Quantitative imaging in medicine and surgery · 2025
Aberrant static and dynamic functional network connectivity in patients with noise-induced hearing loss.
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Huang Ranran, Wang Aijie, Li Yunxin, Bao Xianghua, Wang Liping, Lv Minghui, Pan Xuhong, Zhang Guowei
Аннотация
BACKGROUND: Brain alterations related to "cortical plasticity" in patients with noise-induced hearing loss (NIHL) have been reported in studies related to hearing deprivation. This study aimed to investigate static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) "cortical plasticity" features in patients with NIHL using independent component analysis (ICA).
METHODS: A total of 69 NIHL patients and 57 age- and education-matched healthy controls (HCs) were enrolled in this study. All participants were tested using the Mini-Mental State Examination (MMSE), Hamilton Anxiety Rating Scale (HAMA), and Tinnitus Handicap Inventory (THI), and scanned by three-dimensional T1-weighted imaging fast spoiled gradient echo (3D-T1WIFSPGR), resting-state functional magnetic resonance imaging (RS-fMRI) sequence. Resting-state networks (RSNs) were established based on ICA, and sFNC and dFNC analyses were performed. Associations between sFNC/dFNC abnormalities and clinical features related to NIHL were analyzed.
RESULTS: A total of 11 RSNs and 23 independent components (ICs) were identified, including auditory network (AUN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), executive control network (ECN), higher visual network (HVIN), primary visual network (PVIN), left precuneus network, right precuneus network, salience network (SAN), and sensorimotor network (SMN). Compared with HCs, NIHL patients had increased sFNC between VAN (IC19) and ECN (IC29) [t=-3.454, P<0.05, false discovery rate (FDR) corrected]. During dFNC analysis, five reoccurring brain states were identified. Compared with HCs, NIHL patients had increased dFNC between VAN (IC19) and ECN (IC29) in state-5 (t=-4.202, P<0.05, FDR corrected), and there were no significant differences in the fraction rate time (FT) and mean dwell time (MDT) of all five states, and the number of transitions (NT) in NIHL patients. The correlation analyses between noise exposure time, hearing threshold, and HAMA were all positive (hearing and exposure time: r=0.263, P=0.029; THI and exposure time: r=0.415, P<0.001). There was a significant positive correlation between abnormal sFNC and HAMA (r=0.188, P=0.035). There was a significant positive correlation between the FT (state-1), MDT (state-1), and HAMA [FT (state-1) and HAMA: r=0.243, P=0.006; MDT (state-1) and HAMA: r=0.300, P=0.001]. There was a significant positive correlation between MDT (state-2) and THI (r=0.247, P=0.041). There was a significant negative correlation between FT (state-1) and THI (r=-0.255, P=0.035). There was a significant negative correlation between the FT (state-5) and hearing threshold (r=-0.242, P=0.045).
CONCLUSIONS: Our findings support the view that hearing impairment due to exposure to excessive environmental noise can lead to changes in a variety of brain functions and provide new evidence that abnormalities in both sFNC and dFNC consist of shared and unique features in NIHL. sFNC combined with dFNC can provide a comprehensive view of brain activity, which contributes to a further understanding of the mechanism of abnormal brain functional activities in NIHL.
METHODS: A total of 69 NIHL patients and 57 age- and education-matched healthy controls (HCs) were enrolled in this study. All participants were tested using the Mini-Mental State Examination (MMSE), Hamilton Anxiety Rating Scale (HAMA), and Tinnitus Handicap Inventory (THI), and scanned by three-dimensional T1-weighted imaging fast spoiled gradient echo (3D-T1WIFSPGR), resting-state functional magnetic resonance imaging (RS-fMRI) sequence. Resting-state networks (RSNs) were established based on ICA, and sFNC and dFNC analyses were performed. Associations between sFNC/dFNC abnormalities and clinical features related to NIHL were analyzed.
RESULTS: A total of 11 RSNs and 23 independent components (ICs) were identified, including auditory network (AUN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), executive control network (ECN), higher visual network (HVIN), primary visual network (PVIN), left precuneus network, right precuneus network, salience network (SAN), and sensorimotor network (SMN). Compared with HCs, NIHL patients had increased sFNC between VAN (IC19) and ECN (IC29) [t=-3.454, P<0.05, false discovery rate (FDR) corrected]. During dFNC analysis, five reoccurring brain states were identified. Compared with HCs, NIHL patients had increased dFNC between VAN (IC19) and ECN (IC29) in state-5 (t=-4.202, P<0.05, FDR corrected), and there were no significant differences in the fraction rate time (FT) and mean dwell time (MDT) of all five states, and the number of transitions (NT) in NIHL patients. The correlation analyses between noise exposure time, hearing threshold, and HAMA were all positive (hearing and exposure time: r=0.263, P=0.029; THI and exposure time: r=0.415, P<0.001). There was a significant positive correlation between abnormal sFNC and HAMA (r=0.188, P=0.035). There was a significant positive correlation between the FT (state-1), MDT (state-1), and HAMA [FT (state-1) and HAMA: r=0.243, P=0.006; MDT (state-1) and HAMA: r=0.300, P=0.001]. There was a significant positive correlation between MDT (state-2) and THI (r=0.247, P=0.041). There was a significant negative correlation between FT (state-1) and THI (r=-0.255, P=0.035). There was a significant negative correlation between the FT (state-5) and hearing threshold (r=-0.242, P=0.045).
CONCLUSIONS: Our findings support the view that hearing impairment due to exposure to excessive environmental noise can lead to changes in a variety of brain functions and provide new evidence that abnormalities in both sFNC and dFNC consist of shared and unique features in NIHL. sFNC combined with dFNC can provide a comprehensive view of brain activity, which contributes to a further understanding of the mechanism of abnormal brain functional activities in NIHL.