ArticleFrontiers in neurology2026
Whole-brain functional activity and connectivity for the classification of subjective tinnitus: a machine learning study.
Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 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.
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