算了三种特征选择方法，包括Spearman等级相关检验，最小绝对收缩和选择算子(LASSO)和t检验以及LASSO。线性,多项式,选择径向基函数核（RBF）和sigmoid支持向量机（SVM）模型作为分类器，并进行五次交叉验证。接收机工作特性曲线,曲线下面积（AUC），灵敏度,计算每个模型的特异性和准确性.
■SNHL受试者在每种频率下都有较高的听阈，以及在认知和情感评估方面表现较差，比HCs。经过比较,使用基于静态和动态特征的LASSO选择的大脑区域与组间分析一致，包括听觉和非听觉区域。四个SVM模型的后续AUC(线性，多项式,RBF和sigmoid）如下：0.8075、0.7340、0.8462和0.8562。RBF和sigmoid支持向量机具有较高的精度,敏感性和特异性。
■我们的研究引起了对听力剥夺的静态和动态改变的关注。基于机器学习的模型可以为SNHL的分类和诊断提供几种有用的生物标志物。
■我们进行了听力评估，110例SNHL患者和106例HC的神经学量表测试和静息态MRI。从MRI数据中提取了1267个静态和动态成像特征，并计算了三种特征选择方法，包括Spearman等级相关检验，最小绝对收缩和选择算子(LASSO)和t检验以及LASSO。线性,多项式,选择径向基函数核（RBF）和sigmoid支持向量机（SVM）模型作为分类器，并进行五次交叉验证。接收机工作特性曲线,曲线下面积（AUC），灵敏度,计算每个模型的特异性和准确性.
■SNHL受试者在每种频率下都有较高的听阈，以及在认知和情感评估方面表现较差，比HCs。经过比较,使用基于静态和动态特征的LASSO选择的大脑区域与组间分析一致，包括听觉和非听觉区域。四个SVM模型的后续AUC(线性，多项式,RBF和sigmoid）如下：0.8075、0.7340、0.8462和0.8562。RBF和sigmoid支持向量机具有较高的精度,敏感性和特异性。
■我们的研究引起了对听力剥夺的静态和动态改变的关注。基于机器学习的模型可以为SNHL的分类和诊断提供几种有用的生物标志物。
