Abstract
Although auditory processing has been widely studied with conventional parametric methods, there have been a limited number of independent component analysis (ICA) applications in this area. The purpose of this study was to examine spatiotemporal behavior of brain networks in response to passive auditory stimulation using ICA. Continuous broadband noise was presented binaurally to 19 subjects with normal hearing. ICA was performed to segregate spatial networks, which were subsequently classified according to their temporal relation to the stimulus using power spectrum analysis. Classification of separated networks resulted in 3 stimulus-activated, 9 stimulus-deactivated, 2 stimulus-neutral (stimulus-dependent but not correlated with the stimulation timing), and 2 stimulus-unrelated (fluctuations that did not follow the stimulus cycles) components. As a result of such classification, spatiotemporal subdivisions were observed in a number of cortical structures, namely auditory, cingulate, and sensorimotor cortices, where parts of the same cortical network responded to the stimulus with different temporal patterns. The majority of the classified networks seemed to comprise subparts of the known resting-state networks (RSNs); however, they displayed different temporal behavior in response to the auditory stimulus, indicating stimulus-dependent temporal segregation of RSNs. Only one of nine deactivated networks coincided with the “classic” default-mode network, suggesting the existence of a stimulus-dependent default-mode network, different from that commonly accepted.
Highlights
Auditory processing has been extensively studied using conventional regression methods such as the general linear model (GLM)
The sound measurement system was a nonmetallic optical microphone integrated in a headphone, which detected the sound intensity in the ear during imaging on a real-time basis and simultaneously provided passive protection against the scanner noise [20]
Fast Fourier transform (FFT) and correlation with stimulus presentation timing (SPT) were conducted for the time courses of all the components
Summary
Auditory processing has been extensively studied using conventional regression methods such as the general linear model (GLM). ICA, on the contrary, is able to isolate networks representing various noise sources, which could facilitate identification of auditory stimulusinduced or unrelated processes [1,2] Another distinct feature of ICA, and its advantage as a data-driven approach, is that it does not require an a priori response model of brain activity. The absence of the prior hypothesis makes it difficult to interpret the resulting ICs, separating ‘‘meaningful’’ components reflecting neurobiological and biophysical processes from those reflecting signal artifacts or noise This issue has been addressed using various approaches in previous studies [1,3,4,5,6,7,8,9].
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.
