Prospective,Blinded Exploratory Evaluation of the PlayWisely Program in Children with Autism Spectrum Disorder

The purpose of the study was to explore a low-cost intervention that targets an increasingly common developmental disorder. The study was a blinded, exploratory evaluation of the PlayWisely program on autism symptoms and essential learning foundation skills (attention, recognition, and memory skills) in children with a diagnosis of autism, autism spectrum disorder (ASD), pervasive developmental disorder – not otherwise specified (PDD-NOS), and Asperger syndrome (AS). Eighteen children, 1 to 10 years of age, were evaluated using the Childhood Autism Rating Scale, Second Edition (CARS2); the PlayWisely Interactive Test of Attention, Recognition, and Memory Skills; Autism Treatment Evaluation Checklist (ATEC), and the Modified Checklist for Autism in Toddlers (M-CHAT). There were significant treatment effects for the PlayWisely measure on the Yellow Sets that examine recognition; Purple Sets that examine brain region agility and early memory skills; Blue Sets that examine phonemic awareness and recognition; and for the Total Sets, with a similar trend toward improvement in the Green Sets that examine perception and Red Sets that examine attention. No other measures reached statistical significance. The results suggest that PlayWisely can improve recognition, brain region agility, phonemic awareness, letter recognition, and early memory skills in ASD. It was observed by the parents, coaches, and study investigators that the children who were less than 3 years of age showed improvements in autism symptoms; however, the group was too small to reach statistical significance. Future studies are needed to see if this intervention can mitigate autism symptoms in very young children with ASD.     

Intelligence May Moderate the Cognitive Profile of Patients with ASD

BackgroundThe intelligence of individuals with Autism Spectrum Disorder (ASD) varies considerably. The pattern of cognitive deficits associated with ASD may differ depending on intelligence. We aimed to study the absolute and relative severity of cognitive deficits in participants with ASD in relation to IQ.MethodsA total of 274 children (M age = 12.1, 68.6% boys) participated: 30 ASD and 22 controls in the below average Intelligence Quotient (IQ) group (IQ<85), 57 ASD and 54 controls in the average IQ group (85<IQ<115) and 41 ASD and 70 controls in the above average IQ group (IQ>115). Matching for age, sex, Full Scale IQ (FSIQ), Verbal IQ (VIQ), Performance IQ (PIQ) and VIQ-PIQ difference was performed. Speed and accuracy of social cognition, executive functioning, visual pattern recognition and basic processing speed were examined per domain and as a composite score.ResultsThe composite score revealed a trend significant IQ by ASD interaction (significant when excluding the average IQ group). In absolute terms, participants with below average IQs performed poorest (regardless of diagnosis). However, in relative terms, above average intelligent participants with ASD showed the most substantial cognitive problems (particularly for social cognition, visual pattern recognition and verbal working memory) since this group differed significantly from the IQ-matched control group (p < .001), whereas this was not the case for below-average intelligence participants with ASD (p = .57).ConclusionsIn relative terms, cognitive deficits appear somewhat more severe in individuals with ASD and above average IQs compared to the below average IQ patients with ASD. Even though high IQ ASD individuals enjoy a certain protection from their higher IQ, they clearly demonstrate cognitive impairments that may be targeted in clinical assessment and treatment. Conversely, even though in absolute terms ASD patients with below average IQs were clearly more impaired than ASD patients with average to above average IQs, the differences in cognitive functioning between participants with and without ASD on the lower end of the IQ spectrum were less pronounced. Clinically this may imply that cognitive assessment and training of cognitive skills in below average intelligent children with ASD may be a less fruitful endeavour. These findings tentatively suggest that intelligence may act as a moderator in the cognitive presentation of ASD, with qualitatively different cognitive processes affected in patients at the high and low end of the IQ spectrum.     

A case study of a multiply talented savant with an autism spectrum disorder: neuropsychological functioning and brain morphometry

Neuropsychological functioning and brain morphometry in a savant (case GW) with an autism spectrum disorder (ASD) and both calendar calculation and artistic skills are quantified and compared with small groups of neurotypical controls. Good memory, mental calculation and visuospatial processing, as well as (implicit) knowledge of calendar structure and ‘weak’ central coherence characterized the cognitive profile of case GW. Possibly reflecting his savant skills, the superior parietal region of GW''s cortex was the only area thicker (while areas such as the superior and medial prefrontal, middle temporal and motor cortices were thinner) than that of a neurotypical control group. Taken from the perspective of learning/practice-based models, skills in domains (e.g. calendars, art, music) that capitalize upon strengths often associated with ASD, such as detail-focused processing, are probably further enhanced through over-learning and massive exposure, and reflected in atypical brain structure.     

Cortical signatures of auditory object binding in children with autism spectrum disorder are anomalous in concordance with behavior and diagnosis

Organizing sensory information into coherent perceptual objects is fundamental to everyday perception and communication. In the visual domain, indirect evidence from cortical responses suggests that children with autism spectrum disorder (ASD) have anomalous figure–ground segregation. While auditory processing abnormalities are common in ASD, especially in environments with multiple sound sources, to date, the question of scene segregation in ASD has not been directly investigated in audition. Using magnetoencephalography, we measured cortical responses to unattended (passively experienced) auditory stimuli while parametrically manipulating the degree of temporal coherence that facilitates auditory figure–ground segregation. Results from 21 children with ASD (aged 7–17 years) and 26 age- and IQ-matched typically developing children provide evidence that children with ASD show anomalous growth of cortical neural responses with increasing temporal coherence of the auditory figure. The documented neurophysiological abnormalities did not depend on age, and were reflected both in the response evoked by changes in temporal coherence of the auditory scene and in the associated induced gamma rhythms. Furthermore, the individual neural measures were predictive of diagnosis (83% accuracy) and also correlated with behavioral measures of ASD severity and auditory processing abnormalities. These findings offer new insight into the neural mechanisms underlying auditory perceptual deficits and sensory overload in ASD, and suggest that temporal-coherence-based auditory scene analysis and suprathreshold processing of coherent auditory objects may be atypical in ASD.

To navigate everyday environments, the auditory system must analyze the temporal coherence of sound elements scattered across different frequencies to organize them into discrete perceptual objects. This neuroimaging study reveals that this process is altered in autism, potentially explaining the often-experienced sensory overload.     

The Strengths and Difficulties Questionnaire as a Predictor of Parent-Reported Diagnosis of Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

The Strengths and Difficulties Questionnaire (SDQ) is widely used as an international standardised instrument measuring child behaviour. The primary aim of our study was to examine whether behavioral symptoms measured by SDQ were elevated among children with autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) relative to the rest of the population, and to examine the predictive value of the SDQ for outcome of parent-reported clinical diagnosis of ASD/ADHD. A secondary aim was to examine the extent of overlap in symptoms between children diagnosed with these two disorders, as measured by the SDQ subscales. A cross-sectional secondary analysis of data from the Millennium Birth Cohort (n = 19,519), was conducted. Data were weighted to be representative of the UK population as a whole. ADHD or ASD identified by a medical doctor or health professional were reported by parents in 2008 and this was the case definition of diagnosis; (ADHD n = 173, ASD n = 209, excluding twins and triplets). Study children''s ages ranged from 6.3–8.2 years; (mean 7.2 years). Logistic regression was used to examine the association between the parent-reported clinical diagnosis of ASD/ADHD and teacher and parent-reported SDQ subscales. All SDQ subscales were strongly associated with both ASD and ADHD. There was substantial co-occurrence of behavioral difficulties between children diagnosed with ASD and those diagnosed with ADHD. After adjustment for other subscales, the final model for ADHD, contained hyperactivity/inattention and impact symptoms only and had a sensitivity of 91% and specificity of 90%; (AUC) = 0.94 (95% CI, 0.90–0.97). The final model for ASD was composed of all subscales except the ‘peer problems’ scales, indicating of the complexity of behavioural difficulties that may accompany ASD. A threshold of 0.03 produced model sensitivity and specificity of 79% and 93% respectively; AUC = 0.90 (95% CI, 0.86–0.95). The results support changes to DSM-5 removing exclusivity clauses.     

Trends in the Prevalence of Autism Spectrum Disorder,Cerebral Palsy,Hearing Loss,Intellectual Disability,and Vision Impairment,Metropolitan Atlanta, 1991–2010

This study examined the prevalence and characteristics of autism spectrum disorder (ASD), cerebral palsy (CP), hearing loss (HL), intellectual disability (ID), and vision impairment (VI) over a 15–20 year time period, with specific focus on concurrent changes in ASD and ID prevalence. We used data from a population-based developmental disabilities surveillance program for 8-year-olds in metropolitan Atlanta. From 1991–2010, prevalence estimates of ID and HL were stable with slight increases in VI prevalence. CP prevalence was constant from 1993–2010. The average annual increase in ASD prevalence was 9.3% per year from 1996–2010, with a 269% increase from 4.2 per 1,000 in 1996 to 15.5 per 1,000 in 2010. From 2000–2010, the prevalence of ID without ASD was stable; during the same time, the prevalence of ASD with and without co-occurring ID increased by an average of 6.6% and 9.6% per year, respectively. ASD prevalence increases were found among both males and females, and among nearly all racial/ethnic subgroups and levels of intellectual ability. Average annual prevalence estimates from 1991–2010 underscore the significant community resources needed to provide early intervention and ongoing supports for children with ID (13.0 per 1,000), CP, (3.5 per 1,000), HL (1.4 per 1,000) and VI (1.3 in 1,000), with a growing urgency for children with ASD.     

The Potential of Accelerating Early Detection of Autism through Content Analysis of YouTube Videos

Autism is on the rise, with 1 in 88 children receiving a diagnosis in the United States, yet the process for diagnosis remains cumbersome and time consuming. Research has shown that home videos of children can help increase the accuracy of diagnosis. However the use of videos in the diagnostic process is uncommon. In the present study, we assessed the feasibility of applying a gold-standard diagnostic instrument to brief and unstructured home videos and tested whether video analysis can enable more rapid detection of the core features of autism outside of clinical environments. We collected 100 public videos from YouTube of children ages 1–15 with either a self-reported diagnosis of an ASD (N = 45) or not (N = 55). Four non-clinical raters independently scored all videos using one of the most widely adopted tools for behavioral diagnosis of autism, the Autism Diagnostic Observation Schedule-Generic (ADOS). The classification accuracy was 96.8%, with 94.1% sensitivity and 100% specificity, the inter-rater correlation for the behavioral domains on the ADOS was 0.88, and the diagnoses matched a trained clinician in all but 3 of 22 randomly selected video cases. Despite the diversity of videos and non-clinical raters, our results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel. Our results also demonstrate the potential for video-based detection of autism in short, unstructured home videos and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments.     

Deficit in visual temporal integration in autism spectrum disorders

Individuals with autism spectrum disorders (ASD) are superior in processing local features. Frith and Happe conceptualize this cognitive bias as ‘weak central coherence’, implying that a local enhancement derives from a weakness in integrating local elements into a coherent whole. The suggested deficit has been challenged, however, because individuals with ASD were not found to be inferior to normal controls in holistic perception. In these opposing studies, however, subjects were encouraged to ignore local features and attend to the whole. Therefore, no one has directly tested whether individuals with ASD are able to integrate local elements over time into a whole image. Here, we report a weakness of individuals with ASD in naming familiar objects moved behind a narrow slit, which was worsened by the absence of local salient features. The results indicate that individuals with ASD have a clear deficit in integrating local visual information over time into a global whole, providing direct evidence for the weak central coherence hypothesis.     

The Imaginary Part of Coherency in Autism: Differences in Cortical Functional Connectivity in Preschool Children

Cognition arises from the transient integration and segregation of activity across functionally distinct brain areas. Autism Spectrum Disorders (ASD), which encompass a wide range of developmental disabilities, have been presumed to be associated with a problem in cortical and sub-cortical dynamics of coordinated activity, often involving enhanced local but decreased long range coordination over areas of integration. In this paper we challenge this idea by presenting results from a relatively large population of ASD children and age-matched controls during a face-processing task. Over most of the explored domain, children with ASD exhibited enhanced synchronization, although finer detail reveals specific enhancement/reduction of synchrony depending on time, frequency and brain site. Our results are derived from the use of the imaginary part of coherency, a measure which is not susceptible to volume conduction artifacts and therefore presents a credible picture of coordinated brain activity. We also present evidence that this measure is a good candidate to provide features in building a classifier to be used as a potential biomarker for autism.     

It’s All in the Eyes: Subcortical and Cortical Activation during Grotesqueness Perception in Autism

Atypical face processing plays a key role in social interaction difficulties encountered by individuals with autism. In the current fMRI study, the Thatcher illusion was used to investigate several aspects of face processing in 20 young adults with high-functioning autism spectrum disorder (ASD) and 20 matched neurotypical controls. “Thatcherized” stimuli were modified at either the eyes or the mouth and participants discriminated between pairs of faces while cued to attend to either of these features in upright and inverted orientation. Behavioral data confirmed sensitivity to the illusion and intact configural processing in ASD. Directing attention towards the eyes vs. the mouth in upright faces in ASD led to (1) improved discrimination accuracy; (2) increased activation in areas involved in social and emotional processing; (3) increased activation in subcortical face-processing areas. Our findings show that when explicitly cued to attend to the eyes, activation of cortical areas involved in face processing, including its social and emotional aspects, can be enhanced in autism. This suggests that impairments in face processing in autism may be caused by a deficit in social attention, and that giving specific cues to attend to the eye-region when performing behavioral therapies aimed at improving social skills may result in a better outcome.     

Opposite risk patterns for autism and schizophrenia are associated with normal variation in birth size: phenotypic support for hypothesized diametric gene-dosage effects

Opposite phenotypic and behavioural traits associated with copy number variation and disruptions to imprinted genes with parent-of-origin effects have led to the hypothesis that autism and schizophrenia share molecular risk factors and pathogenic mechanisms, but a direct phenotypic comparison of how their risks covary has not been attempted. Here, we use health registry data collected on Denmark''s roughly 5 million residents between 1978 and 2009 to detect opposing risks of autism and schizophrenia depending on normal variation (mean ± 1 s.d.) in adjusted birth size, which we use as a proxy for diametric gene-dosage variation in utero. Above-average-sized babies (weight, 3691–4090 g; length, 52.8–54.3 cm) had significantly higher risk for autism spectrum (AS) and significantly lower risk for schizophrenia spectrum (SS) disorders. By contrast, below-average-sized babies (2891–3290 g; 49.7–51.2 cm) had significantly lower risk for AS and significantly higher risk for SS disorders. This is the first study directly comparing autism and schizophrenia risks in the same population, and provides the first large-scale empirical support for the hypothesis that diametric gene-dosage effects contribute to these disorders. Only the kinship theory of genomic imprinting predicts the opposing risk patterns that we discovered, suggesting that molecular research on mental disease risk would benefit from considering evolutionary theory.     

Co-expression Profiling of Autism Genes in the Mouse Brain

Autism spectrum disorder (ASD) is one of the most prevalent and highly heritable neurodevelopmental disorders in humans. There is significant evidence that the onset and severity of ASD is governed in part by complex genetic mechanisms affecting the normal development of the brain. To date, a number of genes have been associated with ASD. However, the temporal and spatial co-expression of these genes in the brain remain unclear. To address this issue, we examined the co-expression network of 26 autism genes from AutDB (http://mindspec.org/autdb.html), in the framework of 3,041 genes whose expression energies have the highest correlation between the coronal and sagittal images from the Allen Mouse Brain Atlas database (http://mouse.brain-map.org). These data were derived from in situ hybridization experiments conducted on male, 56-day old C57BL/6J mice co-registered to the Allen Reference Atlas, and were used to generate a normalized co-expression matrix indicating the cosine similarity between expression vectors of genes in this database. The network formed by the autism-associated genes showed a higher degree of co-expression connectivity than seen for the other genes in this dataset (Kolmogorov–Smirnov P = 5×10⁻²⁸). Using Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly enriched with autism genes (A Bonferroni corrected P<0.05). Genes in both these cliques were significantly over-expressed in the cerebellar cortex (P = 1×10⁻⁵) suggesting possible implication of this brain region in autism. In conclusion, our study provides a detailed profiling of co-expression patterns of autism genes in the mouse brain, and suggests specific brain regions and new candidate genes that could be involved in autism etiology.     

Incidence and predictors of morphometric vertebral fractures in patients with ankylosing spondylitis

Introduction

Ankylosing spondylitis (AS) is associated with an increased incidence of vertebral fractures (VFs); however the actual incidence and predictors of morphometric VFs are unknown. The present study examined the incidence and predictors of new VFs in a large AS cohort.

Methods

In total, 298 AS patients who fulfilled the modified New York criteria were enrolled and spinal radiographs were evaluated biennially. Clinical and laboratory data and radiographic progression were assessed according to the Bath AS Disease Activity Index, erythrocyte sedimentation rate, C-reactive protein (CRP), and the Stoke AS spine score (SASSS). VF was defined according to the Genant criteria. The incidence of VFs at 2 and 4 years was evaluated using the Kaplan-Meier method. The age-specific standardized prevalence ratio (SPR) for AS patients in comparison with the general population was calculated.

Results

Of 298 patients, 31 (10.8%) had previous VFs at baseline. A total of 30 new VFs occurred in 26 patients over 4 years. The incidence of morphometric VFs was 4.7% at 2 years and 13.6% at 4 years. Multivariate logistic regression analysis showed that previous VFs at baseline and increased CRP levels at 2 years were predictors of new VFs (odds ratio (OR) =12.8, 95% confidence interval (CI) = 3.6-45.3 and OR = 5.4, 95% CI = 1.4–15.9). The age-specific specific standardized prevalence ratio of morphometric VFs in AS was 3.3 (95% CI 2.1–4.5).

Conclusions

The incidence of morphometric VFs increased in AS. Previous VFs and increased CRP levels predicted future VFs. Further studies are needed to identify the effects of treatment interventions on the prevention of new VFs.     

Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.     

High resolution metabolomic analysis of ASD human brain uncovers novel biomarkers of disease

Introduction

Autism spectrum disorders (ASD) is a group of neurodevelopmental disorders believed to have a multifactorial basis. Presently, diagnosis is based on behavioral and developmental signs in children before the age of 3 and no reliable clinical biomarkers are available for early detection.

Objectives

This study aimed to biochemically profile the cerebellum from post-mortem human brain from ASD sufferers (n = 11) and compare their profiles to that of age-matched controls (n = 11) with no known brain disorder.

Methods

Using liquid chromatography combined with LTQ-Orbitrap mass spectrometry we detected 14,328 features in ESI+ mode in polar extracts of post-mortem brain.

Results

Of these only 37 were found to be statistically significantly different between ASD and controls (p < 0.05; fdr < 0.05). A panel of four features had a predictive power of 96.64 %, following statistical cross validation, for ASD detection. This model produced an AUC = 0.874 (CI 0.768–0.944) and a Fisher’s exact score of p = 4.50E?29.

Conclusion

Whilst at this time we were unable to chemically identify the four features of interest we believe that this study underscores the potential value of high resolution metabolomics for the study of ASD. Further characterization of the polar metabolome of post mortem ASD brains could lead to the identification of potential biomarkers and novel therapeutics for the disease. The development of accurate biomarkers could assist in the early detection of ASD and promote early intervention strategies to improve outcome.

     

DUF1220 Dosage Is Linearly Associated with Increasing Severity of the Three Primary Symptoms of Autism

One of the three most frequently documented copy number variations associated with autism spectrum disorder (ASD) is a 1q21.1 duplication that encompasses sequences encoding DUF1220 protein domains, the dosage of which we previously implicated in increased human brain size. Further, individuals with ASD frequently display accelerated brain growth and a larger brain size that is also associated with increased symptom severity. Given these findings, we investigated the relationship between DUF1220 copy number and ASD severity, and here show that in individuals with ASD (n = 170), the copy number (dosage) of DUF1220 subtype CON1 is highly variable, ranging from 56 to 88 copies following a Gaussian distribution. More remarkably, in individuals with ASD CON1 copy number is also linearly associated, in a dose-response manner, with increased severity of each of the three primary symptoms of ASD: social deficits (p = 0.021), communicative impairments (p = 0.030), and repetitive behaviors (p = 0.047). These data indicate that DUF1220 protein domain (CON1) dosage has an ASD-wide effect and, as such, is likely to be a key component of a major pathway underlying ASD severity. Finally, these findings, by implicating the dosage of a previously unexamined, copy number polymorphic and brain evolution-related gene coding sequence in ASD severity, provide an important new direction for further research into the genetic factors underlying ASD.     

Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders

We employed a multi-scale clustering methodology known as “data cloud geometry” to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both “fine” and “coarse” scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of and specificity of . Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.     

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.