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Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.
Liu, Yawen; Niu, Haijun; Zhu, Jianming; Zhao, Pengfei; Yin, Hongxia; Ding, Heyu; Gong, Shusheng; Yang, Zhenghan; Lv, Han; Wang, Zhenchang.
Afiliação
  • Liu Y; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Niu H; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Zhu J; Department of Radiation Oncology, University of North Carolina Healthcare, North Carolina, USA.
  • Zhao P; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Yin H; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Ding H; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Gong S; Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Yang Z; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Lv H; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Wang Z; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Neural Plast ; 2019: 1712342, 2019.
Article em En | MEDLINE | ID: mdl-31915431
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zumbido / Encéfalo / Neuroimagem / Substância Cinzenta / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zumbido / Encéfalo / Neuroimagem / Substância Cinzenta / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2019 Tipo de documento: Article