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

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Altered patterns of sensory responsiveness are a frequently reported feature of Autism Spectrum Disorder (ASD). Younger siblings of individuals with ASD are at a greatly elevated risk of a future diagnosis of ASD, but little is known about the neural basis of sensory responsiveness patterns in this population. Younger siblings (n = 20) of children diagnosed with ASD participated in resting electroencephalography (EEG) at an age of 18 months. Data on toddlers' sensory responsiveness were obtained using the Sensory Experiences Questionnaire. Correlations were present between hyporesponsiveness and patterns of oscillatory power, functional connectivity, and signal complexity. Our findings suggest that neural signal features hold promise for facilitating early identification and targeted remediation in young children at risk for ASD.
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J Autism Dev Disord (2017) 47:2710–2722
DOI 10.1007/s10803-017-3191-4
1 3
Neural Correlates ofSensory Hyporesponsiveness inToddlers
atHigh Risk forAutism Spectrum Disorder
DavidM.Simon1,2· CaraR.Damiano3· TiffanyG.Woynaroski4,5· LisaV.Ibañez6·
MichaelMurias7· WendyL.Stone6· MarkT.Wallace2,4,5,8,9· CarissaJ.Cascio4,9
Published online: 8 June 2017
© Springer Science+Business Media New York 2017
Our findings suggest that neural signal features hold prom-
ise for facilitating early identification and targeted remedia-
tion in young children at risk for ASD.
Keywords Autism spectrum disorder· Infant siblings·
Electroencephalogram (EEG)· Functional connectivity·
Frontal EEG asymmetry· Sensory hyporesponsiveness
The Need toIdentify Neurophysiological Substrates
ofSensory Hyporesponsiveness inASD
Atypical responses to sensory stimuli have been noted
since the earliest accounts of autism spectrum disorder
(ASD) and are now recognized as a core feature of the
disorder (American Psychiatric Association 2013; Kan-
ner 1943). A broad range of altered sensory responses
has been observed across individuals with ASD; however,
hyporesponsiveness is of special interest. Hyporespon-
siveness is characterized by the absence, diminishment,
or delay of the expected behavioral response to sensory
stimuli (Baranek et al. 2006). Examples of hyporespon-
sive behaviors include a failure to orient towards a novel
sound, or a reduced response to unpleasant or painful tac-
tile stimuli (Baranek etal. 2006). This pattern of sensory
responsiveness is highly prevalent in ASD and differenti-
ates ASD from generalized developmental delay (Baranek
et al. 2006; Ben-Sasson etal. 2009; Rogers and Ozonoff
2005). Evidence that hyporesponsiveness emerges early
in development (Baranek 1999; Freuler etal. 2012; Jones
et al. 2003) and is associated with broader ASD sympto-
mology (Baranek etal. 2013; Foss-Feig etal. 2012; Wat-
son etal. 2011) has led researchers to propose that reduced
Abstract Altered patterns of sensory responsiveness are
a frequently reported feature of Autism Spectrum Disorder
(ASD). Younger siblings of individuals with ASD are at a
greatly elevated risk of a future diagnosis of ASD, but little
is known about the neural basis of sensory responsiveness
patterns in this population. Younger siblings (n = 20) of
children diagnosed with ASD participated in resting elec-
troencephalography (EEG) at an age of 18 months. Data on
toddlers’ sensory responsiveness were obtained using the
Sensory Experiences Questionnaire. Correlations were pre-
sent between hyporesponsiveness and patterns of oscilla-
tory power, functional connectivity, and signal complexity.
* Carissa J. Cascio
1 Neuroscience Graduate Program, Vanderbilt Brain Institute,
Vanderbilt University Medical Center, Vanderbilt University,
Nashville, TN, USA
2 Vanderbilt Brain Institute, Vanderbilt University Medical
Center, Vanderbilt University, Nashville, TN, USA
3 Duke Center forAutism andBrain Development, Duke
University, Durham, NC, USA
4 Vanderbilt Kennedy Center, Vanderbilt University, Nashville,
5 Department ofHearing andSpeech Sciences, Vanderbilt
University Medical Center, Nashville, TN, USA
6 Department ofPsychology, University ofWashington,
Seattle, WA, USA
7 Duke Institute forBrain Sciences, Duke University, Durham,
8 Department ofPsychology, Vanderbilt University, Nashville,
9 Department ofPsychiatry, Vanderbilt University, Nashville,
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... The ADI-R is a standardised and semi-structured interview with the participant's parent to assess previous and current autistic symptoms, following ICD-10 and DSM-IV criteria (Lord et al., 1994). Several studies conducted after the development of the DSM-5 that have used the ADI-R for research purposes (Isler et al., 2010;Machado et al., 2015;Magana & Vanegas, 2017;Simon et al., 2017). Lord et al. (1994) have demonstrated interrater reliability coefficients for the majority of the ADI-R items to be over 0.70, with no coefficients below 0.60. ...
... The relevant ASD literature using EEG to measure power spectra and connectivity under resting or task-based experimental conditions has reported abnormalities mainly within and between Frontal, Temporal, and Occipital brain regions (Linden & Gunkelman, 2013;O'Reilly et al., 2017;Simon et al., 2017;Wang et al., 2013). In particular, research measuring event-related potentials (ERP) and power spectra to identify sensory features (SF) in autistic children and adolescents has found impairments primarily in Frontal and Temporal regions (Baum et al., 2015;Jeste & Nelson, 2009;Linden & Gunkelman, 2013;Marco et al., 2011). ...
... In addition, studies identified as directly investigating EEG connectivity and SF (using either uni-or multi-sensory experimental stimuli) in autistic children and adolescents have also found more instances of altered connectivity within and between the Frontal, Temporal, and Occipital regions (Isler et al., 2010;Lazarev et al., 2009Lazarev et al., , 2015Machado et al., 2015;Simon et al., 2017). Based on these previous findings and the nature of the experimental stimuli chosen (i.e., audiovisual stimuli), it was decided to calculate connectivity between Frontal and Temporal, and Frontal and Occipital regions. ...
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Several lines of research suggest that autism is a neurological phenomenon, but the precise associations between neurological activity and the key diagnostic symptoms of autism are yet to be completely clarified. This study examined EEG connectivity and Sensory Features (SF) in a sample of young autistic males by examining bi-directional neural connectivity between separate brain regions as the key potential correlate of SF. Forty male autistic participants aged between 6 and 17 years, with an IQ of at least 70, underwent EEG measurements of their Frontal, Occipital and Temporal region responses to low-, medium-, and high-intensity audiovisual stimulus conditions. EEG connectivity data were analysed via Granger Causality. SF was measured via parent responses about their sons on the Child Sensory Profile (2nd ed.) (CSP-2). There were significant (p < .05) correlations between right hemisphere Frontal and Temporal connectivity and CSP-2 dominant scores, largely due to lower Temporal-to-Frontal than Frontal-to-Temporal connectivity. There were no significant correlations between general CSP-2 scores and EEG connectivity data collected during audiovisual stimuli. These results confirm and extend previous findings by adding bi-directional connectivity as an index of brain activity to other studies that used only uni-directional connectivity data when measuring SF. Although there may be a discrepancy between the kinds of information collected via instruments such as the CSP-2 and actual brain electrical connectivity across major regions, these results hold implications for the use of brain-training interventions with autistic boys.
... Further, significantly reduced alpha power has also been shown in school-aged children and adolescents [18][19][20][21] as well as adults [22] with ASD (although see [23][24][25], for evidence of greater, or, [26], for evidence of equivalent alpha power in ASD). Within ASD, differences in alpha power have been related to sensory hypo-responsiveness [27], sensory seeking behavior [28], and greater attention to detail [24], suggesting that they may contribute to the sensory processing differences present in individuals with ASD (see [29], for review). ...
... Lastly, although we observed associations between alpha levels and Sensitivity and Seeking scores within the TD group, we did not find evidence of a relationship between alpha levels and sensory symptoms within children with ASD. Previous research demonstrating links between alpha and sensory symptoms has focused on frontal alpha asymmetry rather than absolute alpha power [27,28]. Thus, hemispheric variations in alpha may be more sensitive to differences in sensory processing symptoms rather than absolute levels. ...
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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.
... Beyond this age and within the first two years of life, HR infants display a different developmental trajectory in changes in spectral power compared to their LR counterparts [19]. Relationships between frontal power and sensory responsiveness in HR infants also exist; one study found that lower levels of sensory responsiveness were associated with higher alpha band synchronization in occipital and temporal regions and higher theta connectivity between frontal and posterior regions [20]. ...
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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.
... Although different researchers have put forward different factors for feature extraction to classify ASD from control, only few studies have been conducted to test and validate the results (Alotaibi & Maharatna, 2021;Duffy & Als, 2012;Matlis et al., 2015;Nowicka et al., 2016;Wadhera & Kakkar, 2020). Several connectivity studies in ASD are associated with reduced long-range connectivity and increased local connectivity (Carson et al., 2014;Coben et al., 2008;Haartsen et al., 2019;Isler et al., 2010;Murias et al., 2007;Nowicka et al., 2016;Shephard et al., 2022;Shou et al., 2017;Simon et al., 2017;Tran et al., 2021;Wang et al., 2020). Enhanced local connectivity resonates well with enhanced skills seen in typical autistic savant's brain (Dakin & Frith, 2005;Mottron et al., 2006)at the cost of low communication with distant regions. ...
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Over the last two decades, there has been a tremendous increase in research activity on brain connectivity studies and its application in different neurological disorders. Studies have been focused on different connectivity patterns generated and potential biomarkers that could be derived to find the etymology of the disorder. In this review, the focus is on the utilization of wireless electroencephalogram monitoring system for functional connectivity analysis and its capacity for deciphering neurological disorders. The paper reviews different methods adopted to estimate connectivity and the possible convergence of connectivity patterns in four neurological disorders: epilepsy, autism spectrum disorder, Alzheimer and Parkinson's disease. The paper reviews the current status of connectivity research in the aforementioned neurological disorders and its potential in developing a smart e‐health service.
... This indicates that sensory patterns may be critical behavioral markers for early detection of ASD, which further introduces opportunities for early intervention leading to better outcomes. Despite the existing cross-sectional evidence of linkages between sensory patterns and autism symptoms or risk at both behavioral and neurophysiological levels (Rogers et al., 2003;Liss et al., 2006;Watson et al., 2011;Simon et al., 2017), there is currently a lack of evidence on the longitudinal impact of sensory patterns on later severity of autistic traits. Recently, longitudinal behavioral and electrophysiological studies demonstrated that sensory seeking behavior by 24 months predicts later social difficulties at 36 months of age (Baranek et al., 2018;Damiano-Goodwin et al., 2018). ...
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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.
... While many studies demonstrate change in MSE across development (Bosl et al., 2011;De Wel et al., 2017;Hasegawa et al., 2018;Kang et al., 2019;Lippé et al., 2009;McIntosh et al., 2008;Miskovic et al., 2016;Polizzotto et al., 2016;Szostakiwskyj et al., 2017;Zhang et al., 2009) or with developmental disorder (Begum et al., 2017;Chenxi et al., 2016;Eroğlu et al., 2020;Kang et al., 2018;Liu et al., 2017;Okazaki et al., 2015;Rezaeezadeh et al., 2020;Simon et al., 2017;Wadhera and Kakkar, 2020;Weng et al., 2017), the vastly differing preprocessing choices and widespread failure to adopt the critical MSE algorithm modification of scale-wise recalculation of the similarity criterion makes it challenging to compare results across studies and realize the role of entropy in neurodevelopment. ...
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It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable function role and captures properties of brain function 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. 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 EEG 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 preprocessing and entropy estimation pipeline that generates scale-wise entropy estimates that are reliable and 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 The dataset used herein to develop and validate the pipeline is available for download from
... Based on the number of dependent variables (i.e., connectivity indices) and statistical tests used in the five previous studies presented in Table 1, only 1.78% of the 731 total significant results (this is an estimation based on the reported results in text or graphical form from each journal article) would be considered to be statistically significant at the corrected p-value. Two of these five studies (Machado et al., 2015;Simon et al., 2017) did not meet this assumption and appeared to apply post-hoc hypothesis-formulation. These shortcomings are not isolated to eeg studies of SF in autistic children but are common in the wider scientific literature, in which 50% of published studies may have at least one statistical limitation of this kind (Altman & Royston, 2006). ...
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Investigation of the neurological underpinnings of the diagnostic symptoms for Autism Spectrum Disorder (ASD) represents a potential pathway towards a biomarker for this disorder. One of the key symptoms of ASD is Sensory Features (SF), which refers to the difficulties that autistic people experience with particular kinds of environmental stimuli. Studies using eeg measures of neural connectivity across various regions of the brain hold promise in identifying how the autistic brain reacts to its environment. This commentary identifies several ‘participant’ and ‘measurement’ methodological issues that need to be adequately addressed in SF-eeg connectivity studies, and applies these comments to a sample of five previous studies. Recommendations are made for future research procedures.
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.
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.
Since its conception, Autism Spectrum Disorder (ASD) has been recognized as a behavioral condition underpinned by atypical neurodevelopmental processes. Recent advances in neuroimaging techniques have allowed for further investigations into the specific neural substrates that may be associated with the behavioral features of ASD. While there is general agreement that an atypical cortical growth trajectory in ASD impacts the organization and connectivity of neural pathways, the specific regions affected and the associations between brain abnormalities and behavioral symptoms remain largely unknown. We summarize the available evidence regarding the trajectory of neural growth in ASD. We then synthesize evidence regarding the associations between brain development and the behavioral phenotype of ASD. Overall, we provide a primer to ASD and brain development across the lifespan.
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Many aspects of perception and cognition are supported by activity in neural populations that are tuned to different stimulus features (e.g., orientation, spatial location, color). Goal-directed behavior, such as sustained attention, requires a mechanism for the selective prioritization of contextually appropriate representations. A candidate mechanism of sustained spatial attention is neural activity in the alpha band (8-13 Hz), whose power in the human EEG covaries with the focus of covert attention. Here, we applied an inverted encoding model to assess whether spatially selective neural responses could be recovered from the topography of alpha-band oscillations during spatial attention. Participants were cued to covertly attend to one of six spatial locations arranged concentrically around fixation while EEG was recorded. A linear classifier applied to EEG data during sustained attention demonstrated successful classification of the attended location from the topography of alpha power, although not from other frequency bands. We next sought to reconstruct the focus of spatial attention over time by applying inverted encoding models to the topography of alpha power and phase. Alpha power, but not phase, allowed for robust reconstructions of the specific attended location beginning around 450 msec postcue, an onset earlier than previous reports. These results demonstrate that posterior alpha-band oscillations can be used in track activity in feature-selective neural populations with high temporal precision during the deployment of covert spatial attention.
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The goal of this study was to explore neural response to touch in children with and without autism spectrum disorder (ASD). Patterns of reduced (hypo-responsiveness) and enhanced (hyper-responsiveness) behavioral reaction to sensory input are prevalent in ASD, but their neural mechanisms are poorly understood. We measured event-related potentials (ERP) to a puff of air on the fingertip and collected parent report of tactile hypo- and hyper-responsiveness in children with ASD (n = 21, mean (SD) age 11.25 (3.09), 2 female), and an age-matched typically developing comparison group (n = 28, mean (SD) age 10.1 (3.08, 2 female). A global measure of ERP response strength approximately 220-270 ms post-stimulus was associated with tactile hypo-responsiveness in ASD, while tactile hyper-responsiveness was associated with earlier neural response (approximately 120-220 ms post-stimulus) in both groups. These neural responses also related to autism severity. These results suggest that, in ASD, tactile hypo- and hyper-responsiveness may reflect different waypoints in the neural processing stream of sensory input. The timing of the relationship for hyper-responsiveness is consistent with somatosensory association cortical response, while that for hypo-responsiveness is more consistent with later processes that may involve allocation of attention or emotional valence to the stimulus.
Autism Spectrum Disorder (ASD) is a highly prevalent developmental disability characterized by deficits in social communication and interaction, restricted interests, and repetitive behaviors. Recently, anomalous sensory and perceptual function has gained an increased level of recognition as an important feature of ASD. A specific impairment in the ability to integrate information across brain networks has been proposed to contribute to these disruptions. A crucial mechanism for these integrative processes is the rhythmic synchronization of neuronal excitability across neural populations; collectively known as oscillations. In ASD there is believed to be a deficit in the ability to efficiently couple functional neural networks using these oscillations. This review discusses evidence for disruptions in oscillatory synchronization in ASD, and how disturbance of this neural mechanism contributes to alterations in sensory and perceptual function. The review also frames oscillatory data from the perspective of prevailing neurobiologically-inspired theories of ASD.
The objective is to overview recent findings on early detection/diagnosis of autism spectrum disorders, as well as clinical trials of early interventions for toddlers at risk for/diagnosed with autism spectrum disorder. Prospective studies of infants at high risk of autism spectrum disorder have yielded significant advances in understanding early development in autism spectrum disorder. Findings from prospective studies indicate that abnormalities in social communication and repetitive behaviors emerge during the second year, whereas additional "prodromal features" (motor and sensory abnormalities) emerge in the first year. Subsequently, exciting progress has been made in establishing the efficacy of autism spectrum disorder-specific interventions for toddlers as young as 15 months. Finally, efforts occur to characterize autism spectrum disorder-specific characteristics in genetic syndromes with concurrent autism spectrum disorder symptomatology. Substantial progress in characterizing early developmental trajectories as well as the identification of specific behavioral markers has aided early detection. Work remains to ensure that research findings are translated into clinical practice for uptake in the health care system. © The Author(s) 2015.
In recent years, numerous studies have tested the relevance of neural oscillations in neuropsychiatric conditions, highlighting the potential role of changes in temporal coordination as a pathophysiological mechanism in brain disorders. In the current review, we provide an update on this hypothesis because of the growing evidence that temporal coordination is essential for the context and goal-dependent, dynamic formation of large-scale cortical networks. We shall focus on issues that we consider particularly promising for a translational research program aimed at furthering our understanding of the origins of neuropsychiatric disorders and the development of effective therapies. We will focus on schizophrenia and autism spectrum disorders (ASDs) to highlight important issues and challenges for the implementation of such an approach. Specifically, we will argue that deficits in temporal coordination lead to a disruption of functional large-scale networks, which in turn can account for several specific dysfunctions associated with these disorders.