Neural Correlates of Sensory Hyporesponsiveness in Toddlers at High Risk for Autism Spectrum Disorder

Increased alpha power in autistic adults: Relation to sensory behaviors and cortical volume

Alpha-band (~10 Hz) neural oscillations, crucial for gating sensory information, may offer insights into the atypical sensory experiences characteristic of autism spectrum disorder (ASD). We investigated alpha-band EEG activity in autistic adults (n = 29) compared with a nonautistic group (n = 23) under various stimulus-driven and resting-state conditions. The autistic group showed consistently higher alpha amplitude across all time points. In addition, there was proportionally more suppression of alpha at stimulus onset in the autistic group, and alpha amplitude in this stimulus-onset period correlated with sensory behaviors. Recent research suggests a link between subcortical structures' volume and cortical alpha magnitude. Prompted by this, we explored the association between alpha power and the volume of subcortical structures and total cortical volume in ASD. Our findings indicate a significant correlation with total cortical volume and a group by hippocampal volume interaction, pointing to the potential role of anatomical structural characteristics as potential modulators of cortical alpha oscillations in ASD. Overall, the results highlight altered alpha in autistic individuals as potentially contributing to the heightened sensory symptoms in autistic compared with nonautistic adults.

Development and Psychometric Properties of the Pain and Sensitivity Reactivity Scale in a Diverse Sample of Autistic People

BACKGROUND

Recent studies indicate the need to examine how the gut microbiota-brain axis is implicated in pain, sensory reactivity and gastro-intestinal symptoms in autism spectrum disorder (ASD), but no scale exists that assesses all these constructs simultaneously.

METHODS

We created a pool of 100 items based on the real-world experience of autistic people, and a multidisciplinary team and stakeholders reduced this pool to 50 items assessing pain, sensory hypersensitivity, and sensory hyposensitivity. In the present study, we present this new assessment tool, the Pain and Sensitivity Reactivity Scale (PSRS), and examine its psychometric properties in a sample of 270 individuals with autism spectrum disorder (ASD; mean age = 9.44, SD = 4.97), of which almost half (45%) had intellectual disability (ID).

RESULTS

A factorial model of three factors (pain, hyporeactivity, and hyperreactivity) and five specific factors for sensory hypo- and hyperreactivity, respectively, fitted the data well. Good to excellent internal consistency and adequate test-retest reliability was found for most PSRS scales. Sound psychometric properties were found in individuals with and without ID. Correlations with other measures of sensory sensitivity and pain indicated sound convergent validity.

CONCLUSIONS

PSRS shows promise as a reliable measure to analyze pain and sensory reactivity in autistic people regardless of whether they have ID or not. The measure overcomes several limitations of previous assessment tools and includes variables that are important for the understanding of the gut microbiota-brain axis in ASD.

Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.

Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments

Early identification of neurodevelopmental disorders is important to ensure a prompt and effective intervention, thus improving the later outcome. Autism spectrum disorder (ASD) and language learning impairment (LLI) are among the most common neurodevelopmental disorders, and they share overlapping symptoms. This study aims to characterize baseline electroencephalography (EEG) spectral power in 6- and 12-month-old infants at higher likelihood of developing ASD and LLI, compared to typically developing infants, and to preliminarily verify if spectral power components associated with the risk status are also linked with the later ASD or LLI diagnosis. We found risk status for ASD to be associated with reduced power in the low-frequency bands and risk status for LLI with increased power in the high-frequency bands. Interestingly, later diagnosis shared similar associations, thus supporting the potential role of EEG spectral power as a biomarker useful for understanding pathophysiology and classifying diagnostic outcomes.

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.

Sensory Processing and Autistic Traits: Mediation Effect of Frontal Alpha Asymmetry

A sensory processing approach can be used to intervene with behaviours in individuals with autistic symptoms. However, neural mechanisms linking sensory processing patterns and autistic features are less understood. The purpose of this study was to investigate whether frontal alpha asymmetry could mediate the relationship between atypical sensory processing and autistic traits. Seventy-three neurotypical young adults were included in this study. Resting-state brain activity was recorded using electroencephalography. After the recording, participants completed the Adolescent/Adult Sensory Profile and the Autism-Spectrum Quotient. Frontal alpha asymmetry was calculated by subtracting left frontal alpha power from right frontal alpha power. Correlation analysis was performed to find which sensory processing patterns were related to frontal alpha asymmetry and autistic traits. Mediation analysis was then conducted with sensory avoiding patterns as an independent variable, autistic traits as a dependent variable, and frontal alpha asymmetry as a mediator. Interrelations between higher sensation avoiding patterns, greater right-sided cortical activity, and increased autistic traits were found. The sensation avoiding patterns affected autistic traits directly and indirectly through right-sided cortical activity. Findings of the current study demonstrate a mediating role of frontal alpha asymmetry in the relationship between sensation avoiding patterns and autistic traits in neurotypical adults. This study suggests that sensation avoiding patterns and withdrawal-related emotions, which are associated with right-sided cortical activity, need to be considered to improve autism symptoms.

Brainstem white matter microstructure is associated with hyporesponsiveness and overall sensory features in autistic children

BACKGROUND

Elevated or reduced responses to sensory stimuli, known as sensory features, are common in autistic individuals and often impact quality of life. Little is known about the neurobiological basis of sensory features in autistic children. However, the brainstem may offer critical insights as it has been associated with both basic sensory processing and core features of autism.

METHODS

Diffusion-weighted imaging (DWI) and parent-report of sensory features were acquired from 133 children (61 autistic children with and 72 non-autistic children, 6-11 years-old). Leveraging novel DWI processing techniques, we investigated the relationship between sensory features and white matter microstructure properties (free-water-elimination-corrected fractional anisotropy [FA] and mean diffusivity [MD]) in precisely delineated brainstem white matter tracts. Follow-up analyses assessed relationships between microstructure and sensory response patterns/modalities and analyzed whole brain white matter using voxel-based analysis.

RESULTS

Results revealed distinct relationships between brainstem microstructure and sensory features in autistic children compared to non-autistic children. In autistic children, more prominent sensory features were generally associated with lower MD. Further, in autistic children, sensory hyporesponsiveness and tactile responsivity were strongly associated with white matter microstructure in nearly all brainstem tracts. Follow-up voxel-based analyses confirmed that these relationships were more prominent in the brainstem/cerebellum, with additional sensory-brain findings in the autistic group in the white matter of the primary motor and somatosensory cortices, the occipital lobe, the inferior parietal lobe, and the thalamic projections.

LIMITATIONS

All participants communicated via spoken language and acclimated to the sensory environment of an MRI session, which should be considered when assessing the generalizability of this work to the whole of the autism spectrum.

CONCLUSIONS

These findings suggest unique brainstem white matter contributions to sensory features in autistic children compared to non-autistic children. The brainstem correlates of sensory features underscore the potential reflex-like nature of behavioral responses to sensory stimuli in autism and have implications for how we conceptualize and address sensory features in autistic populations.

The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations

It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable functional role related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience calculated from time series such as electroencephalography (EEG) signals. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that adapts a critical modification to the original MSE algorithm, and generates reliable scale-wise entropy estimates capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED.

Neuroimaging Techniques as Descriptive and Diagnostic Tools for Infants at Risk for Autism Spectrum Disorder: A Systematic Review

Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.

Developmental trajectories of sensory patterns from infancy to school age in a community sample and associations with autistic traits

This prospective study examined the latent growth trajectories of sensory patterns among a North Carolina birth cohort (N = 1517; 49% boys, 87% White) across infancy (6-19 months), preschool (3-4 years), and school years (6-7 years). Change rates of sensory hyper- and hyporesponsiveness better differentiated children with an autism diagnosis or elevated autistic traits from those with other developmental conditions, including non-autistic children with sensory differences. More sensory hyper- and hyporesponsiveness at infancy followed by steeper increases differentially predicted more autistic traits at school age. Further, children of parents with higher education tended to show stable or improving trajectories. These findings highlight the importance of tracking sensory patterns from infancy for facilitating early identification of associated challenges and tailored support for families.

Social EEG: A novel neurodevelopmental approach to studying brain-behavior links and brain-to-brain synchrony during naturalistic toddler-parent interactions

Despite increasing emphasis on emergent brain-behavior patterns supporting language, cognitive, and socioemotional development in toddlerhood, methodologic challenges impede their characterization. Toddlers are notoriously difficult to engage in brain research, leaving a developmental window in which neural processes are understudied. Further, electroencephalography (EEG) and event-related potential paradigms at this age typically employ structured, experimental tasks that rarely reflect formative naturalistic interactions with caregivers. Here, we introduce and provide proof of concept for a new "Social EEG" paradigm, in which parent-toddler dyads interact naturally during EEG recording. Parents and toddlers sit at a table together and engage in different activities, such as book sharing or watching a movie. EEG is time locked to the video recording of their interaction. Offline, behavioral data are microcoded with mutually exclusive engagement state codes. From 216 sessions to date with 2- and 3-year-old toddlers and their parents, 72% of dyads successfully completed the full Social EEG paradigm, suggesting that it is possible to collect dual EEG from parents and toddlers during naturalistic interactions. In addition to providing naturalistic information about child neural development within the caregiving context, this paradigm holds promise for examination of emerging constructs such as brain-to-brain synchrony in parents and children.

Discriminant value of repetitive behaviors in families with autism spectrum disorder and obsessional compulsive disorder probands

Repetitive behaviors (RB) represent a wide spectrum of symptoms ranging from sensory-motor stereotypies to complex cognitive rituals, frequently dichotomized as low- and high-order sub-groups of symptoms. Even though these subgroups are considered as phenomenologically distinct in autism spectrum disorder (ASD) and obsessive-compulsive disorder (OCD), brain imaging and genetic studies suggest that they have common mechanisms and pathways. This discrepancy may be explained by the frequent intellectual disability reported in ASD, which blurs the RB expressivity. Given the high heritability of RB, that is, the diversity of symptoms expressed in the relatives are dependent on those expressed in their probands, we hypothesize that if RB expressed in ASD or OCD are two distinct entities, then the RB expressed in relatives will also reflect these two dimensions. We thus conduct a linear discriminant analysis on RB in both the relatives of probands with ASD and OCD and subjects from the general population (n = 1023). The discriminant analysis results in a classification of 81.1% of the controls (p < 10^-4 ), but poorly differentiated the ASD and OCD relatives (≈46%). The stepwise analysis reveals that five symptoms attributed to high-order RB and two related to low-order RB (including hypersensitivity) are the most discriminant. Our results support the idea that the difference of RB patterns in the relatives is mild compared with the distribution of symptoms in controls. Our findings reinforce the evidence of a common biological pattern of RB both in ASD and OCD but with minor differences, specific to each of these two neuro-developmental disorders. LAY SUMMARY: Repetitive behaviors (RB), a key symptom in the classification of both OCD and ASD, are phenomenologically considered as distinct in the two disorders, which is in contrast with brain imaging studies describing a common neural circuit. Intellectual disability, which is frequently associated with ASD, makes RB in ASD more difficult to understand as it affects the expression of the RB symptoms. To avoid this bias, we propose to consider the familial aggregation in ASD and OCD by exploring RB in the first-degree relatives of ASD and OCD. Our results highlight the existence of RB expressed in relatives compared to the general population, with a common pattern of symptoms in relatives of both ASD and OCD but also minor differences, specific to each of these two neuro-developmental disorders.

Associations between sensory processing and electrophysiological and neurochemical measures in children with ASD: an EEG-MRS study

Background

Autism spectrum disorder (ASD) is associated with hyper- and/or hypo-sensitivity to sensory input. Spontaneous alpha power, which plays an important role in shaping responsivity to sensory information, is reduced across the lifespan in individuals with ASD. Furthermore, an excitatory/inhibitory imbalance has also been linked to sensory dysfunction in ASD and has been hypothesized to underlie atypical patterns of spontaneous brain activity. The present study examined whether resting-state alpha power differed in children with ASD as compared to TD children, and investigated the relationships between alpha levels, concentrations of excitatory and inhibitory neurotransmitters, and atypical sensory processing in ASD.

Methods

Participants included thirty-one children and adolescents with ASD and thirty-one age- and IQ-matched typically developing (TD) participants. Resting-state electroencephalography (EEG) was used to obtain measures of alpha power. A subset of participants (ASD = 16; TD = 16) also completed a magnetic resonance spectroscopy (MRS) protocol in order to measure concentrations of excitatory (glutamate + glutamine; Glx) and inhibitory (GABA) neurotransmitters.

Results

Children with ASD evidenced significantly decreased resting alpha power compared to their TD peers. MRS estimates of GABA and Glx did not differ between groups with the exception of Glx in the temporal-parietal junction. Inter-individual differences in alpha power within the ASD group were not associated with region-specific concentrations of GABA or Glx, nor were they associated with sensory processing differences. However, atypically decreased Glx was associated with increased sensory impairment in children with ASD.

Conclusions

Although we replicated prior reports of decreased alpha power in ASD, atypically reduced alpha was not related to neurochemical differences or sensory symptoms in ASD. Instead, reduced Glx in the temporal-parietal cortex was associated with greater hyper-sensitivity in ASD. Together, these findings may provide insight into the neural underpinnings of sensory processing differences present in ASD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-020-09351-0.

Comparing different EEG connectivity methods in young males with ASD

Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.

A review of the use of EEG connectivity to measure the neurological characteristics of the sensory features in young people with autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting about 1 in 100 children and is currently incurable. ASD represents a challenge to traditional methods of assessment and diagnosis, and it has been suggested that direct measures of brain activity and connectivity between brain regions during demanding tasks represents a potential pathway to building more accurate models of underlying brain function and ASD. One of the key behavioural diagnostic indicators of ASD consists of sensory features (SF), often characterised by over- or under-reactivity to environmental stimuli. SF are associated with behavioural difficulties that impede social and education success in these children as well as anxiety and depression. This review examines the previous literature on the measurement of EEG connectivity and SF observed in individuals with ASD.

Distinct Patterns of Neural Habituation and Generalization in Children and Adolescents With Autism With Low and High Sensory Overresponsivity

OBJECTIVE

Sensory overresponsivity (SOR), an atypical negative reaction to sensory stimuli, is highly prevalent in autism spectrum disorder (ASD). Previous work has related SOR to increased brain response in sensory-limbic regions. This study investigated where these atypical responses fall in three fundamental stages of sensory processing: arousal (i.e., initial response), habituation (i.e., change in response over time), and generalization of response to novel stimuli. Different areas of atypical response would require distinct intervention approaches.

METHODS

Functional MRI was used to examine these patterns of neural habituation to two sets of similar mildly aversive auditory and tactile stimuli in 42 high-functioning children and adolescents with ASD (21 with high levels of SOR and 21 with low levels of SOR) and 27 age-matched typically developing youths (ages 8-17). The relationship between SOR and change in amygdala-prefrontal functional connectivity across the sensory stimulation was also examined.

RESULTS

Across repeated sensory stimulation, high-SOR participants with ASD showed reduced ability to maintain habituation in the amygdala and relevant sensory cortices and to maintain inhibition of irrelevant sensory cortices. These results indicate that sensory habituation is a dynamic, time-varying process dependent on sustained regulation across time, which is a particular deficit in high-SOR participants with ASD. However, low-SOR participants with ASD also showed distinct, nontypical neural response patterns, including reduced responsiveness to novel but similar stimuli and increases in prefrontal-amygdala regulation across the sensory exposure.

CONCLUSIONS

The results suggest that all children with autism have atypical brain responses to sensory stimuli, but whether they express atypical behavioral responses depends on top-down regulatory mechanisms. Results are discussed in terms of targeted intervention approaches.

Longitudinal EEG power in the first postnatal year differentiates autism outcomes

An aim of autism spectrum disorder (ASD) research is to identify early biomarkers that inform ASD pathophysiology and expedite detection. Brain oscillations captured in electroencephalography (EEG) are thought to be disrupted as core ASD pathophysiology. We leverage longitudinal EEG power measurements from 3 to 36 months of age in infants at low- and high-risk for ASD to test how and when power distinguishes ASD risk and diagnosis by age 3-years. Power trajectories across the first year, second year, or first three years postnatally were submitted to data-driven modeling to differentiate ASD outcomes. Power dynamics during the first postnatal year best differentiate ASD diagnoses. Delta and gamma frequency power trajectories consistently distinguish infants with ASD diagnoses from others. There is also a developmental shift across timescales towards including higher-frequency power to differentiate outcomes. These findings reveal the importance of developmental timing and trajectory in understanding pathophysiology and classifying ASD outcomes.

Brain oscillations may be disrupted in children with autism spectrum disorder. The authors performed a longitudinal study of electroencephalography recordings and found that EEG recordings from the first year after birth can distinguish healthy children from children with autism spectrum disorder.

Higher Tactile Temporal Resolution as a Basis of Hypersensitivity in Individuals with Autism Spectrum Disorder

Many individuals with autism spectrum disorder (ASD) have symptoms of sensory hypersensitivity. Several studies have shown high individual variations in temporal processing of tactile stimuli. We hypothesized that these individual differences are linked to differences in hyper-reactivity among individuals with ASD. Participants performed two tasks as to vibrotactile stimuli: One is a temporal order judgement task, and another is a detection task. We found that individuals with ASD with higher temporal resolution tended to have more severe hypersensitivity symptoms. In contrast, the tactile detection threshold/sensitivity were related to the severities of stereotyped behaviour and restricted interests, rather than to hypersensitivity. Our findings demonstrate that higher temporal resolution to sensory stimuli may contribute to sensory hypersensitivity in individuals with ASD.

Single Trial Plasticity in Evidence Accumulation Underlies Rapid Recalibration to Asynchronous Audiovisual Speech

Asynchronous arrival of audiovisual information at the peripheral sensory organs is a ubiquitous property of signals in the natural environment due to differences in the propagation time of light and sound. As these cues are constantly changing their distance from the observer, rapid adaptation to asynchronies is crucial for their appropriate integration. We investigated the neural basis of rapid recalibration to asynchronous audiovisual speech in humans using a combination of psychophysics, drift diffusion modeling, and electroencephalography (EEG). Consistent with previous reports, we found that perception of audiovisual temporal synchrony depends on the temporal ordering of the previous trial. Drift diffusion modelling indicated that this recalibration effect is well accounted for by changes in the rate of evidence accumulation (i.e. drift rate). Neural responses as indexed via evoked potentials were similarly found to vary based on the temporal ordering of the previous trial. Within and across subject correlations indicated that the observed changes in drift rate and the modulation of evoked potential magnitude were related. These results indicate that the rate and direction of evidence accumulation are affected by immediate sensory history and that these changes contribute to single trial recalibration to audiovisual temporal asynchrony.

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

Developmental sequelae and neurophysiologic substrates of sensory seeking in infant siblings of children with autism spectrum disorder

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Infant siblings of children with autism (Sibs-ASD) display elevated sensory seeking.

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Sibs-ASD who will be diagnosed with ASD show the highest levels of sensory seeking.

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Sensory seeking at 18 months predicts future social symptomatology in Sibs-ASD.

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The effect of seeking on social symptomatology is explained by reduced social orienting.

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Atypical frontal asymmetry may underlie early differences in sensory seeking.

It has been proposed that early differences in sensory responsiveness arise from atypical neural function and produce cascading effects on development across domains. This longitudinal study prospectively followed infants at heightened risk for autism spectrum disorder (ASD) based on their status as younger siblings of children diagnosed with ASD (Sibs-ASD) and infants at relatively lower risk for ASD (siblings of typically developing children; Sibs-TD) to examine the developmental sequelae and possible neurophysiological substrates of a specific sensory response pattern: unusually intense interest in nonsocial sensory stimuli or “sensory seeking.” At 18 months, sensory seeking and social orienting were measured with the Sensory Processing Assessment, and a potential neural signature for sensory seeking (i.e., frontal alpha asymmetry) was measured via resting state electroencephalography. At 36 months, infants’ social symptomatology was assessed in a comprehensive diagnostic evaluation. Sibs-ASD showed elevated sensory seeking relative to Sibs-TD, and increased sensory seeking was concurrently associated with reduced social orienting across groups and resting frontal asymmetry in Sibs-ASD. Sensory seeking also predicted later social symptomatology. Findings suggest that sensory seeking may produce cascading effects on social development in infants at risk for ASD and that atypical frontal asymmetry may underlie this atypical pattern of sensory responsiveness.