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Functional reorganization is a response to auditory deficits or deprivation, and less is known about the overall brain network alterations involving resting-state networks (RSNs) and multiple functional networks in patients with occupational noise-induced hearing loss (NIHL). So this study evaluated resting-state functional network connectivity (FNC) alterations in occupational NIHL using an independent component analysis (ICA). In total, 79 mild NIHL patients (MP), 32 relatively severe NIHL patients (RSP), and 84 age- and education- matched healthy controls (HC) were recruited. All subjects were tested using the Mini-mental State Examination scale, the tinnitus Handicap Inventory scale, the Hamilton Anxiety scale (HAMA) and scanned by T1-3DFSPGR, resting-state functional magnetic resonance imaging sequence in 3.0 T and analysed by the ICA. Seven RSNs were identified, compared with the HC, the MP showed increased FNC within the executive control network (ECN) and enhanced FNC within the default mode network (DMN) and the visual network (VN); compared with the HC, the RSP showed decreased FNC within the ECN and auditory network (AUN), DMN and VN; no significant changes in FNC were found in the MP compared with the RSP. Furthermore, the correlation analysis between the noise exposure time and hearing loss level, HAMA were both negative, and there were no significant correlations between the abnormal RSNs and the hearing level, noise exposure time and HAMA. These findings indicate that different degrees of NIHL involve different alterations in RSNs connectivity and may reveal the neural mechanisms related to emotion-related features and functional abnormalities following long-term NIHL.
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http://dx.doi.org/10.1111/ejn.16266 | DOI Listing |
IEEE Trans Med Imaging
September 2025
Analyzing the spontaneous activity of the human brain using dynamic approaches can reveal functional organizations. The co-activation pattern (CAP) analysis of signals from different brain regions is used to characterize brain neural networks that may serve specialized functions. However, CAP is based on spatial information but ignores temporal reproducible transition patterns, and lacks robustness to low signal-to-noise rate (SNR) data.
View Article and Find Full Text PDFFront Neurosci
August 2025
Department of Neurology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Objective: Construct a predictive model for rehabilitation outcomes in ischemic stroke patients 3 months post-stroke using resting state functional magnetic resonance imaging (fMRI) images, as well as synchronized electroencephalography (EEG) and electromyography (EMG) time series data.
Methods: A total of 102 hemiplegic patients with ischemic stroke were recruited. Resting - state functional magnetic resonance imaging (fMRI) scans were carried out on all patients and 86 of them underwent simultaneous electroencephalogram (EEG) and electromyogram (EMG) examinations.
Imaging Neurosci (Camb)
September 2025
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States.
Spatial similarity of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial similarity is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial similarity is limited to comparing two samples at a time.
View Article and Find Full Text PDFNeuroimage Rep
September 2025
Arizona State University, Tempe, AZ, 85287, USA.
Non-intrusive neuroimaging technology offers fast and robust diagnostic tools for neuro-disorder disease diagnosis, such as Attention-Deficit/Hyperactivity Disorder (ADHD). Resting-state functional magnetic imaging (rs-fMRI) has been demonstrated to have great potential for such applications due to its unique capability and convenience in providing spatial-temporal brain imaging. One critical challenge of using rs-fMRI data is the high dimensionality for both spatial and temporal domains.
View Article and Find Full Text PDFFront Neurol
August 2025
The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Background: After stroke, upper limb dysfunction seriously affects patients' quality of life. The uncertain prognosis of patients poses a challenge for therapists in developing personalized rehabilitation programs. Electroencephalograph (EEG) power spectrum changes during rehabilitation training may have a predictive effect on the improvement of upper limb movement.
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