Cortical fNIRS Responses Can Be Better Explained by Loudness Percept than Sound Intensity

Abstract

Objectives: Functional near-infrared spectroscopy (fNIRS) is a brain imaging technique particularly suitable for hearing studies. However, the nature of fNIRS responses to auditory stimuli presented at different stimulus intensities is not well understood. In this study, we investigated whether fNIRS response amplitude was better predicted by stimulus properties (intensity) or individually perceived attributes (loudness). Design: Twenty-two young adults were included in this experimental study. Four different stimulus intensities of a broadband noise were used as stimuli. First, loudness estimates for each stimulus intensity were measured for each participant. Then, the 4 stimulation intensities were presented in counterbalanced order while recording hemoglobin saturation changes from cortical auditory brain areas. The fNIRS response was analyzed in a general linear model design, using 3 different regressors: a non-modulated, an intensity-modulated, and a loudness-modulated regressor. Results: Higher intensity stimuli resulted in higher amplitude fNIRS responses. The relationship between stimulus intensity and fNIRS response amplitude was better explained using a regressor based on individually estimated loudness estimates compared with a regressor modulated by stimulus intensity alone. Conclusions: Brain activation in response to different stimulus intensities is more reliant upon individual loudness sensation than physical stimulus properties. Therefore, in measurements using different auditory stimulus intensities or subjective hearing parameters, loudness estimates should be examined when interpreting results.

Analysis methods for measuring passive auditory fNIRS responses generated by a block-design paradigm

“…As expected, applying the short-channel method to remove systemic components resulted in a substantial improvement in the detection rate. 41 , 51 – 53 Although it is common to use just the first or second PCs as regressors, 2 we observed that including all components resulted in the best performance, consistent with Huppert. 74 Including either all PCA components, the mean, or all individual short channels simplifies analysis as these approaches do not require a specific selection criterion, making them easy to implement, describe, and replicate.…”

“…A primary use has been the investigation of cortical processing of physical qualities of sound, such as intensity, amplitude and frequency modulations, and auditory-spatial cues. 2 – 4 fNIRS has also been employed to evaluate the perceptual qualities of speech and listening effort and language development in normal-hearing and hearing-impaired populations. 5 – 14 Research questions relating to the development of auditory cortical function 15 and cortical reorganization following impaired sensory input and subsequent rehabilitation 16 , 17 have been investigated using fNIRS, as have outcomes related to cochlear implantation 5 and auditory pathologies such as tinnitus.…”

Analysis methods for measuring fNIRS responses generated by a block-design paradigm

“…As expected, applying the short-channel method to remove systemic components resulted in a substantial improvement to the detection rate (Santosa et al, 2020;Scholkmann et al, 2014;Tak and Ye, 2014;Wyser et al, 2020). Although it is common to use just the first or second principal components as regressors (Weder et al, 2020), we observed that including all components resulted in the best performance, consistent with Santosa et al (2020).…”

“…A primary use has been the investigation of cortical processing of physical qualities of sound, such as intensity, and amplitude and frequency modulations, and auditory-spatial cues (Weder et al, 2020;Weder et al, 2018;Zhang et al, 2018). fNIRS has also been employed to evaluate the perceptual qualities of speech and listening effort, as well as language development in normal-hearing and hearing-impaired populations (Anderson et al, 2019;Lawrence et al, 2018;Mushtaq et al, 2019;Pollonini et al, 2014;Rovetti et al, 2019;Rowland et al, 2018;Sevy et al, 2010;Wiggins et al, 2016b;Wijayasiri et al, 2017;Zhang et al, 2020).…”

Auditory cortex activation is modulated nonlinearly by stimulation duration: A functional near-infrared spectroscopy (fNIRS) study

“…A good knowledge of the interrelationship between stimulus parameters and the functional imaging signal response is critical for designing the experimental protocol and interpreting cortical activation. Initially, one should carefully consider auditory stimulus properties, which include intensity, frequency modulation, single stimulus duration, and spatial cues (Bauernfeind et al, 2018;Weder et al, 2018Weder et al, , 2020Zhang et al, 2018). Then, stimulation parameters should be optimally chosen, including stimulus presentation rate (Binder et al, 1994), interstimulus intervals, and block stimulation durations (Robson et al, 1998;Hu et al, 2010).…”