EEG Spatiotemporal Patterns Underlying Self-other Voice Discrimination

Abstract

There is growing evidence showing that the representation of the human "self" recruits special systems across different functions and modalities. Compared to self-face and self-body representations, few studies have investigated neural underpinnings specific to self-voice. Moreover, self-voice stimuli in those studies were consistently presented through air and lacking bone conduction, rendering the sound of self-voice stimuli different to the self-voice heard during natural speech. Here, we combined psychophysics, voice-morphing technology, and high-density EEG in order to identify the spatiotemporal patterns underlying self-other voice discrimination (SOVD) in a population of 26 healthy participants, both with air- and bone-conducted stimuli. We identified a self-voice-specific EEG topographic map occurring around 345 ms post-stimulus and activating a network involving insula, cingulate cortex, and medial temporal lobe structures. Occurrence of this map was modulated both with SOVD task performance and bone conduction. Specifically, the better participants performed at SOVD task, the less frequently they activated this network. In addition, the same network was recruited less frequently with bone conduction, which, accordingly, increased the SOVD task performance. This work could have an important clinical impact. Indeed, it reveals neural correlates of SOVD impairments, believed to account for auditory-verbal hallucinations, a common and highly distressing psychiatric symptom.

Keywords: bone conduction; high-density EEG; insula; limbic system; self-other voice discrimination.

Figure 1

SOVD task. (A) Stimuli. Six self-other voice morphs between participant’s voice Self, black and the voice of a gender-matched unfamiliar person (Other, gray) were randomly presented 50 times throughout the experiment. (B) Task. Voice morphs were presented either through air (laptop, above) or bone conduction (commercial headset, below). In every trial, participants responded whether the morph they hear resembles more to their or to someone else’s voice by clicking on the corresponding mouse button. (C) EEG setup. Bone conduction headphones (black) placed under a high-density EEG cap (light gray spheres and connections) formed by 256 electrodes organized as an extension of the standard clinical 10–20 setup (electrode names indicated in black).

Figure 2

ERP components analysis. Butterfly plot of the ERP waveform of all electrodes overlapped (A) and of the GFP (B) for each of the four experimental conditions (Bone-Other, Bone-Self, Air-Other Air-Self) are represented. The gray dashed boxes in (A) and (B) indicate the time-windows selected for the auditory ERP components (P1, N1, P2, P3/N4). Peak and latency of the ERP components were defined in the associated time windows and then used for the electrode-wise and GFP ANOVA analyses. In (C) the averaged-topographies (across subjects, conditions, and time-window) associated to each ERP component are shown.

Figure 3

Behavioral results indicating the effects of the two forms of sound conduction (air, bone) on accuracy (left) and response times (right) in self-other voice discrimination task. The abscissa of both plots indicates the percentage of the self-voice present in a Voice Morph. The shaded areas around each curve represent the 95% confidence intervals. Accuracy was higher and response times were faster for bone conduction.

Figure 4

Electrode-wise ANOVA results. The ANOVA conducted for each peak and each latency of the auditory ERPs components, corrected for multiple comparisons, showed a main effect of Conduction (P < 0.0002) for the latency of N1 component over the electrodes 113 and 155. They are located over the right centro-parietal area (A) and their waveform is shown in (B), for each of the four experimental conditions.

Figure 5

Topographical differences. The TANOVA across the period [0–500] ms revealed a significant main effect of the Conduction (P < 0.05) for N1 (B) and the late complex P3/N4 (A). The topographies associated to the Bone and Air and averaged in the periods of significance (gray dashed boxes) are shown (B).

Figure 6

Results of the group-average ERP segmentation. (A) Different colors indicate different segments marked under the Global Field Power curves extracted by the K-means clustering on the group-averaged ERPs corresponding to the Other-dominant (15–30% self-voice) and Self-dominant (70–85%) Voice Morphs and to the two types of sound conduction (Bone, Air). The gray dashed boxes indicate the three time-windows considered for the back-fitting procedure. (B) Topographic maps associated to each segment. The “+” symbol indicates the position of the electrodes exhibiting the highest (positive, red) and the lower (negative, blue) amplitudes of the scalp voltage potential.

Figure 7

Experimental effects on map 4 occurrence. Map 4 occurred more for self-dominant morphs (A) and when stimuli were presented through air conduction (B). Horizontal lines in boxplots indicate median, whereas dots mean values. The whiskers extend to 1.5 interquartile range and the remaining dots are outliers. Map 4 occurrence was negatively correlated to task accuracy (C) and positively to response times (D), specifically for self-dominant stimuli presented through air conduction. Shaded areas around linear regressions represent 95% confidence intervals. ^*: P < 0.05; ^**: P < 0.001.

Figure 8

Brain network associated to map 4. The axial (A), coronal (B), and sagittal (C) view of the source current density estimated for map 4 are shown. Details on the anatomical regions forming the network are reported in Table 2. Green-to-red blobs indicate activation above the 95th percentile of the distribution of the total activation values (color-bar on the top left). Red lines indicate the region of maximal activation. R: right.