Discrimination of speech stimuli based on neuronal response phase patterns depends on acoustics but not comprehension

Abstract

Speech stimuli give rise to neural activity in the listener that can be observed as waveforms using magnetoencephalography. Although waveforms vary greatly from trial to trial due to activity unrelated to the stimulus, it has been demonstrated that spoken sentences can be discriminated based on theta-band (3-7 Hz) phase patterns in single-trial response waveforms. Furthermore, manipulations of the speech signal envelope and fine structure that reduced intelligibility were found to produce correlated reductions in discrimination performance, suggesting a relationship between theta-band phase patterns and speech comprehension. This study investigates the nature of this relationship, hypothesizing that theta-band phase patterns primarily reflect cortical processing of low-frequency (<40 Hz) modulations present in the acoustic signal and required for intelligibility, rather than processing exclusively related to comprehension (e.g., lexical, syntactic, semantic). Using stimuli that are quite similar to normal spoken sentences in terms of low-frequency modulation characteristics but are unintelligible (i.e., their time-inverted counterparts), we find that discrimination performance based on theta-band phase patterns is equal for both types of stimuli. Consistent with earlier findings, we also observe that whereas theta-band phase patterns differ across stimuli, power patterns do not. We use a simulation model of the single-trial response to spoken sentence stimuli to demonstrate that phase-locked responses to low-frequency modulations of the acoustic signal can account not only for the phase but also for the power results. The simulation offers insight into the interpretation of the empirical results with respect to phase-resetting and power-enhancement models of the evoked response.

Fig. 1.

Topography of grand-average M100 peak response to 1,000 Hz tones based on grayscale map of magnetic field absolute values from highest (white) to lowest (black). Auditory cortex activity channel set (80 channels) is indicated by white “” marks.

Fig. 2.

Example waveforms for the signal, the envelope of the signal, the derivative of the envelope (absolute value), and the convolution of the derivative of the envelope (absolute value) with the theta-band response (sentence 1 in stimulus set 1).

Fig. 3.

Mean power for stimulus set 1 of (A) envelope and derivative of envelope (absolute value) and (B) evoked responses used in simulations. The evoked response was the envelope in simulation 1 and the convolution of the envelope derivative (absolute value) with the transient theta-band response in simulation 2.

Fig. 4.

Grand averages for phase and power dissimilarity index spectra based on 80 auditory channels for a) normal sentences. b) time-reversed sentences. Circle markers indicate consistent positive or negative values across all subjects. Dashed lines represent the random-effects threshold.

Fig. 5.

Theta-band phase dissimilarity index topography. Subject number followed by stimulus number in parentheses shown on left. Grayscale map of dissimilarity index values from highest (white) to lowest (black) shown for normal (left map column) and time-reversed (right map column) sentences. Note clusters of highest dissimilarity in vicinity of auditory cortex channels in all cases. The top 20 theta phase dissimilarity channels within the set of auditory channels are indicated by black “” marks.

Fig. 6.

Dissimilarity indices and classification performance. Subject number followed by stimulus number in parentheses shown on the left. Graph pairs shown for normal (2 left columns) and time-reversed (2 right columns) sentences. First graph in pair displays phase and power dissimilarity indices averaged across 20 auditory channels having the strongest theta-band phase dissimilarity (theta-band phase dissimilarity values circled; error bars represent SE). Second graph in pair displays fraction of trials for each sentence classified as matching template 1 (black), 2 (gray), and 3 (white) based on theta-band phase information for the 20 channels.

Fig. 7.

Grand-average theta-band phase dissimilarity and classification results for normal (white) and time-reversed (black) sentences. Error bars represent SE. A: theta-band phase dissimilarity index values for the top channel in the left and right hemispheres and the top 20-channel mean. B: percentage correct classification for all trials.

Fig. 8.

Grand average of phase and power dissimilarity results for 6 data sets. Error bars show 95% confidence limits based on bootstrap resampling. A: empirical results for top channel response to normal stimuli in 6 subjects B: simulation 1 results. Evoked response is proportional to stimulus envelope. C: simulation 2 results. Evoked response is proportional to derivative of envelope (absolute value) convolved with a theta-band response. D: simulation results assuming single-trial responses consist of noise only (no evoked response).

Fig. 9.

Theta (4 Hz bin) phase and power distributions over 10k random trials based on fast Fourier transform for 500 ms data segment (500 ms window). Signal is a single one-cycle 5 Hz sine wave of amplitude 0.082 that begins at 100 ms. Noise is random1/f noise as described in methods. A: phase. For noise only, mean = 0, SD = 1.8. For noise plus signal, mean = −0.12, SD = 1.5. B: power. For noise only, mean = 1.82, SD = 1.76. For signal plus noise, mean = 1.98, SD = 1.89.