Autism is a severe developmental disorder marked by a triad of deficits, including impairments in reciprocal social interaction, delays in early language and communication, and the presence of ...restrictive, repetitive and stereotyped behaviors. In this review, it is argued that the search for the neurobiological bases of the autism spectrum disorders should focus on the social deficits, as they alone are specific to autism and they are likely to be most informative with respect to modeling the pathophysiology of the disorder. Many recent studies have documented the difficulties persons with an autism spectrum disorder have accurately perceiving facial identity and facial expressions. This behavioral literature on face perception abnormalities in autism is reviewed and integrated with the functional magnetic resonance imaging (fMRI) literature in this area, and a heuristic model of the pathophysiology of autism is presented. This model posits an early developmental failure in autism involving the amygdala, with a cascading influence on the development of cortical areas that mediate social perception in the visual domain, specifically the fusiform “face area” of the ventral temporal lobe. Moreover, there are now some provocative data to suggest that visual perceptual areas of the ventral temporal pathway are also involved in important ways in representations of the semantic attributes of people, social knowledge and social cognition. Social perception and social cognition are postulated as normally linked during development such that growth in social perceptual skills during childhood provides important scaffolding for social skill development. It is argued that the development of face perception and social cognitive skills are supported by the amygdala–fusiform system, and that deficits in this network are instrumental in causing autism.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
2.
Autism Levy, Susan E, Prof; Mandell, David S, ScD; Schultz, Robert T, Prof
Lancet,
11/2009, Volume:
374, Issue:
9701
Journal Article, Book Review
Peer reviewed
Open access
Summary Autism spectrum disorders are characterised by severe deficits in socialisation, communication, and repetitive or unusual behaviours. Increases over time in the frequency of these disorders ...(to present rates of about 60 cases per 10 000 children) might be attributable to factors such as new administrative classifications, policy and practice changes, and increased awareness. Surveillance and screening strategies for early identification could enable early treatment and improved outcomes. Autism spectrum disorders are highly genetic and multifactorial, with many risk factors acting together. Genes that affect synaptic maturation are implicated, resulting in neurobiological theories focusing on connectivity and neural effects of gene expression. Several treatments might address core and comorbid symptoms. However, not all treatments have been adequately studied. Improved strategies for early identification with phenotypic characteristics and biological markers (eg, electrophysiological changes) might hopefully improve effectiveness of treatment. Further knowledge about early identification, neurobiology of autism, effective treatments, and the effect of this disorder on families is needed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Universal screening is recommended to reduce the age of diagnosis for autism spectrum disorder (ASD). However, there are insufficient data on children who screen negative and no study of outcomes ...from truly universal screening. With this study, we filled these gaps by examining the accuracy of universal screening with systematic follow-up through 4 to 8 years.
Universal, primary care-based screening was conducted using the Modified Checklist for Autism in Toddlers with Follow-Up (M-CHAT/F) and supported by electronic administration and integration into electronic health records. All children with a well-child visit (1) between 16 and 26 months, (2) at a Children's Hospital of Philadelphia site after universal electronic screening was initiated, and (3) between January 2011 and July 2015 were included (
= 25 999).
Nearly universal screening was achieved (91%), and ASD prevalence was 2.2%. Overall, the M-CHAT/F's sensitivity was 38.8%, and its positive predictive value (PPV) was 14.6%. Sensitivity was higher in older toddlers and with repeated screenings, whereas PPV was lower in girls. Finally, the M-CHAT/F's specificity and PPV were lower in children of color and those from lower-income households.
Universal screening in primary care is possible when supported by electronic administration. In this "real-world" cohort that was systematically followed, the M-CHAT/F was less accurate in detecting ASD than in previous studies. Disparities in screening rates and accuracy were evident in traditionally underrepresented groups. Future research should focus on the development of new methods that detect a greater proportion of children with ASD and reduce disparities in the screening process.
The social motivation theory of autism Chevallier, Coralie; Kohls, Gregor; Troiani, Vanessa ...
Trends in cognitive sciences,
04/2012, Volume:
16, Issue:
4
Journal Article
Peer reviewed
Open access
The idea that social motivation deficits play a central role in Autism Spectrum Disorders (ASD) has recently gained increased interest. This constitutes a shift in autism research, which has ...traditionally focused more intensely on cognitive impairments, such as theory-of-mind deficits or executive dysfunction, and has granted comparatively less attention to motivational factors. This review delineates the concept of social motivation and capitalizes on recent findings in several research areas to provide an integrated account of social motivation at the behavioral, biological and evolutionary levels. We conclude that ASD can be construed as an extreme case of diminished social motivation and, as such, provides a powerful model to understand humans’ intrinsic drive to seek acceptance and avoid rejection.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, ...DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.
•Significant site and scanner effects exist in DTI scalar maps.•Several multi-site harmonization methods are proposed.•ComBat performs the best at removing site effects in FA and MD.•Voxels associated with age in FA and MD are more replicable after ComBat.•ComBat is generalizable to other imaging modalities.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early ...interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% 95% confidence interval (CI), 62.9 to 100, correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3). These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.
Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, ...might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.
► Three default mode sub-networks (DM-SNs) were identified from resting state fMRI scans of 16 patients with high-functioning autism spectrum disorders (ASD) and 16 matched healthy controls (HC) using independent component analysis (ICA). ► Compared to HC, patients with ASD showed decreased functional connectivity between the precuneus and medial prefrontal cortex/anterior cingulate cortex and other DM-SNs areas. ► FC magnitude in these regions inversely correlated with the severity of patients’ social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. ► Supplemental analyses suggest that these results were independent of treatment status.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
There is growing evidence of a camouflaging effect among females with autism spectrum disorder (ASD), particularly among those without intellectual disability, which may affect performance on ...gold-standard diagnostic measures. This study utilized an age- and IQ-matched sample of school-aged youth (
n
= 228) diagnosed with ASD to assess sex differences on the ADOS and ADI-R, parent-reported autistic traits, and adaptive skills. Although females and males were rated similarly on gold-standard diagnostic measures overall, females with higher IQs were less likely to meet criteria on the ADI-R. Females were also found to be significantly more impaired on parent reported autistic traits and adaptive skills. Overall, the findings suggest that some autistic females may be missed by current diagnostic procedures.
Full text
Available for:
DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
IMPORTANCE: The social motivation hypothesis posits that individuals with autism spectrum disorder (ASD) find social stimuli less rewarding than do people with neurotypical activity. However, ...functional magnetic resonance imaging (fMRI) studies of reward processing have yielded mixed results. OBJECTIVES: To examine whether individuals with ASD process rewarding stimuli differently than typically developing individuals (controls), whether differences are limited to social rewards, and whether contradictory findings in the literature might be due to sample characteristics. DATA SOURCES: Articles were identified in PubMed, Embase, and PsycINFO from database inception until June 1, 2017. Functional MRI data from these articles were provided by most authors. STUDY SELECTION: Publications were included that provided brain activation contrasts between a sample with ASD and controls on a reward task, determined by multiple reviewer consensus. DATA EXTRACTION AND SYNTHESIS: When fMRI data were not provided by authors, multiple reviewers extracted peak coordinates and effect sizes from articles to recreate statistical maps using seed-based d mapping software. Random-effects meta-analyses of responses to social, nonsocial, and restricted interest stimuli, as well as all of these domains together, were performed. Secondary analyses included meta-analyses of wanting and liking, meta-regression with age, and correlations with ASD severity. All procedures were conducted in accordance with Meta-analysis of Observational Studies in Epidemiology guidelines. MAIN OUTCOMES AND MEASURES: Brain activation differences between groups with ASD and typically developing controls while processing rewards. All analyses except the domain-general meta-analysis were planned before data collection. RESULTS: The meta-analysis included 13 studies (30 total fMRI contrasts) from 259 individuals with ASD and 246 controls. Autism spectrum disorder was associated with aberrant processing of both social and nonsocial rewards in striatal regions and increased activation in response to restricted interests (social reward, caudate cluster: d = −0.25 95% CI, −0.41 to −0.08; nonsocial reward, caudate and anterior cingulate cluster: d = −0.22 95% CI, −0.42 to −0.02; restricted interests, caudate and nucleus accumbens cluster: d = 0.42 95% CI, 0.07 to 0.78). CONCLUSIONS AND RELEVANCE: Individuals with ASD show atypical processing of social and nonsocial rewards. Findings support a broader interpretation of the social motivation hypothesis of ASD whereby general atypical reward processing encompasses social reward, nonsocial reward, and perhaps restricted interests. This meta-analysis also suggests that prior mixed results could be driven by sample age differences, warranting further study of the developmental trajectory for reward processing in ASD.
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of ...discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
PDF
