Objective: To explore the changes in the cerebral microstructure of patients with noise‐induced hearing loss (NIHL) using diffusion tensor imaging (DTI).Method: Overall, 122 patients with NIHL (mild [MP, n = 79], relatively severe patients [including moderate and severe; RSP, n = 32], and undetermined [lost to follow‐up, n = 11]) and 84 healthy controls (HCs) were enrolled. All clinical data, including age, education level, hearing threshold, occupation type, noise exposure time, and some scale scores (including the Mini‐Mental State Examination [MMSE], tinnitus handicap inventory [THI], and Hamilton Anxiety Scale [HAMA]), were collected and analyzed. All participants underwent T1WI3DFSPGR and DTI, and tract‐based spatial statistics and region of interest (ROI) analysis were used for assessment.Results: The final sample included 71 MP, 28 RSP, and 75 HCs. The HAMA scores of the three groups were significantly different (p < .05). The noise exposure times, hearing thresholds, and HAMA scores of the MP and RSP were significantly different (p < .05). The noise exposure time was positively correlated with the hearing threshold and negatively correlated with the HAMA scores (p < .05), whereas the THI scores were positively correlated with the hearing threshold (p < .05). DTI analysis showed that all DTI parameters (fractional anisotropy [FA], axial diffusivity [AD], mean diffusivity [MD], and radial diffusivity [RD]) were significantly different in the left inferior longitudinal fasciculus (ILF) and left inferior fronto‐occipital fasciculus (IFOF) for the three groups (p < .05). In addition, the FA values were significantly lower in the bilateral corticospinal tract (CST), right fronto‐pontine tract (FPT), right forceps major, left superior longitudinal fasciculus (temporal part) (SLF), and left cingulum (hippocampus) (C‐H) of the MP and RSP than in those of the HCs (p < .05); the AD values showed diverse changes in the bilateral CST, left IFOF, right anterior thalamic radiation, right external capsule (EC), right SLF, and right superior cerebellar peduncle (SCP) of the MP and RSP relative to those of the HC (p < .05). However, there were no significant differences among the bilateral auditory cortex ROIs of the three groups (p > .05). There was a significant negative correlation between the FA and HAMA scores for the left IFOF/ILF, right FPT, left SLF, and left C‐H for the three groups (p < .05). There was a significant positive correlation between the AD and HAMA scores for the left IFOF/ILF and right EC of the three groups (p < .05). There were significantly positive correlations between the RD/MD and HAMA scores in the left IFOF/ILF of the three groups (p < .05). There was a significant negative correlation between the AD in the right SCP and noise exposure time of the MP and RSP groups (p < .05). The AD, MD, and RD in the left ROI were significantly positively correlated with hearing threshold in the MP and RSP groups (p < .05), whereas FA in the right ROI was significantly positively correlated with the HAMA scores for the three groups (p < .05).Conclusion: The changes in the white matter (WM) microstructure may be related to hearing loss caused by noise exposure, and the WM structural abnormalities in patients with NIHL were mainly located in the syndesmotic fibers of the temporooccipital region, which affected the auditory and language pathways. This confirmed that the auditory pathways have abnormal structural connectivity in patients with NIHL.

Objectives: To investigate changes in brain functional activity among occupational noise-induced hearing loss (NIHL) with tinnitus patients, using resting-state brain magnetic resonance imaging (Rs-fMRI) and voxel-based morphometry (VBM). Methods: Mild (27) and Relative severe (15) patient groups, 30 matched healthy group, scanned by Rs-fMRI and T1WI 3D fast spoiled gradient echo, FSPGR, using voxel-based morphometry and amplitude of low frequency fluctuation to analyze the differences. Results: Brain gray matter volume was significantly different among the three groups in the left occipitotemporal lateral gyrus, the anterior cingulate gyrus, the bilateral angular gyrus, the precuneus, and near the midline area of the cerebellum. The Rs-fMRI changes showed that amplitude of low frequency fluctuation differed significantly among the three groups in the right superior temporal gyrus, the right inferior frontal gyrus, and the right angular gyrus. Conclusion: All this provides a new perspective for understanding the pathophysiological mechanism of occupational noise-induced hearing loss and provides some theoretical bases for subsequent treatment.

Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model.

To investigate diffusion changes suggestive of possible glymphatic alterations in patients with noise-induced hearing loss (NIHL), and to evaluate their relationships with free water (FW) and clinical characteristics. DTI scans were acquired from 48 NIHL patients and 40 healthy controls (HCs). The diffusion tensor imaging along perivascular spaces (DTI-ALPS) technique and FW diffusion model were used to calculate DTI-ALPS and FW values. Between-group differences were assessed using ANCOVA. Spearman correlation analysis was conducted between DTI-ALPS, FW, Hamilton Anxiety Scale (HAMA), and better-ear monaural threshold weighted value (MTWV). Compared with HCs, NIHL patients exhibited significantly reduced DTI-ALPS indices (mean: β = – 0.073, p = 0.008; left: β = – 0.081, p = 0.006; right: β = – 0.068, p = 0.026) and increased FW ( β = 0.017, p = 0.019). Within the NIHL group, the left DTI-ALPS index was significantly lower than the right DTI-ALPS index ( p < 0.001). Significant negative correlations were observed: mean DTI-ALPS with HAMA ( r = – 0.406, p = 0.004) and FW ( r = – 0.510, p < 0.001); left DTI-ALPS with HAMA ( r = – 0.463, p < 0.001) and FW ( r = – 0.459, p = 0.001); and right DTI-ALPS with FW ( r = – 0.488, p < 0.001). All above results remained significant after FDR correction. Correlations of DTI-ALPS with MTWV (mean: r = – 0.311, p = 0.031; left: r = – 0.295, p = 0.042; right: r = – 0.287, p = 0.048) and right DTI-ALPS with HAMA ( r = -0.308, p = 0.033) did not survive FDR correction. Patients with NIHL exhibited altered diffusion patterns suggestive of possible glymphatic alterations. These cross-sectional, correlational findings do not permit causal inferences. Longitudinal studies in larger and more diverse cohorts are needed for validation.

Objective: To investigate the changes in the global attributes of structural connectivity (SC) and functional connectivity (FC) graph theory, as well as the coupling topological structure of the whole brain SC-FC in patients with noise-induced hearing loss (NIHL). Methods: 57 NIHL patients and 55 health controls (HCs) were included; resting-state functional magnetic resonance imaging, diffusion tensor imaging, clinical data (scales, laboratory indicators) were collected. The graph theory network analysis of SC and FC, the whole-brain SC-FC coupling were performed, and a correlation analysis was employed to analyze the results in relation to the clinical data. Results: For FC, there was no significant difference in the global indices directly between groups ( P > 0.05, FDR corrected). For SC, the normalized clustering coefficient (γ) and small-worldness (σ) of the NIHL were higher than those of the HCs ( P < 0.05, FDR corrected). There was no significant difference in the SC-FC coupling strength of whole brain between two groups ( P > 0.05). The graph attribute indices and coupling values of NIHL were correlated to varying degrees with the results of anxiety scale, coagulation, inflammation and biochemical indices ( P < 0.05). Conclusion: The brain network topology structure of NIHL patients is abnormal, but the SC-FC coupling strength does not change significantly. This may provide a basis for understanding the theoretical mechanism of brain neural function remodeling and for future more detailed and diverse studies.