Journal of Autism and Developmental Disorders

, Volume 47, Issue 6, pp 1904–1909 | Cite as

Brief Report: Biological Sound Processing in Children with Autistic Spectrum Disorder

  • Melissa Lortie
  • Léa Proulx-Bégin
  • Dave Saint-Amour
  • Dominique Cousineau
  • Hugo Théoret
  • Jean-François Lepage
Brief Report

Abstract

There is debate whether social impairments in autism spectrum disorder (ASD) are truly domain-specific, or if they reflect generalized deficits in lower-level cognitive processes. To solve this issue, we used auditory-evoked EEG responses to assess novelty detection (MMN component) and involuntary attentional orientation (P3 component) induced by socially-relevant, human-produced, biological sounds and acoustically-matched control stimuli in children with ASD and controls. Results show that early sensory and novelty processing of biological stimuli are preserved in ASD, but that automatic attentional orientation for biological sounds is markedly altered. These results support the notion that at least some cognitive processes of ASD are specifically altered when it comes to processing social stimuli.

Keywords

Social impairments Event related potentials EEG Biological actions Auditory Attention Novelty detection MMN P3 

Introduction

Social impairments are a defining feature of autism spectrum disorder (ASD) (APA 2013). The leading hypothesis to explain these difficulties involves abnormal function of the “social brain”, a network of subcortical and cortical regions specialized in the processing of socially-relevant stimuli, such as face, eye gaze, biological motion, and other stimuli indicating the presence of a nearby human (Pelphrey et al. 2011). However, recent evidence suggests that aberrant functioning of this network may be secondary to abnormal low-level mechanisms such as attention and sensory processing also seen in ASD (Baum et al. 2015; DiCriscio et al. 2016; Karhson and Golob 2016; Sinclair et al. 2016). Whether these anomalies in early-cognitive mechanisms bear a disproportionate impact on the processing of biological and social stimuli is under debate (Leekam 2015); although preferential orientation towards faces (Guillon et al. 2016), biological motion (Klin et al. 2009), and other biological stimuli (Dawson et al. 1998) is altered in young children with ASD, recent research shows that oculomotor and attentional abnormalities are also present when using a wide array of non-social visual stimuli (Wass et al. 2015). These observations raise the possibility that a core element of ASD—and a diagnostic criterion—such as social impairments might not be a “primary” feature of the disorder, but simply the most noticeable outcome of generalized and non-specific abnormalities in sensory and attentional processes (Leekam 2015).

While numerous studies investigated sensory and attentional processing of social versus non-social stimuli in ASD, most of them relied on behavioural measures and visual stimuli. Visual stimuli demand high-order attentional and oculomotor control, components that appear altered in ASD (DiCriscio et al. 2016; Wass et al. 2015). On the other hand, a handful of studies focusing on speech used auditory evoked event related potentials (ERP) to assess sensory and cognitive processing of social versus non-social stimuli (Fan and Cheng 2014; Lepistö et al. 2008, 2006; Whitehouse and Bishop 2008). While results from these studies support the presence of sensory and attentional alterations specific to speech and vocal stimuli (Kujala et al. 2013), it remains unknown whether these abnormalities are confined to language, or if they encompass other socially relevant, human-related, stimuli as well. Here, we used EEG to assess novelty detection (mismatch negativity response ERP; MMN) and attention orientation (P3 ERP) in children with ASD when exposed to acoustic stimuli that are not related to speech, but denoting human motor actions.

Materials and Methods

Participants

Eleven children with ASD and 13 age-matched healthy controls participated in this study. ASD diagnostic was established by a psychiatrist using based on DSM-IV-TR criteria. One participant in each group was rejected because of a failure to complete the experimental protocol, resulting in 12 participants in the control group (8 males; mean age 6.72 ± 1.03 years-old; range 5.25–7.91) and 10 participants in the ASD group (9 males; mean age 6.71 ± 0.68 years-old; range 5.58–7.41). All participants were in good physical health, with normal hearing and vision, and were not using psychotropic medication at the time of testing. Informed and signed consent was obtained from all participants’ legal tutor or parent, and verbal assent was obtained from participants prior to experiment. The study was approved by the ethics committee of the institution and was conducted in conformity with the 1964 Declaration of Helsinki.

Stimuli

Stimuli consisted of two biological sounds, representing a finger-click and mouth suction, and two acoustically matched control sounds. The finger-click sound was produced by applying a firm pressure of the thumb placed on the outside of the middle finger making the fingers slip, producing a sound when hitting the palm of the hand. The mouth suction sound was made by building pressure by the mid-portion of the tongue against the palate, followed by a slight lowering of the tongue, the mouth slightly opened. Both sounds were 150 ms in duration with a peak latency of 16 ms; the peak frequency was 1981 Hz for the finger sound and 5857 Hz for the suction sound. Acoustically-matched control stimuli faithfully replicating properties of both natural sounds on duration, peak frequency, envelope, onset and peaks latencies were also created. In addition to these four sounds, two stimuli with in-between acoustic properties (peak frequency 3876 Hz, peak latency 19 ms) and a dissimilar envelope were created; one was used as the standard stimulus while the other was used as an additional deviant, similarly to the original protocol (Hauk et al. 2006; Näätänen et al. 2004). Sounds were digitally recorded (sampling rate 44.1 kHz) and edited with Cool Edit 96 software (Syntrillium Software Corporation, Phoenix, AZ, USA). Before the experiment, biological and control sounds were evaluated by 10 adults using a 5-point likert scale (1 “not at all”; to 5 “very much”) with regards to naturalness, feasibility to produce the sound with the body, and frequency of self-production. Paired sample t-tests showed that the biological sounds had significantly higher scores than their controls for all three dimensions (all p < 0.001).

Oddball Paradigm

The procedure used in the present protocol was inspired by Hauk et al. (2006). Briefly, each deviant stimulus is interspersed randomly and always preceded by a standard stimulus with the two following rules: (1) the same deviant stimulus has to be separated at least by two presentations of the standard stimulus; and (2) all deviant stimuli must be present at least one time in every series of ten stimuli. Deviant sounds were each presented 252 times and the standard was presented 1260 times (90%), with a stimulus onset asynchrony of 500 ms using Presentation® software. Stimuli were delivered through speakers (Logitech X310 model) positioned 25 cm from the participant’s head at auricular height and 56 dB sound pressure level. Participants were instructed to ignore the auditory stimuli and to watch a silent movie.

EEG Recording

A simplified montage was used to reduce electrode installation time and to minimize possible sensory discomfort in hypersensitive ASD individuals. EEG was acquired from four Ag/AgCl electrodes (Grass technologies, USA) placed on FCz (International 10–20 system) with the ground electrode located on the forehead and the average reference place bilaterally on the mastoids. FCz was chosen as the recording site because it was at midpoint from the putative optimal position for typical children (Fz) and the more posterior activity often reported in ASD children with oddball paradigms (Cz) (Garrido et al. 2009; Lepistö et al. 2006).

Analysis

Analyzer software (Brain Products, Germany, Version 4.1) was used for offline analysis. EEG data was re-sampled (512 Hz) and filtered (1–50 Hz). Epochs of 100 ms pre-stimulus and 500 ms post-stimulus periods were segmented for the standard and deviant stimulus trials. Artefact rejection was performed semi-automatically (maximum and minimum amplitude of ±100 µV) and a minimum of 80% of segments were kept for each condition. MMN/P3 were obtained by subtracting the standard from each deviant ERP, resulting in four waveforms. Whole-segment analyses were conducted to assess amplitude difference using two by two repeated measure ANOVAs (group × sound category) performed on each data point (256). This approach, used without a priori-defined temporal window, is more conservative than the usual t test method and allows delineating the relative contribution of individual factors (group, sound category, and interaction effects) to the observed difference. To control for multiple comparisons and reduce the probability of type II errors, a minimum of 20 data-points with p-values <0.05 (one-tailed) was required to be considered statistical significance (Guthrie and Buchwald 1991). To ensure that potential findings were not due to alterations in primary auditory processing, ERP responses to the standard stimulus were compared with the same statistical threshold. Latency analyses were conducted on peaks corresponding to typically defined MMN and P3 responses for each deviant. Negative (≈175 ms) and positive (≈250 ms) peaks were automatically identified for each subject using a ±50 ms time window around maximal deflection points seen from the group average. Resulting data was subject to repeated measure ANOVAs, with group and sound category as factors.

Results

Between-group comparisons performed on ERP related to the standard stimulus show no significant difference in amplitude (Fig. 1a). Amplitude analysis performed on MMN responses revealed a sustained segment reaching statistical significance extending between 158 and 300 ms (83 data-points) [F(1,20) > 2.974; p ≤ 0.05] (Fig. 1b). This segment comprised three distinct but overlapping statistical effects; the first part comprised a main effect of sound category (158–225 ms), where the amplitude of response was larger for control sounds than for biological sounds. The second portion included an interaction effect (203–258 ms). Post-hoc t test showed that typically developing children’s response for biological sounds was larger than for control stimuli, and larger than ASD participants’ response to biological sounds (all p < 0.05), while no difference was seen for control stimuli (Fig. 1c, d). Overlapping with the interaction, the last segment consisted of a main effect of group, where healthy children displayed larger overall positivity in comparison with ASD children (250–300 ms). Regarding latency, repeated measure ANOVA conducted on the MMN showed a main effect of sound category [F(1,20) = 7.479; p = 0.013, η2p = 0.272], without group or interaction effects, indicating that biological stimuli induced an earlier peak than control sounds (148 vs. 162 ms). For the P3 component, analysis showed no main effect, but the presence of a group × sound category interaction [F(1,20) = 8.461; p = 0.009, η2p = 0.297]. Post-hoc t tests indicated that typically developing children had an earlier peak for biological sounds than for control stimuli (236 vs. 255 ms, p = 0.039), while children with ASD showed the opposite pattern (260 vs. 238 ms, p = 0.102) (Fig. 1e).

Fig. 1

ERP for ASD (red) and typically developing children (blue). a Both groups display similar P1 and N2 responses to the standard stimuli, suggesting intact sensory processing in ASD. b Resulting ERPs for each deviant category (minus standard). Periods of significant differences are highlighted in blue, orange and green, corresponding respectively to group, sound category, and interaction effects. c, d Post-hoc t tests used to decompose the interaction show a marked reduction of amplitude in the ASD group that is specific for biological sounds (highlighted). e Interaction effect for P3 peak-latency; while neurotypical children display an earlier response to biological stimuli than to control sounds, the opposite pattern in seen in ASD. (Color figure online)

Discussion

The main goal of the present study was to investigate if children with ASD showed abnormalities in processing biological sounds not related to speech. Our results show areas of commonalities between ASD and typically developing children with regards to the early stages of sound processing, but also evidence of alterations in ASD, some of which specific to biological stimuli.

Low-level auditory response, as indexed from ERP to the standard stimulus, appears normal in ASD, as both amplitude and latency of P1 and N2 components do not differ from healthy children. These results suggest preserved sensory encoding in ASD, which is in line with previous findings (Donkers et al. 2013; Whitehouse and Bishop 2008), and show that baseline responses against which deviant stimuli were contrasted did not play a significant role in our results. With regards to deviant stimuli, the early response of ASD individuals was highly similar to the one of typically developing children: both groups showed similar amplitude until ≈203 ms, i.e., manifested discriminant responses to biological and non-biological sounds, and had similar peak latency and polarity, consistent with a MMN-like response for children of this age (Lohvansuu et al. 2013; Ponton et al. 2000). This pattern is highly reminiscent of what has been reported with speech, where detection of social stimuli and their basic auditory features appears to be mostly preserved in ASD and visible at similar latencies (150–230 ms; Ceponiene et al. 2003; Whitehouse and Bishop 2008). Whether automatic detection at this early, pre-attentive sensory memory stage is related to conscious recognition during later phases of auditory processing is for the moment unresolved. It is interesting to note that adults, under similar experimental conditions, show comparatively earlier discriminant responses to sound category, visible as early as 100 ms (Hauk et al. 2006), where biological sounds induced larger potentials than control stimuli. The reason for these differences is elusive, but they could reflect maturational changes within the neural network involved in the processing of biological stimuli, where tuning might still occur during childhood, eventually leading to stronger and faster responses for these stimuli.

While early sensory and cognitive mechanisms seem mostly preserved in ASD, late elements of response, however, depart significantly from those of healthy children, as neurotypical individuals showed larger positivity (208–258 ms), increased P3a amplitude, and earlier peak latency specifically for biological stimuli in comparison to children with ASD. The P3a is though to index involuntary attentional orientation following novelty detection, a crucial step to quickly and adequately respond to environmental demands (Friedman et al. 2001). Here again, this response pattern is evocative of what is seen with speech stimuli (Ceponiene et al. 2003); despite normal detection of acoustical and categorical changes reflected by MMN modulation, biological stimuli fail to attract attention of ASD children. This faulty attentional capture, initially restricted to biological sounds, extends later on to all stimuli indiscriminately. It is interesting to note that these findings echo behavioural observations of ASD children. Indeed, ASD children display reduced orientation to naturally occurring sounds, social and non-social, but this reduction is especially pronounced for biological and social stimuli such as hand-clapping and name called (Dawson et al. 1998).

Literature pertaining to the auditory ERP in ASD is notoriously heterogeneous, especially regarding the much-studied MMN and P3a components. These components have been reported as reduced, normal, or increased in ASD (for a review, see Orekhova and Stroganova 2014). Explanations for this variability include fluctuation of subject’s attentiveness to the auditory stream and limited attentional resources in individuals with ASD. These hypotheses may explain parts of our results, namely the between-groups differences in amplitude. However, given that all deviants where embedded within a continuous stream, general attentiveness is an unlikely explanation for the particular responses displayed by ASD children specifically for biological stimuli, as revealed by the interaction effects observed. This pattern of alteration, specific for one category of stimulus, departs markedly from what is seen is other psychiatric disorders such as schizophrenia, where MMN and P3 abnormalities are present regardless of the nature of the stimuli (Hay et al. 2015; Javitt and Sweet 2015).

In sum, we show that brain responses to novel sounds is altered in a sample of children with ASD. Considering the well-known heterogeneity of the disorder, it remains to be seen whether this pattern is common amongst individuals with ASD from different age groups, symptomatology profile, and symptoms severity. This is especially important given that ADI-R and ADOS scores could not be obtained in our sample. Similarly, additional studies are required to assess the cross-cultural reproducibility of our findings, as some evidence suggests that sensory profile of ASD might be influence by cultural factors (Caron et al. 2012; Chow 2005). The paradigm used in the present study can easily be implemented in non-verbal and very young populations, giving the opportunity to study detection and orientation to social stimuli in different age groups and functioning levels. Doing so would be particularly pertinent considering the apparent dynamic nature of the social impairments in ASD. For example, aberrant social orientation to faces has been observed in newborns at risk for ASD (Di Giorgio et al. 2016), while being normal in 2-month-old children with ASD, declining until 2 year-old (Jones and Klin 2013), emerging back during childhood (Fischer et al. 2014), and then maintain in adulthood (Shah et al. 2013). Clarifying the precise developmental course of social processing in ASD is crucial to reconcile the seemingly divergent findings in the field and define periods when neurobehavioral interventions should be implemented to achieve maximal efficacy.

Notes

Acknowledgments

This work was supported by grants the Fonds de la Recherche du Québec- Santé (FRQS). ML was supported by a Canada Graduate Scholarship Doctoral Award from CIHR. JL is supported by a Junior 1 Salary Award from the FRQS.

Author Contributions

ML conceived of the study, acquired the data, performed the statistical analysis, and drafted the manuscript. LPB contributed to data processing, statistical analysis and helped to draft of the manuscript. DC participated in the design of the study and recruited participants. DS, HT and JL conceived of the study, supervised data processing, statistical analysis, and drafted the manuscript.

Funding

This study was funded by the Fonds de la Recherche du Québec-Santé (#34604).

Compliance with Ethical Standards

Conflict of interest

The authors have no conflict of interest to declare.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study or their legal tutor in the case of children.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of PsychologyUniversité de MontréalMontréalCanada
  2. 2.Department of PediatricsUniversité de SherbrookeSherbrookeCanada
  3. 3.CHU Sainte-Justine Research Center and Department of PsychologyUniversité du Québec à MontréalMontréalCanada
  4. 4.Department of PediatricsUniversité de MontréalMontréalCanada
  5. 5.CHU Sainte-Justine Research Center and Department of PsychologyUniversité de MontréalMontréalCanada
  6. 6.Department of PediatricsSherbrooke University, CHU Sherbrooke Research CenterSherbrookeCanada
  7. 7.Université du Québec à Trois-RivièresTrois-RivièresCanada
  8. 8.CHU Sainte-Justine Research CenterMontréalCanada

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