Abstract
Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments. The translation of sensory information into perceptual decisions for goal-directed tasks hinges on dynamic changes in neural activity. However, the underlying brain network dynamics involved in this process are not well specified. In this study, we collected functional MRI data of participants engaging in auditory discrimination and auditory working memory tasks. Independent component analysis (ICA) was performed to extract the brain networks involved and the sliding-window functional connectivity (FC) among networks was calculated. Next, a temporal clustering technique was used to identify the brain states underlying auditory processing. Our results identified seven networks configured into four brain states. The number of brain state transitions was negatively correlated with auditory discrimination performance, and the fractional dwell time of State 2-which included connectivity among the triple high-order cognitive networks and the auditory network (AN)-was positively correlated with working memory performance. A comparison of the two tasks showed significant differences in the connectivity of the frontoparietal, default mode, and sensorimotor networks (SMNs), which is consistent with previous studies of the modulation of task load on brain network interaction. In summary, the dynamic network analysis employed in this study allowed us to isolate moment-to-moment fluctuations in inter-network synchrony, find network configuration in each state, and identify the specific state that enables fast, effective performance during auditory processing. This information is important for understanding the key neural mechanisms underlying goal-directed auditory tasks.
Highlights
Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information are critical for communication and problem solving in everyday acoustic environments (Huang et al, 2013)
After removing the components related to artifacts, we selected 14 task-related brain network components based on the spatial maps and frequency distribution as mentioned in the method section
The extracted 14 independent components were distributed in 7 functional networks, including the auditory network (AN), the visual network (VN), the sensorimotor network (SMN), the cerebellar network (CER), the frontoparietal network (FPN), the default mode network (DMN), FIGURE 2 | The whole data processing steps
Summary
Both perceiving and processing external sound stimuli as well as actively maintaining and updating relevant information (i.e., working memory) are critical for communication and problem solving in everyday acoustic environments (Huang et al, 2013). The dynamic changes in brain networks involved in this process are not well specified. Resting state fMRI measurements have shown that a brain network of auditory modalityspecific areas in the temporal lobe participate in auditory processing (Damoiseaux et al, 2006). Task fMRI studies based on different cognitive loads have reported that distinct cortical networks were activated by auditory attention and working memory load (Huang et al, 2013), and FC between the supratemporal plane (STP) and inferior parietal lobule (IPL) in the auditory network (AN) was modulated when discriminating and actively maintaining different pitch-varying sounds (Hakkinen and Rinne, 2018). This study found that intra-network connectivity was stronger in one language than in another (Jung et al, 2018)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
