Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning
Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning
复制标题
耳鸣的形态神经影像生物标志物：通过应用机器学习获得的证据
DOI：
10.1155/2019/1712342
复制
发表时间：
2019-12-13
影响因子：
3.1
通讯作者：
Wang, Zhenchang
中科院分区：
文献类型：
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作者：
Liu, Yawen;Niu, Haijun;Wang, Zhenchang
根据以往的研究，耳鸣患者已经发现了许多神经解剖学上的改变。然而，这些研究的结果并不一致。本研究的目的是利用机器学习方法探索可能表征特发性耳鸣的皮层/皮层下形态学神经成像生物标志物。本研究包括46例特发性耳鸣患者和56名健康受试者。对每个受试者提取61个脑区的灰质体积作为原始特征池。从该特征池中，采用F-score和顺序正向浮动选择（SFFS）相结合的混合特征选择算法进行特征选择。然后，使用选择的特征训练支持向量机（SVM）模型。使用曲线下面积（AUC）和准确率来评估分类模型的性能。结果发现，13个脑皮层/皮层下区域的组合在有效区分耳鸣患者和健康受试者方面具有最高的分类准确性。这些脑区包括双侧下丘脑、右侧脑岛、双侧颞上回、左侧额叶中吻回、双侧颞下回、右侧顶叶下小叶、右侧颞横回、右侧颞中回、右侧扣带回和左侧额上回。训练集和测试集的准确率分别为80.49%和80.00%，AUC为0.8586。据我们所知，这是第一个通过应用SVM分类器来阐明耳鸣患者脑形态变化的研究。本研究提供了有效的皮层/皮层下形态学神经成像生物标志物，用于区分耳鸣患者和健康受试者，并有助于了解耳鸣患者的神经解剖学改变。
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.