Computational protocol: Brainstem encoding of speech and musical stimuli in congenital amusia: evidence from Cantonese speakers

Similar protocols

Protocol publication

[…] Percentage of correct responses and reaction time were calculated for the behavioral tone identification task. Only trials with correct responses were retained in the reaction time analysis.Main FFR data were analyzed using Matlab (The Mathworks, Natick, MA, USA) scripts adapted from the Brainstem Toolbox (). Before analysis, the stimuli were resampled to 20,000 Hz, to match the sampling rate of the responses. Both the stimuli and responses were band-pass filtered from 80 to 2500 Hz to remove the slower cortical ERPs and attenuate EEG noise above the limit of phase-locking (; ). The FFR was assumed to encompass the entire duration of the stimulus (175 ms).Using a sliding window analysis procedure (; ), 50-ms bins of the FFR were shifted in 1-ms steps to produce a total of 125 (= 175–50) Hanning-windowed overlapping bins in the frequency domain. A narrow-band spectrogram was calculated for each FFR bin by applying the fast Fourier transform (FFT). To increase spectral resolution, each time bin was zero-padded to 1 s before performing the FFT. The spectrogram gave an estimate of spectral energy over time and the F0 (pitch) contour was extracted from the spectrogram by finding the spectral peak closest to the expected (stimulus) frequency. Both F0 frequency and amplitude were recorded for each time bin. The same short-term spectral analysis procedure was applied to the stimulus waveforms, in order to compare the responses with the stimuli. The following time, pitch, and amplitude measurements were extracted to determine how well the amusic brainstem encodes the timing, periodicity, and the spectral envelop of the evoking stimuli compared to controls (; ; ; ).Neural lag (in ms) is the amount of time shift to achieve the maximum positive correlation between the waveforms of the stimulus and the response. This measure was calculated using a cross-correlation technique that slid the stimulus and response waveforms back and forth in time with respect to one another until the maximum positive correlation between the two was found. It estimated the FFR latency due to the neural conduction time of the auditory system for each tone/participant, which was taken into account when calculating each of the following measurements.Pitch strength (or autocorrelation, values between –1 and 1) is a measure of periodicity and phase locking of the response. Using a short-time running autocorrelation technique, the response, in 50-ms time bins, was successively time-shifted in 1-ms steps with a delayed version of itself, and a Pearson’s r was calculated at each 1-ms interval. The maximum (peak) autocorrelation value was recorded for each bin, with higher values indicating more periodic time frames. Pitch strength was calculated by averaging the autocorrelation peaks (r-values) from the 125 bins for each tone/participant.Pitch error (in Hz) is the average absolute Euclidian distance between the stimulus F0 and response F0 across the total of 125 time bins analyzed. It is a measure of pitch encoding accuracy of the FFR over the entire duration of the stimulus, shifted in time to match with the response based on the specific neural lag value obtained for each tone/participant.Stimulus-to-response correlation (values between –1 and 1) is the Pearson’s correlation coefficient (r) between the stimulus and response F0 contours (shifted in time by neural lag). This measure indicates both the strength and direction of the linear relationship between the two signals.Root mean square (RMS) amplitude (in μV) of the FFR waveform is the magnitude of neural activation over the entire FFR period (neural lag, neural lag +175 ms).Signal-to-noise ratio is the ratio of the RMS amplitude of the response over the RMS of the pre-stimulus period (50 ms).Mean amplitudes of the first three harmonics (in dB, indicating peak amplitudes of the power spectrum) are spectral peaks within the frequency ranges of the fundamental frequency (F0, first harmonic) and the next two harmonics (second and third harmonics). They were calculated by finding the largest spectral peaks in the frequency ranges of the first three harmonics in the narrow-band spectrogram after applying the short-time Fourier transform (STFT).Statistical analyses were conducted using R (). For parametric statistical analyses, r-values (pitch strength, stimulus-to-response correlation) were converted to z′-scores using Fisher’s transformation (), percent correct scores for tone identification was converted using rationalized arcsine transformation (), and reaction times were transformed using log transformation (), since these measures deviated from normal distributions (Shapiro–Wilk normality test: all ps < 0.05). Linear mixed-effects models were fit on all measures using the R package ‘nlme’ (). Post hoc pairwise comparisons were conducted using t-tests with p-values adjusted with the method. […]

Pipeline specifications

Software tools lme4, nlme
Application Mathematical modeling
Organisms Homo sapiens