Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning. (13th December 2019)

• Record Type: Journal Article
• Authors: Liu, Yawen; Niu, Haijun; Zhu, Jianming; Zhao, Pengfei; Yin, Hongxia; Ding, Heyu; Gong, Shusheng; Yang, Zhenghan; Lv, Han; Wang, Zhenchang
• Other Names: Bagnato Sergio Academic Editor.
• Is Part Of: Neural plasticity. Volume 2019(2019)
• Publication Date: 2019-12-13
• Subjects: Neuroplasticity -- Periodicals; 612.82
• DOI: 10.1155/2019/1712342
• Languages: English
• ISSNs: 2090-5904

Abstract

Abstract : 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.