Data analyses
To verify auditory pathway integrity, wave V in the click ABR was
identified automatically as the major positive peak between 7.70 to 9.90
ms62 and, subsequently, reviewed by visual inspection
for correctness. Wave V peak amplitude and latency were then quantified.
Analysis of the FFR was based on recent guidelines and our protocols for
the neonate FFR39,49,53. First, to avoid that group
differences in the FFR could be explained by spontaneous neuroelectric
fluctuations, the EEG at the pre-stimulus period was analyzed. For that,
the root mean square (RMS) of the EEG amplitude of the 40 ms before
stimulus onset (i.e., -40 to 0 ms) was computed and defined as the
pre-stimulus RMS. Next, to account for the neural transmission delay
from stimulus reception at the cochlea until neural phase locking onset
in the central nervous system, the response’s lag (i.e., neural lag) was
estimated as the time shift with which the correlation between the
stimulus and the response waveforms was maximum. All successive FFR
parameters computed in the present study were calculated from the
frequency-following response onset, which is the stimulus onset plus the
individual neural lag.
To investigate the stimulus encoding, the Fast Fourier Transform (FFT)
was applied. The FFT is an algorithm that transforms the EEG signal in
the time domain to a representation in the frequency domain. The lowest
frequency of a periodic sound such as speech is called fundamental
frequency (F0). Since pitch, defined as a perceptual
correlate of the F063, is a sound
attribute essential for language acquisition64,65, the
analysis was centered on the stimulus F0.
The stimulus F0 spectral amplitude and its normalization
were reported as indexes of response strength for the consonant
transition and for the vowel regions independently, due to the different
complexity in the spectral components of each
region49. To carry out the spectral amplitude
measurement, the mean over a 10 Hz frequency window around the stimulus
F0 (i.e., ±5 Hz to 113 Hz) was retrieved. Its
normalization, termed signal-to-noise ratio (SNR), was calculated as SNR
= 10*log10(Signal spectral power/Noise spectral
power)39, being the noise spectral power defined as
the mean over two 28 Hz frequency windows located at each side of the
signal window. All signal analyses were carried out using Matlab scripts
(R2019b, Mathworks) created by Intelligent Hearing Systems (Miami, FI,
EEUU) and in our laboratory.