Correlation between the frequency difference limen and an index based on principal component analysis of the frequency-following response of normal hearing listeners

Frequency-Following Responses to Complex Tones at Different Frequencies Reflect Different Source Configurations

“…First, source imaging of the FFRs showed that subcortical sources dominated the FFRs to sounds with a pitch at or above 262 Hz (C4; Figure 3), consistent with previous reports (Bidelman, 2015, 2018; Zhang and Gong, 2017). However, the specific generating sites of the FFR could not be identified, largely owing to the limitations of the sLORETA method in spatial resolution.…”

“…A conventional view is that the generators of the FFR are entirely restricted to subcortical nuclei (Chandrasekaran and Kraus, 2010). This is supported by evidence from comparisons between scalp and deep recordings in animal models (Smith et al, 1975), from lesion studies in animal models (Smith et al, 1975; Gardi et al, 1979) and in humans with brainstem injury (Sohmer et al, 1977), and recently from source-reconstruction studies (Bidelman, 2015; Zhang and Gong, 2017). Although they all suggest that the FFR has only subcortical sources, the exact generating site is under debate; some suggest the predominant role of inferior colliculus (IC; Smith et al, 1975; Sohmer et al, 1977), while others regard the FFR as representing integrated activity from multiple nuclei including not only IC but also cochlear nucleus (CN), superior olive complex (SOC), and/or lateral lemniscus (LL; Gardi et al, 1979).…”

Using frequency-following responses (FFRs) to evaluate the auditory function of frequency-modulation (FM) discrimination

“…Pearson's correlation analysis indicated that PS was significantly correlated with JNDs (r = − 0.587, p = 0.045). This negative correlation between neural index and behavioral threshold of tonal sweeps is as expected and consistent with that of FDL (Marmel et al 2013;Zhang and Gong 2017). The similar correlation analysis was conducted for the other four deviation conditions separately, and it was found that correlation was not significant for the fewer degrees (r = − 0.233, p = 0.465 for 20% and r = − 0.431, p = 0.161 Table 1.…”

Neural Correlates of Early Sound Encoding and their Relationship to Speech-in-Noise Perception

“…The MEG-FFR technique may allow us to more consistently observe behavioral and experience-related relationships with FFR-f0 strength in less challenging listening conditions as compared with the EEG-FFR. The EEG-FFR is likely a composite from several subcortical and cortical sources (Herdman et al, 2002 ; Kuwada and Anderson, 2002 ; Coffey et al, 2016b ; King et al, 2016 ; Zhang and Gong, 2016 ; Tichko and Skoe, 2017 ). In recent work, we compared two common single-channel EEG montages (Cz-mastoids and Fz-C7) and found that while FFR-f0 strength in each montage (measured simultaneously) was moderately correlated, a large proportion of variability was unaccounted for and the two methods differed in their sensitivity to a behavioral measure of interest (Coffey et al, 2016a ).…”

“…Rather than relating fundamental encoding recorded in the presence of noise to later performance (which has previously been shown, described above) and might include influences from top-down processes that spontaneously act to separate speech and noise streams, here we reduce the similarity between the conditions of the electrophysiological recording and the offline SIN behavioral task to a single overlapping feature: the presence of pitch-related information. Although several studies have not found a significant relationship between FFR-f0 measured in conditions of silence and SIN performance (e.g., Parbery-Clark et al, 2009a ), such relationships may be obscured by EEG-based FFR recordings which likely blend responses coming from different sources (Zhang and Gong, 2016 ; Tichko and Skoe, 2017 ). Despite the relative insensitivity of magnetoencephalography (MEG) to deep sources (which approximate radial sources, Baillet et al, 2001 ), sufficient information is preserved in the MEG signal for accurate localization of deeper structures such as the hippocampus, amygdala and thalamus (Attal and Schwartz, 2013 ; Dumas et al, 2013 ), and for the contributions from subcortical and cortical FFR generator sites to be separated (Coffey et al, 2016b ), which may increase the sensitivity of the experimental design to behavioral relationships.…”