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Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture

Authors :
Qianhui Xu
Lei-Lei Zhou
Chunhua Xing
Xiaomin Xu
Yuan Feng
Han Lv
Fei Zhao
Yu-Chen Chen
Yuexin Cai
Source :
NeuroImage, Vol 290, Iss , Pp 120566- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Objectives: Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. Methods: A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. Results: Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. Conclusion: Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.

Details

Language :
English
ISSN :
10959572
Volume :
290
Issue :
120566-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.6239b08091ff4ce69b742eb81b8cc99e
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2024.120566