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Supplemental material, sj-docx-1-anp-10.1177_00048674221116027 for Vitamin D status in pregnancy and childhood associates with intelligence quotient at age 7 years: An Odense child cohort study by Emily Cantio, Niels Bilenberg, Signe Monrad Nørgaard, Iben Have Beck, Sören Möller, Cathriona Cantio, Tina Kold Jensen, Nicoline Bebe Mortensen, Asta Rasmussen and Henrik Boye Thybo Christesen in Australian & New Zealand Journal of Psychiatry

Children with autism spectrum disorders (ASD) often show changes in (automatic) auditory processing. Electrophysiology provides a method to study auditory processing, by investigating event-related potentials such as mismatch negativity (MMN) and P3a-amplitude. However, findings on MMN in autism are highly inconsistent, partly due to small sample sizes in the studies and differences in MMN paradigms. Therefore, in the current study, MMN and P3a amplitude were assessed in a relatively large sample of children with ASD, using a more extensive MMN paradigm and compared with that of typically developing children (TDC). Thirty-five children (aged 8-12 years) with ASD and 38 age and gender matched TDC were assessed with a MMN paradigm with three types of deviants, i.e., frequency, duration and a combination of these two. MMN elicited by duration and frequency-duration deviants was significantly reduced in the ASD group. P3a-amplitude elicited by duration deviants was significantly increased in the ASD group. Reduced MMN in children with ASD suggests that children with ASD may be less responsive to environmentally deviant stimuli at an early (sensory) level. P3a-amplitude was increased in ASD, implying a hyper-responsivity at the attentional level. In addition, as similar MMN deficits are found in schizophrenia, these MMN results may explain some of the frequently reported increased risk of children with ASD to develop schizophrenia later in life. Autism Res 2017, 10: 1857-1865. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.LAY SUMMARY: Automatic detection of deviant sounds in the environment, such as upcoming traffic, is often affected in children with autism spectrum disorders (ASD). Mismatch negativity (MMN) is a way to quantify automatic deviancy detection, using electroencephalography. In this study, auditory MMN was assessed in 35 children with ASD and 38 matched control children, revealing significantly reduced MMN in the ASD group. This may indicate that children with ASD are less able to automatically detect environmentally deviant stimuli.

Autism spectrum disorders (ASD) and schizophrenia are separate disorders, but there is evidence of conversion or comorbid overlap. The objective of this paper was to explore whether deficits in sensory gating, as seen in some schizophrenia patients, can also be found in a group of ASD children compared to neurotypically developed children. An additional aim was to investigate the possibility of subdividing our ASD sample based on these gating deficits. In a case-control design, we assessed gating of the P50 and N100 amplitude in 31 ASD children and 39 healthy matched controls (8-12 years) and screened for differences between groups and within the ASD group. We did not find disturbances in auditory P50 and N100 filtering in the group of ASD children as a whole, nor did we find abnormal P50 and N100 amplitudes. However, the P50 amplitude to the conditioning stimulus was significantly reduced in the Asperger subgroup compared to healthy controls. In contrast to what is usually reported for patients with schizophrenia, we found no evidence for sensory gating deficits in our group of ASD children taken as a whole. However, reduced P50 amplitude to conditioning stimuli was found in the Asperger group, which is similar to what has been described in some studies in schizophrenia patients. There was a positive correlation between the P50 amplitude of the conditioning stimuli and anxiety score in the pervasive developmental disorder not otherwise specified group, which indicates a relation between anxiety and sensory registration in this group.

The relation between autism spectrum disorders (ASD) and schizophrenia is a subject of intense debate and research due to evidence of common neurobiological pathways in the two disorders. The objective of this study was to explore whether deficits in prepulse inhibition (PPI) of the startle reflex, as usually seen in schizophrenic patients, can be replicated in a group of children with ASD in comparison with a group of matched neuro-typically developed (NTD) controls. An additional aim was to explore possible psychophysiological subgroups within our ASD sample. In a case-control design, 35 ASD patients and 40 matched NTD controls were tested in a psychophysiological test battery. The PPI of the acoustic startle reflex was analyzed in 18 ASD subjects and 34 NTD controls. Habituation and sensitization were analyzed in 23 ASD subjects and 39 NTD controls. In trials with less intense prestimuli (76 dB), patients with ASD did not display the drop in percentage PPI normally found in healthy controls. In addition, ASD patients showed significantly increased sensitization compared with NTD controls. Combined, our results may reflect the hypersensitivity to sensory information in children with ASD. The relation to PPI deficits observed in schizophrenia is not apparent. Future research should study the developmental course of PPI deficits in ASD patients in a longitudinal design.

Background:Individuals with autism spectrum disorder (ASD) tend to have atypical modulation of speech, often described as awkward, monotone, or sing-songy [1-3]. The patterns may be one of the most robust and fast signals of social communication deficits in ASD [4, 5]. However, it has proven difficult to determine a consistent set of acoustic features that can account for these perceived differences. Using Recurrence Quantification analysis of acoustic features, Fusaroli et al. [6] demonstrated a high efficacy of identifying voice patterns characteristic of adult Danish speakers with Asperger’s syndrome. Objectives:We systematically quantify and explore speech patterns in Danish children (8-12 years) with and without autism. We employ traditional and non-linear techniques measuring the structure (regularity and complexity) of speech behavior (i.e. fundamental frequency, use of pauses, speech rate). Our aims are (1) to achieve a more fine-grained understanding of the speech patterns in children with ASD, and (2) to employ the results in a supervised machine-learning process to determine whether acoustic features can be used to predict diagnostic status and severity of the symptoms.Methods:Our analysis was based on previously-acquired repeated narratives (TOMAL-2 [7]). We tested 25 Danish children diagnosed with ASD and matched controls. Participants had been diagnosed using ADOS and ADI-R and their symptoms assess with SRS and SCQ. Transcripts were time-coded, and pitch (F0), speech-pause sequences and speech rate were automatically extracted. Per each prosodic feature we calculated traditional statistical measures. We then extracted non-linear measure of recurrence: treating voice as a dynamical system, we reconstructed its phase space and measured the number, duration and structure of repeated trajectories in that space[8]. The results were employed to train (1) a linear discriminant function algorithm to classify the descriptions as belonging either to the ASD or the control group, and (2) a multiple linear regression to predict scores in Social Responsiveness Scale (SRS) and Social Communication Questionnaire (SCQ). Both models were developed and tested using 1000 iterations of 10-fold cross-validation (to test the generalizability of the accuracy) and variational Bayesian mixed-effects inferences (to compensate for biases in sample sizes). Results:While traditional measured did not allow for accurate classification, recurrence measures allowed to define voices as autistic or not with balanced accuracy > 77% (pConclusions:Non-linear time series analyses techniques suggest that there are quantifiable acoustic features in speech production of children with ASD that both distinguish them from typically developing speakers and reflect the severity of the symptoms. [1] R.B. Grossman, L. Edelson, H. Tager-Flusberg, Production of emotional facial and vocal expressions during story retelling by children and adolescents with high-functioning autism, Journal of Speech Language and Hearing Research, 56 (2013) 1035-1044.[2] J.J. Diehl, L. Bennetto, D. Watson, C. Gunlogson, J. McDonough, Resolving ambiguity: A psycholinguistic approach to understanding prosody processing in high-functioning autism, Brain and Language, 106 (2008) 144–152.[3] L.D. Shriberg, R. Paul, J.L. McSweeny, A. Klin, D.J. Cohen, F.R. Volkmar)Speech and prosody characteristics of adolescents and adults with high-functioning autism and Asperger syndrome, Journal of Speech, Language, and Hearing Research, 44 (2001) 1097–1115.[4] R. Paul, L.D. Shriberg, J. McSweeny, D. Cicchetti, A. Klin, F.R. Volkmar, Relations between prosodic performance and communication and socialization ratings in high functioning speakers with autism spectrum disorders, Journal of autism and developmental disorders, 35 (2005) 861–869.[5] R.B. Grossman, H. Tager-Flusberg, Quality matters! Differences between expressive and receptive non-verbal communication skills in children with ASD, Res Autism Spect Dis, 6 (2012) 1150-1155.[6] R. Fusaroli, D. Bang, E. Weed, Non-Linear Analyses of Speech and Prosody in Asperger's Syndrome, in: IMFAR 2013, San Sebastian, 2013.[7] C.R. Reynolds, J. Voress, Test of Memory and Learning (TOMAL-2), TX: PRO-ED, (2007).[8] N. Marwan, M. Carmen Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex systems, Physics Reports, 438 (2007) 237-329.