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

• Published in: NeuroImage (Orlando, Fla.) Vol. 290; p. 120566
• Main Authors: Xu, Qianhui, Zhou, Lei-Lei, Xing, Chunhua, Xu, Xiaomin, Feng, Yuan, Lv, Han, Zhao, Fei, Chen, Yu-Chen, Cai, Yuexin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2024; Elsevier Limited; Elsevier
• Subjects: Artificial intelligence; Brain mapping; Brain research; Classification; Convolutional neural network; Deep learning; Diagnosis; Functional connectivity; Functional magnetic resonance imaging; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Neuroimaging; Otolaryngology; Resting state fMRI; Sensorimotor system; Support vector machines; Tinnitus; Transfer learning; Functional connectivity; Resting state fMRI; Tinnitus; Convolutional neural network

•A decomposed convolutional neural network model was established based on rs-fMRI connectivity.•The model paired with the Dos_160 atlas can be effectively applied to the diagnosis of tinnitus.•This study pinpointed key brain regions for subjective tinnitus using a data-driven approach. Many studies...