A Multidimensional Investigation of Sensory Processing in Autism: Parent- and Self-Report Questionnaires, Psychophysical Thresholds, and Event-Related Potentials in the Auditory and Somatosensory Modalities

BackgroundReconciling results obtained using different types of sensory measures is a challenge for autism sensory research. The present study used questionnaire, psychophysical, and neurophysiological measures to characterize autistic sensory processing in different measurement modalities.MethodsParticipants were 46 autistic and 21 typically-developing adolescents. Participants and their caregivers completed questionnaires regarding sensory experiences and behaviours. Auditory and somatosensory ERPs were recorded as part of a multisensory ERP task. Auditory, tactile static detection, and tactile spatial resolution psychophysical thresholds were measured.ResultsSensory questionnaires strongly differentiated between autistic and typically-developing individuals, while little evidence of group differences was observed in psychophysical thresholds. Crucially, the different types of measures (neurophysiological, psychophysical, questionnaire) appeared to be largely independent of one another. However, we unexpectedly found autistic participants with larger auditory Tb ERP amplitudes had reduced hearing acuity, even though all participants had hearing acuity in the non-clinical range.LimitationsThe autistic and typically-developing groups were not well-matched, although this limitation does not affect our main analyses regarding convergence of measures within ASD. The autistic sample in the present study is not representative of the whole autistic constellation, limiting generalizability. Auditory ERPs and auditory thresholds were measured with non-equivalent stimuli.ConclusionsOverall, based on these results, measures in different sensory modalities appear to capture distinct aspects of sensory processing in autism, with relatively limited convergence between questionnaires and laboratory-based tasks. Generally, this might reflect the reality that laboratory tasks are often carried out in controlled environments without background stimuli to compete for attention, a context which may not closely resemble the busier and more complex environments in which autistic people’s atypical sensory experiences commonly occur. For this reason, sensory questionnaires may be more practically useful assessments of autistic people’s real-world sensory challenges. Further research is needed to replicate and investigate the drivers of the unexpected association we observed between auditory Tb ERP amplitudes and hearing acuity, which could represent an important confound for ERP researchers to consider in their studies.

Hierarchical Learning of Statistical Regularities over Multiple Timescales of Sound Sequence Processing: A Dynamic Causal Modeling Study

Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change over time. However, task-independent exposure to sound has revealed that strong first impressions exert a lasting impact on how the relevance of sound is contextualized. Dynamic causal modeling was applied to auditory ERPs collected during presentation of alternating pattern sequences. A local regularity (a rare p = .125 vs. common p = .875 sound) alternated to create a longer timescale regularity (sound probabilities alternated regularly creating a predictable block length), and the longer timescale regularity changed halfway through the sequence (the regular block length became shorter or longer). Predictions should be revised for local patterns when blocks alternated and for longer patterning when the block length changed. Dynamic causal modeling revealed an overall higher precision for the error signal to the rare sound in the first block type, consistent with the first impression. The connectivity changes in response to errors within the underlying neural network were also different for the two blocks with significantly more revision of predictions in the arrangement that violated the first impression. Furthermore, the effects of block length change suggested errors within the first block type exerted more influence on the updating of longer timescale predictions. These observations support the hypothesis that automatic sequential learning creates a high-precision context (first impression) that impacts learning rates and updates to those learning rates when predictions arising from that context are violated. The results further evidence automatic pattern learning over multiple timescales simultaneously, even during task-independent passive exposure to sound.

Time-oriented attention improves accuracy in a paced finger tapping task

Finger tapping is a task widely used in a variety of experimental paradigms, in particular to understand sensorimotor synchronization and time processing in the range of hundreds of milliseconds (millisecond timing). Normally, subjects don’t receive any instruction about what to attend to and the results are seldom interpreted taking into account the possible effects of attention. In this work we show that attention can be oriented to the purely temporal aspects of a paced finger tapping task and that it affects performance. Specifically, time-oriented attention improves the accuracy in paced finger tapping and it also increases the resynchronization efficiency after a period perturbation. We use two markers of the attention level: auditory ERPs and subjective report of the mental workload. In addition, we propose a novel algorithm to separate the auditory, stimulus-related components from the somatosensory, response-related ones, which are naturally overlapped in the recorded EEG.

The Cross-Modal Suppressive Role of Visual Context on Speech Intelligibility: An ERP Study

The efficacy of audiovisual (AV) integration is reflected in the degree of cross-modal suppression of the auditory event-related potentials (ERPs, P1-N1-P2), while stronger semantic encoding is reflected in enhanced late ERP negativities (e.g., N450). We hypothesized that increasing visual stimulus reliability should lead to more robust AV-integration and enhanced semantic prediction, reflected in suppression of auditory ERPs and enhanced N450, respectively. EEG was acquired while individuals watched and listened to clear and blurred videos of a speaker uttering intact or highly-intelligible degraded (vocoded) words and made binary judgments about word meaning (animate or inanimate). We found that intact speech evoked larger negativity between 280–527-ms than vocoded speech, suggestive of more robust semantic prediction for the intact signal. For visual reliability, we found that greater cross-modal ERP suppression occurred for clear than blurred videos prior to sound onset and for the P2 ERP. Additionally, the later semantic-related negativity tended to be larger for clear than blurred videos. These results suggest that the cross-modal effect is largely confined to suppression of early auditory networks with weak effect on networks associated with semantic prediction. However, the semantic-related visual effect on the late negativity may have been tempered by the vocoded signal’s high-reliability.

Applying SCA for high-accuracy cortical auditory ERPs in children

Overlapping neurophysiological signals are the main obstacle preventing from using cortical event-related potentials (ERPs) in clinical settings. Children ERPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently introduced, to isolate with high accuracy the neural components of auditory ERP responses (AEPs) in 8-year-old children. Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard ERP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected.Superior accuracy in isolating neural components at the individual level even in children was demonstrated for SCA-HSAC over other SCA approaches. Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults.HighlightsSpike-density component analysis (SCA) was validated on children ERPsSCA extracted overlapping neural components from auditory ERPs (AEPs)Child AEPs were modelled at the individual level