Relationship Between Directly Observed Sensory Reactivity Differences and Classroom Behaviors of Autistic Children.

IMPORTANCE: Differences in sensory reactivity are a core feature of autism; however, more remains to be learned about their role in classroom learning. OBJECTIVE: To use direct observational measures to investigate whether there is a link between sensory reactivity differences and classroom behaviors of autistic children. DESIGN: Correlational study. SETTING: Two special educational needs schools. PARTICIPANTS: Children with a clinical diagnosis of autism, ages 5 to 18 yr (N = 53). OUTCOMES AND MEASURES: Sensory reactivity differences were assessed with the Sensory Assessment for Neurodevelopmental Differences. Classroom behaviors were measured using the Behavior Assessment for Children-Second Edition Student Observation System. RESULTS: Total sensory reactivity differences were correlated positively with behaviors that impede learning (r = .31, p < .05) and negatively with behaviors that facilitate learning (r = -.38, p < .05). Hyporeactivity differences were correlated positively with behaviors that impede learning (r = .28, p < .05) and negatively with behaviors that facilitate learning (r = -.31, p < .05). Hyperreactivity and sensory-seeking differences were not significantly correlated with behavior. CONCLUSIONS AND RELEVANCE: Results suggest a link between sensory reactivity differences and classroom behaviors, highlighting a need for further research using observational measures in special education settings. Plain-Language Summary: Differences in hyporeactivity for children with autism may play a bigger role in classroom behavior and learning than previous literature has suggested. This has implications in occupational therapy practice for how to tailor support for children with hyporeactivity differences.

Examining the latent structure and correlates of sensory reactivity in autism: a multi-site integrative data analysis by the autism sensory research consortium.

BACKGROUND: Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these "supra-modal" traits in the autistic population. METHODS: Leveraging a combined sample of 3868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a "general response pattern" factor for each supra-modal construct and determine the added value of "modality-specific response pattern" scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO, and SEEK (sub)constructs. RESULTS: All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses supported the validity of a supra-modal HYPER construct (ωH = .800) but not a supra-modal HYPO construct (ωH = .653), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .800; 4/7 modalities). Modality-specific subscales demonstrated significant added value for all response patterns. Meta-analytic correlations varied by construct, although sensory features tended to correlate most with other domains of core autism features and co-occurring psychiatric symptoms (with general HYPER and speech HYPO demonstrating the largest numbers of practically significant correlations). LIMITATIONS: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to caregiver report of observable behaviors and excluded multisensory items that reflect many "real-world" sensory experiences. CONCLUSION: Of the three sensory response patterns, only HYPER demonstrated sufficient evidence for valid interpretation at the supra-modal level, whereas supra-modal HYPO/SEEK constructs demonstrated substantial psychometric limitations. For clinicians and researchers seeking to characterize sensory reactivity in autism, modality-specific response pattern scores may represent viable alternatives that overcome many of these limitations.

A working taxonomy for describing the sensory differences of autism.

BACKGROUND: Individuals on the autism spectrum have been long described to process sensory information differently than neurotypical individuals. While much effort has been leveraged towards characterizing and investigating the neurobiology underlying the sensory differences of autism, there has been a notable lack of consistency in the terms being used to describe the nature of those differences. MAIN BODY: We argue that inconsistent and interchangeable terminology-use when describing the sensory differences of autism has become problematic beyond mere pedantry and inconvenience. We begin by highlighting popular terms that are currently being used to describe the sensory differences of autism (e.g. "sensitivity", "reactivity" and "responsivity") and discuss why poor nomenclature may hamper efforts towards understanding the aetiology of sensory differences in autism. We then provide a solution to poor terminology-use by proposing a hierarchical taxonomy for describing and referring to various sensory features. CONCLUSION: Inconsistent terminology-use when describing the sensory features of autism has stifled discussion and scientific understanding of the sensory differences of autism. The hierarchical taxonomy proposed was developed to help resolve lack of clarity when discussing the sensory differences of autism and to place future research targets at appropriate levels of analysis.

Examining the Latent Structure and Correlates of Sensory Reactivity in Autism: A Multi-site Integrative Data Analysis by the Autism Sensory Research Consortium.

Background Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these "supra-modal" traits in the autistic population. Methods Leveraging a combined sample of 3,868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a "general response pattern" factor for each supra-modal construct and determine the added value of "modality-specific response pattern" scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO and SEEK (sub)constructs. Results All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses unambiguously supported the validity of a supra-modal HYPER construct (ω H = .800), whereas a coherent supra-modal HYPO construct was not supported (ω H = .611), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ω H = .799; 4/7 modalities). Within each sensory construct, modality-specific subscales demonstrated substantial added value beyond the supra-modal score. Meta-analytic correlations varied by construct, although sensory features tended to correlate most strongly with other domains of core autism features and co-occurring psychiatric symptoms. Certain subconstructs within the HYPO and SEEK domains were also associated with lower adaptive behavior scores. Limitations: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to parent-report of observable behaviors and excluded multisensory items that reflect many "real-world" sensory experiences. Conclusion Psychometric issues may limit the degree to which some measures of supra-modal HYPO/SEEK can be interpreted. Depending on the research question at hand, modality-specific response pattern scores may represent a valid alternative method of characterizing sensory reactivity in autism.

The Predictive Relationship Between Sensory Reactivity and Depressive Symptoms in Young Autistic Children with Few to No Words.

Depression and sensory reactivity are both common in autism. However, there is little understanding of the predictive relationship between these factors, or the nature of this relationship in autistic children who speak few to no words. This study set out to explore the longitudinal relationship between sensory reactivity and depressive symptoms in 33 young autistic children who speak few to no words. We found positive correlations between depressive symptoms and hyper-reactivity and sensory seeking at both timepoints, and across timepoints. We further found a bidirectional predictive relationship between depressive symptoms and sensory seeking. These results implicate sensory seeking in the development of depressive symptoms in young autistic children who use few to no words. Our findings have important implications for preventative mental health interventions, especially for those with a developmental language delay.

High-dose Vitamin B6 supplementation reduces anxiety and strengthens visual surround suppression.

OBJECTIVE: Vitamins B6 and B12 are involved in metabolic processes that decrease neural excitation and increase inhibition. This double-blind study investigated the effects of supplementation for 1 month with a high-dose of B6 or B12, compared to placebo, on a range of behavioural outcome measures connected to the balance between neural inhibition and excitation. METHODS: 478 young adults were recruited over five linked phases. Self-reported anxiety (N = 265) and depression (N = 146) were assessed at baseline and after supplementation. Several sensory measures acted as assays of inhibitory function and were assessed post-supplementation only; these were surround suppression of visual contrast detection (N = 307), binocular rivalry reversal rate (N = 172), and a battery of tactile sensitivity tests (N = 180). RESULTS: Vitamin B6 supplementation reduced self-reported anxiety and induced a trend towards reduced depression, as well as increased surround suppression of visual contrast detection, but did not reliably influence the other outcome measures. Vitamin B12 supplementation produced trends towards changes in anxiety and visual processing. CONCLUSIONS: Our results suggest that high-dose Vitamin B6 supplementation increases inhibitory GABAergic neural influences, which is consistent with its known role in the synthesis of GABA.

Visual Evoked Potential Abnormalities in Phelan-McDermid Syndrome.

OBJECTIVE: The current study used visual evoked potentials (VEPs) to examine excitatory and inhibitory postsynaptic activity in children with Phelan-McDermid syndrome (PMS) and the association with genetic factors. PMS is caused by haploinsufficiency of SHANK3 on chromosome 22 and represents a common single-gene cause of autism spectrum disorder (ASD) and intellectual disability. METHOD: Transient VEPs were obtained from 175 children, including 31 with PMS, 79 with idiopathic ASD, 45 typically developing controls, and 20 unaffected siblings of children with PMS. Stimuli included standard and short-duration contrast-reversing checkerboard conditions, and the reliability between these 2 conditions was assessed. Test-retest reliability and correlations with deletion size were explored in the group with PMS. RESULTS: Children with PMS and, to a lesser extent, those with idiopathic ASD displayed significantly smaller amplitudes and decreased beta and gamma band activity relative to TD controls and PMS siblings. Across groups, high intraclass correlation coefficients were obtained between standard and short-duration conditions. In children with PMS, test-retest reliability was strong. Deletion size was significantly correlated with P60-N75 amplitude for both conditions. CONCLUSION: Children with PMS displayed distinct transient VEP waveform abnormalities in both time and frequency domains that might reflect underlying glutamatergic deficits that were associated with deletion size. A similar response pattern was observed in a subset of children with idiopathic ASD. VEPs offer a noninvasive measure of excitatory and inhibitory neurotransmission that holds promise for stratification and surrogate endpoints in ongoing clinical trials in PMS and ASD.

The Perceived Causal Relations Between Sensory Reactivity Differences and Anxiety Symptoms in Autistic Adults.

Background: Rates of anxiety are inordinately high in autistic adults. Sensory reactivity differences, such as hyperreactivity (e.g., strong reactions to sound), hyporeactivity (e.g., no, or slower reactions to pain), and seeking (e.g., fascination with spinning objects), are a diagnostic criterion of autism and have been linked with anxiety. Understanding how individuals perceive these to be causally related can impact the assessment and treatment of anxiety. Therefore, we examined the perceived causal relations (PCR) between sensory reactivity differences and anxiety in autistic adults. Method: Two hundred forty-six autistic adults aged 18-76 years took part in an online study. They completed self-report assessments of sensory reactivity differences, and anxiety, followed by the PCR scale, indicating whether they perceived their sensory reactivity differences to be more of a cause or an effect of their anxiety symptoms. Results: We found sensory reactivity hyperreactivity, hyporeactivity, and seeking to be significantly correlated with anxiety. Further, we found total sensory hyperreactivity, and visual, auditory, and olfactory hyperreactivity, to be perceived as significantly more of a cause of anxiety than an effect, and total sensory seeking, and tactile and vestibular seeking, to be perceived as significantly more of an effect of anxiety than a cause. Conclusion: Future individualized approaches to treating anxiety in autistic individuals may benefit from differentiating between potential sensory causes of anxiety (e.g. hypersensitivities) vs. potential sensory effects of anxiety (e.g. sensory seeking behaviors).

Why is this an important issue?: Autistic people are more likely to experience anxiety compared with the general population. Sensory reactivity differences, such as hyperreactivity (e.g., strong reactivity to sounds), hyporeactivity (e.g., not noticing touch), or seeking (e.g., being fascinated by spinning objects), are common in autistic individuals, and may be a risk factor for anxiety. However, existing anxiety treatments are not always effective for autistic people. Understanding how autistic people feel their anxiety and sensory reactivity differences are causally linked could be important to help clinicians understand the challenges that should be prioritized in anxiety treatment for autistic people. What was the purpose of this study?: In this study, we aimed at examining sensory reactivity differences and anxiety symptoms in autistic adults, and asking them whether they perceive their anxiety symptoms to be more of a cause or an effect of their sensory reactivity differences. What did the researchers do?: In our study, we measured sensory reactivity differences and anxiety symptoms using online surveys. For the sensory reactivity differences and anxiety symptoms that each individual reported to be present, they were then asked how much they felt each anxiety symptom was a cause of their sensory reactivity differences, and how much they felt each sensory reactivity difference was a cause of their anxiety symptoms. This study included 246 autistic adults aged 18­76 years. What were the results of the study?: Our results showed that the autistic individuals felt that their sensory hyperreactivity, including hyperreactivity related to vision, hearing, and scent, is more of a cause than an effect of anxiety. However, they also felt that their sensory seeking, especially touch- and balance-related seeking, is more of an effect than a cause of anxiety. What do these findings add to what was already known?: Although sensory hyperreactivity has been shown earlier to be a cause of anxiety for autistic individuals, our study was the first to suggest that anxiety may influence sensory seeking, which is something that can be tested in future research studies. What are potential weaknesses in the study?: We did not ask participants about co-occurring conditions, such as if they have additional diagnoses related to intellectual disability or attention deficit hyperreactivity disorder, which means there may be important differences between individuals that we did not examine. Also, we did not measure key symptoms associated with wider anxiety conditions and autism-related anxiety symptoms, such as social fears unrelated to negative self-assessment, which would be important for clinical understanding. How will these findings help autistic adults now or in the future?: Understanding how people perceive their traits and symptoms to be causally related can impact how effective anxiety treatments are for individuals. So, our findings can importantly help inform clinical approaches to the treatment of anxiety for autistic individuals.

Sensory Reactivity Phenotype in Phelan-McDermid Syndrome Is Distinct from Idiopathic ASD.

Phelan-McDermid syndrome (PMS) is one of the most common genetic forms of autism spectrum disorder (ASD). While sensory reactivity symptoms are widely reported in idiopathic ASD (iASD), few studies have examined sensory symptoms in PMS. The current study delineates the sensory reactivity phenotype and examines genotype-phenotype interactions in a large sample of children with PMS. Sensory reactivity was measured in a group of 52 children with PMS, 132 children with iASD, and 54 typically developing (TD) children using the Sensory Assessment for Neurodevelopmental Disorders (SAND). The SAND is a clinician-administered observation and corresponding caregiver interview that captures sensory symptoms based on the DSM-5 criteria for ASD. Children with PMS demonstrated significantly greater hyporeactivity symptoms and fewer hyperreactivity and seeking symptoms compared to children with iASD and TD controls. There were no differences between those with Class I deletions or sequence variants and those with larger Class II deletions, suggesting that haploinsufficiency of SHANK3 is the main driver of the sensory phenotype seen in PMS. The syndrome-specific sensory phenotype identified in this study is distinct from other monogenic forms of ASD and offers insight into the potential role of SHANK3 deficiency in sensory reactivity. Understanding sensory reactivity abnormalities in PMS, in the context of known glutamatergic dysregulation, may inform future clinical trials in the syndrome.

The relationship between sensory reactivity differences and mental health symptoms in preschool-age autistic children.

There is growing evidence for an association between sensory reactivity and mental health in autism. This study set out to explore the relationship between sensory reactivity and mental health in preschool-aged autistic children. In total 54 preschool-aged children with an Autism Spectrum Condition took part. Sensory and mental health symptoms were obtained from the Sensory Processing Scale Inventory, Sensory Assessment of Neurodevelopmental Differences and the Behavioral Assessment System for Children-3. Correlational analyses showed a relationship between sensory reactivity and mental health symptoms in autistic preschool-aged children. Results also indicate divergence in sensory-mental health profiles between autistic preschool-aged children who are verbal and those who use few to no words. For the first time this study has revealed a relationship between sensory hyper-reactivity, as well as sensory seeking, and mental health symptoms outside of anxiety in autism. Of note, this relationship between sensory hyper-reactivity and internalizing symptoms appears to be driven by those with few to no words. This has implications for both research and clinical interventions, in particular for our understanding of the factors underlying mental health symptoms in different autistic phenotypes, as well as the possible role of functional communication in mitigating the development of mental health symptoms. LAY SUMMARY: The present study found that in autistic preschoolers, externalizing mental health symptoms, such as hyperactivity, are related to sensory seeking (seeking out or being fascinated with sensory stimuli), and internalizing mental health symptoms, such as depression, are related to sensory hyper-reactivity (sensitivity to sensory stimuli). Our results also show that whilst the relationships between externalizing symptoms and sensory seeking is seen across participants, the relationship between internalizing symptoms and sensory hyper-reactivity was only seen in autistic preschoolers who used few to no words. This has important implications for the assessment of sensory differences as part of more rigorous mental health assessments, especially in autistic people who use few to no words.

The relationship between sensory reactivity, intolerance of uncertainty and anxiety subtypes in preschool-age autistic children.

LAY ABSTRACT: This study found links between greater sensory hyperreactivity (e.g., over-sensitive to sensory input), intolerance of uncertainty and anxiety, including separation anxiety, in autistic pre-schoolers. Sensory hyperreactivity may predict both anxiety and intolerance of uncertainty, and anxiety and intolerance of uncertainty may both be mutually important, mediating factors. These findings have implications for early anxiety interventions. But there is a pressing need for objective assessments that can be used with preschool-age autistic children.

Sensory Reactivity Symptoms Are a Core Feature of ADNP Syndrome Irrespective of Autism Diagnosis.

Background: Activity dependent neuroprotective protein (ADNP) syndrome is one of the most common single-gene causes of autism spectrum disorder (ASD) and intellectual disability, however, the phenotypes remain poorly described. Here we examine the sensory reactivity phenotype in children and adolescents with ADNP syndrome. Methods: Twenty-two individuals with ADNP syndrome received comprehensive clinical evaluations including standardized observations, caregiver interviews, and questionnaires to assess sensory reactivity symptoms. Relationships between sensory symptoms and age, sex, ASD, IQ, and adaptive behavior were examined. Genotype-phenotype correlations with the recurrent p.Tyr719* variant were also explored. Results: Sensory reactivity symptoms were observed and reported in all participants. A syndrome-specific phenotype was identified, characterized by high levels of sensory seeking across tactile, auditory, and visual domains. Tactile hyporeactivity, characterized by pain insensitivity, was reported in the majority of participants. Sensory symptoms were identified across individuals regardless of age, sex, IQ, adaptive ability, genetic variant, and most importantly, ASD status. No significant differences were identified between participants with and without the recurrent p.Tyr719* variant on any sensory measure. Conclusions: Sensory reactivity symptoms are a common clinical feature of ADNP syndrome. Quantifying sensory reactivity using existing standardized measures will enhance understanding of sensory reactivity in individuals with ADNP syndrome and will aid in clinical care. The sensory domain may also represent a promising target for treatment in clinical trials.

Revised scored Sensory Perception Quotient reveals sensory hypersensitivity in women with autism.

BACKGROUND: Previous research using the Sensory Perception Quotient (SPQ) has reported greater sensory hypersensitivity in people with autism spectrum condition (ASC) compared to controls, consistent with other research. However, current scoring of the SPQ does not differentiate between hyper and hyposensitivity, making it uncertain whether individuals with ASC might also show differences in hyposensitivity. Furthermore, no research to date has focused on sensory differences in females, and whether differences in sensory sensitivity extend to the broader autism phenotype (BAP). The present study aimed to fill these gaps. METHODS: The present study developed and validated a Revised Scoring of the Sensory Perception Quotient (SPQ-RS) in order to investigate self-reported hypersensitivity and hyposensitivity in three groups of adults: a female ASC group (n = 152), mothers of children with ASC (BAP mothers group; n = 103), and a control mothers group (n = 74). All participants completed the SPQ as a self-report measure of sensory processing and the Autism-Spectrum Quotient (AQ) as a measure of the degree of autism traits. RESULTS: The female ASC group reported significantly more hypersensitivity, but not more hyposensitivity, compared to the control female and BAP mothers groups. The BAP mothers group did not differ from the control mothers group in either reported hypersensitivity (p = .365) or hyposensitivity (p = .075), suggesting atypical sensory sensitivity is not a BAP trait within females. SPQ-RS hypersensitivity scores positively correlated with autistic traits in the female ASC (r = .266) and BAP mothers groups (r = .350). CONCLUSIONS: The present findings revealed greater sensory hypersensitivity, but not hyposensitivity, in females with ASC compared to BAP and control female groups, and that a greater degree of autism traits relates to higher hypersensitivity in ASC females. The results offer support for the enhanced perceptual functioning model using large samples of females, who are an understudied population, and demonstrate the validity of the SPQ-RS as a valuable new research tool for exploring self-reported hypersensitivity and hyposensitivity.

The Relationship Between Sensory Reactivity Differences and Anxiety Subtypes in Autistic Children.

Autistic children are at greater risk of developing anxiety than their nonautistic peers. Sensory reactivity differences have been implicated as one of the risk factors. Specifically, sensory hyperreactivity has previously been linked to anxiety, including separation anxiety and specific phobia; however, minimal research has explored the influence of sensory hyporeactivity and seeking. Therefore, the present study examined the correlational relationship between sensory reactivity differences and anxiety subtypes in 41 autistic children aged between 3 and 14 years, using parent- and self-reported measures. We found positive correlations between sensory hyperreactivity and total anxiety, separation anxiety and physical injury fears. However, when controlling for autism traits, we found sensory hyperreactivity to be related to physical injury fears and specific phobia, and sensory hyporeactivity to be related to lower total and social anxiety. We found no significant relationships between sensory seeking and anxiety. These results indicate that sensory hyperreactivity and hyporeactivity might be implicated in specific anxiety symptomology. Our results also indicate minimal agreement between parent- and self-reported anxieties, which highlights the limitations of informant reports for anxiety and the pressing need for objective anxiety assessments for autistic children to be developed. Our findings have important implications for limiting the development of anxiety in autistic children and suggest that sensory reactivity differences should be considered when developing targeted interventions for certain anxiety disorders. Autism Res 2020, 13: 785-795. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. LAY SUMMARY: The present study found that when considering autism traits, greater sensory hyperreactivity, such as being oversensitive to sounds, was related to elevated phobia-related symptoms and sensory hyporeactivity, such as being under-responsive to touch, was related to lower total and social anxieties. Sensory seeking, such as a fascination with lights, was not related to anxiety. Our results have important implications for targeted anxiety interventions for autistic children. However, due to minimal agreement between the parent- and child-reported scores, developing more objective measures of anxiety would be beneficial.

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers.

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps - evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.

Sensory over-responsivity: parent report, direct assessment measures, and neural architecture.

Background: Sensory processing difficulties are common across neurodevelopmental disorders. Thus, reliable measures are needed to understand the biological underpinnings of these differences. This study aimed to define a scoring methodology specific to auditory (AOR) and tactile (TOR) over-responsivity. Second, in a pilot cohort using MRI Diffusion Tensor Imaging, we performed a proof of concept study of whether children with AOR showed measurable differences in their white matter integrity. Methods: This study included children with AOR and TOR from a mixed neurodevelopmental disorder cohort including autism and sensory processing dysfunction (n = 176) as well as neurotypical children (n = 128). We established cohorts based on sensory over-responsivity using parent report (Short Sensory Profile (SSP)) and direct assessment (Sensory Processing-Three Dimensions: Assessment (SP-3D:A)) measures. With a subset of the children (n = 39), group comparisons, based on AOR phenotype, were conducted comparing the white matter fractional anisotropy in 23 regions of interest. Results: Using direct assessment, 31% of the children with neurodevelopmental disorders had AOR and 27% had TOR. The inter-test agreement between SSP and SP-3D:A for AOR was 65% and TOR was 50%. Children with AOR had three white matter tracts showing decreased fractional anisotropy relative to children without AOR. Conclusions: This study identified cut-off scores for AOR and TOR using the SSP parent report and SP-3D:A observation. A combination of questionnaire and direct observation measures should be used in clinical and research settings. The SSP parent report and SP-3D:A direct observation ratings overlapped moderately for sensory related behaviors. Based on these preliminary structural neuroimaging results, we suggest a putative neural network may contribute to AOR.

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models.

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

Educators' Views on Using Humanoid Robots With Autistic Learners in Special Education Settings in England.

Researchers, industry, and practitioners are increasingly interested in the potential of social robots in education for learners on the autism spectrum. In this study, we conducted semi-structured interviews and focus groups with educators in England to gain their perspectives on the potential use of humanoid robots with autistic pupils, eliciting ideas, and specific examples of potential use. Understanding educator views is essential, because they are key decision-makers for the adoption of robots and would directly facilitate future use with pupils. Educators were provided with several example images (e.g., NAO, KASPAR, Milo), but did not directly interact with robots or receive information on current technical capabilities. The goal was for educators to respond to the general concept of humanoid robots as an educational tool, rather than to focus on the existing uses or behaviour of a particular robot. Thirty-one autism education staff participated, representing a range of special education settings and age groups as well as multiple professional roles (e.g., teachers, teaching assistants, speech, and language therapists). Thematic analysis of the interview transcripts identified four themes: Engagingness of robots, Predictability and consistency, Roles of robots in autism education, and Need for children to interact with people, not robots. Although almost all interviewees were receptive toward using humanoid robots in the classroom, they were not uncritically approving. Rather, they perceived future robot use as likely posing a series of complex cost-benefit trade-offs over time. For example, they felt that a highly motivating, predictable social robot might increase children's readiness to learn in the classroom, but it could also prevent children from engaging fully with other people or activities. Educator views also assumed that skills learned with a robot would generalise, and that robots' predictability is beneficial for autistic children-claims that need further supporting evidence. These interview results offer many points of guidance to the HRI research community about how humanoid robots could meet the specific needs of autistic learners, as well as identifying issues that will need to be resolved for robots to be both acceptable and successfully deployed in special education contexts.

Grandma knows best: Family structure and age of diagnosis of autism spectrum disorder.

This pilot study estimates the effects of family structure on age of diagnosis, with the goal of identifying factors that may accelerate or delay diagnosis. We conducted an online survey with 477 parents of children with autism. In addition, we carried out novel, follow-up surveys of 196 "friends and family," who were referred by parents. Family structure and frequency of interactions with family members have significant effects on age of diagnosis (p < 0.05). In all, 25% of parents report that other individuals indicated that their child might have a serious condition before they themselves suspected it. Moreover, around 50% of friends and family report that they suspected that the child had a serious condition before they were aware that either parent was concerned, suggesting that the clues were there to see, especially for experienced viewers. While half of those individuals shared their concerns with the parents, the other half either did not raise any concern (23%) or just "hinted" at their concern (27%). Among children with siblings, children with an older sibling are diagnosed approximately 10 months earlier (p < 0.01) than those without, and children with no siblings were diagnosed 6-8 months earlier than children with siblings (p < 0.01). Interestingly, frequent interactions with grandparents, especially grandmothers, significantly lowered the age of diagnosis by as much as 5 months (p < 0.05). While this pilot study requires replication, the results identify potential causes for accelerated or delayed diagnosis, which if better understood, could ultimately improve age of diagnosis and treatment, and hence outcomes.