Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections.

Patients with age-related hearing loss face hearing difficulties in daily life. The causes of age-related hearing loss are complex and include changes in peripheral hearing, central processing, and cognitive-related abilities. Furthermore, the factors by which aging relates to hearing loss via changes in auditory processing ability are still unclear. In this cross-sectional study, we evaluated 27 older adults (over 60 years old) with age-related hearing loss, 21 older adults (over 60 years old) with normal hearing, and 30 younger subjects (18-30 years old) with normal hearing. We used the outcome of the upper-threshold test, including the time-compressed threshold and the speech recognition threshold in noisy conditions, as a behavioral indicator of auditory processing ability. We also used electroencephalography to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state. The time-compressed threshold and speech recognition threshold data indicated significant differences among the groups. In patients with age-related hearing loss, information masking (babble noise) had a greater effect than energy masking (speech-shaped noise) on processing difficulties. In terms of resting-state electroencephalography signals, we observed enhanced frontal lobe (Brodmann's area, BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing, and greater activation in the parietal (BA7) and occipital (BA19) lobes in the individuals with age-related hearing loss compared with the younger adults. Our functional connection analysis suggested that compared with younger people, the older adults with normal hearing exhibited enhanced connections among networks, including the default mode network, sensorimotor network, cingulo-opercular network, occipital network, and frontoparietal network. These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced auditory processing capabilities and that hearing loss accelerates the decline in speech comprehension, especially in speech competition situations. Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the top-down active listening mechanism, while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.

Tinnitus Abnormal Brain Region Detection Based on Dynamic Causal Modeling and Exponential Ranking.

Dynamic Causal Modeling (DCM) has been extended for the analysis of electroencephalography (EEG) based on a specific biophysical and neurobiological generative model for EEG. Comparing to methods that summarize neural activities with linear relationships, the generative model enables DCM to better describe how signals are generated and better reveal the underlying mechanism of the activities occurring in human brains. Since DCM provides us with an approach to the effective connectivity between brain areas, with exponential ranking, the abnormality of the observed signals can be further located to a specific brain region. In this paper, a combination of DCM and exponential ranking is proposed as a new method aiming at searching for the abnormal brain regions which are associated with chronic tinnitus.

Brain Network Regional Synchrony Analysis in Deafness.

Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method.