The effect of exercise combined with Transcranial direct-current stimulation on the temporal fine structure sensitivity

Authors: 欢 姜1,  1


Background: In noisy environments, the temporal fine structure (TFS) of sound is crucial for understanding speech. But age and hearing loss reduce our sensitivity to TFS, impairing our ability to comprehend speech. Although physical exercise boosts cognitive function and transcranial direct-current stimulation (tDCS) improves sensory function, their combined effect on TFS sensitivity remains unclear. The study investigated whether combining exercise and tDCS could enhance TFS sensitivity.

Method: Forty-one participants were assessed for TFS sensitivity using internationally recognized tests: TFS1, TFS-LF, and TFS-AF. The Physical Activity Rating was used to measure participants’ physical exercise, from intensity, frequency, and duration. Participants were randomly assigned to an experimental group or a control group. One day after pretesting, the experimental group underwent 20 minutes of 2 mA anodal tDCS, with the anode over the primary motor cortex (M1) and four cathodes surrounding it; subsequently, TFS sensitivity was retested. The control group’s setup was identical, except without the tDCS application. The test sequence was balanced using a Latin square design.

Result: Paired-sample t-tests revealed a significant increase in TFS sensitivity for the anodal stimulation group (p < 0.001), while no change was observed in the sham stimulation group (p = 0.187). 2 × 2 repeated measures analysis of variance demonstrated that TFS sensitivity significantly increased in the experimental group (p < 0.001). There was no difference in TFS sensitivity between no exercise and received sham stimulation groups.

Conclusion: The study demonstrated combined physical exercise and received anodal tDCS interventions synergistically enhanced TFS sensitivity processing. These findings suggest that non-invasive neuromodulation and physical exercise should be further explored as strategies to improve hearing impairment and language comprehension difficulties.comprehension difficulties.DNNs have shown remarkable performance in various auditory tasks, including speech recognition, speaker identification, and music classification. In this study, we propose a DNN-based approach for hearing-loss compensation, which is trained on the outputs of hearing-impaired and normal-hearing DNN-based auditory models in response to speech signals. We introduce a framework for emulating auditory models using DNNs, focusing on an auditory-nerve model in the auditory pathway. We propose a linearization of the DNN-based approach, which we use to analyze the DNN-based hearing-loss compensation. We evaluate the DNN-based hearing-loss compensation strategies using listening tests with hearing impaired listeners. The results demonstrate that the proposed approach results in feasible hearing-loss compensation strategies.