Whole-brain functional activity and connectivity for the classification of subjective tinnitus: a machine learning study.

BACKGROUND AND PURPOSE: Clinical evaluation of subjective tinnitus mainly depends on patients' self-reported auditory complaints, and standardized neuroimaging biomarkers for characterizing its central brain functional abnormalities remain lacking. The aim of this study is to utilize the resting-state functional magnetic resonance imaging (rs-fMRI) machine learning technique based on the region of interest (ROI), and to construct an exploratory classification framework for subjective tinnitus by analyzing functional activities and connectivity. METHODS: The rs-fMRI data of 63 patients with subjective tinnitus (38.79 ± 15.79) and 84 healthy controls (HCs) (42.01 ± 9.45) were collected from the Department of Otorhinolaryngology, the first affiliated Hospital of Nanchang University. Five analysis methods were used: regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), fraction amplitude of low frequency fluctuation (fALFF), resting state functional connectivity (RSFC) and degree centrality (DC). A total of 7,134 features are extracted after z conversion. Then, the predicted features were selected through Mann-Whitney U test, the variables with high pairwise correlation (the correlation coefficient is greater than 0.75) were removed, the least absolute shrinkage and selection operator method was used to screen the features. Finally, a machine learning model was constructed by combining logistic regression (LR), support vector machine (SVM) and random forest (RF), and the performance differences of the three models were compared. RESULTS: 21 features are retained, including 3 zRSFCs, 1 zALFFs, 6 zfALFFs, 3 zDCs, and 8 zReHos. Based on these 21 features, the model accuracy and area under the curve constructed by LR, SVM and RF were 75.51% and 0.80, 80.27% and 0.82, 73.47% and 0.79, respectively. CONCLUSION: Our findings indicate that the ROI-based rs-fMRI machine-learning provides preliminary proof-of-concept evidence for the objective confirmation of subjective tinnitus. The imaging information based on rs-fMRI has the potential to become a neuroimaging biomarker for tinnitus.