Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Frontiers in Computational Neuroscience

Chaoyang Jin¹  Lei Yang¹  Qiyuan Song¹  Wei Qian²  Houyu Zhao³  Yi Yin⁴  Hui Yu⁵  Shouliang Qi⁶ 

[1] College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China;Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, United States;Department of Otolaryngology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China;Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China;Department of Radiology, The Seventh Affiliated Hospital, Southern Medical University, Foshan, China;Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China;

关键词: sensorineural hearing loss;    resting-state fMRI;    functional brain network;    cochlear implantation;    machine learning;    multiple logistic regression;   

DOI  :  10.3389/fncom.2022.825160

来源: DOAJ

【 摘 要 】

Identification of congenital sensorineural hearing loss (SNHL) and early intervention, especially by cochlear implantation (CI), are crucial for restoring hearing in patients. However, high accuracy diagnostics of SNHL and prognostic prediction of CI are lacking to date. To diagnose SNHL and predict the outcome of CI, we propose a method combining functional connections (FCs) measured by functional magnetic resonance imaging (fMRI) and machine learning. A total of 68 children with SNHL and 34 healthy controls (HC) of matched age and gender were recruited to construct classification models for SNHL and HC. A total of 52 children with SNHL that underwent CI were selected to establish a predictive model of the outcome measured by the category of auditory performance (CAP), and their resting-state fMRI images were acquired. After the dimensional reduction of FCs by kernel principal component analysis, three machine learning methods including the support vector machine, logistic regression, and k-nearest neighbor and their voting were used as the classifiers. A multiple logistic regression method was performed to predict the CAP of CI. The classification model of voting achieves an area under the curve of 0.84, which is higher than that of three single classifiers. The multiple logistic regression model predicts CAP after CI in SNHL with an average accuracy of 82.7%. These models may improve the identification of SNHL through fMRI images and prognosis prediction of CI in SNHL.

【 授权许可】

Unknown