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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.

Surface area expansion from 6–12 months precedes brain overgrowth in high risk infants diagnosed with autism at 24 months and cortical features in the first year predict individual diagnostic outcomes. Early brain overgrowth predicts autism spectrum disorder Autism spectrum disorder (ASD) is associated with brain overgrowth, but it has been unclear how this relates to behavioural symptoms. In a longitudinal neuroimaging study of young children at high familial risk of autism, Heather Hazlett and colleagues now show that high-risk children who receive a diagnosis of ASD at 24 months of age had an increased cortical growth rate at 6–12 months. Early overgrowth in high-risk children is associated with social impairments at 24 months, and imaging data obtained at 6 and 12 months can predict an ASD diagnosis at 24 months in high-risk children. These findings indicate that differences in the developmental trajectory towards ASD emerge as early as the first year of life. Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development 1 , 2 . Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life 3 , 4 . These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6–12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

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.

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.

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.

We assessed anxiety consistent (i.e., “traditional”) and inconsistent (i.e., “atypical”) with diagnostic and statistical manual (DSM) definitions in autism spectrum disorder (ASD). Differential relationships between traditional anxiety, atypical anxiety, child characteristics, anxiety predictors and ASD-symptomology were explored. Fifty-nine participants (7–17 years, M age  = 10.48 years; IQ > 60) with ASD and parents completed semi-structured interviews, self- and parent-reports. Seventeen percent of youth presented with traditional anxiety, 15 % with atypical anxiety, and 31 % with both. Language ability, anxious cognitions and hypersensitivity predicted traditional anxiety, whereas traditional anxiety and ASD symptoms predicted atypical anxiety. Findings suggest youth with ASD express anxiety in ways similar and dissimilar to DSM definitions. Similarities support the presence of comorbid anxiety disorders in ASD. Whether dissimilarities are unique to ASD requires further examination.

An under-explored issue in teacher education is the active documentation of pedagogical usage after initial training and practice implementation have been completed. In this phenomenological study, the researcher contacted previous graduate students who earned a teaching-endorsement credential for working with Gifted/Talented learners to explore the application of a pedagogical method developed specifically to shift from teacher-directed to student-engaged pedagogy. Common impediments to pedagogical change are described and discussed. Participants did overcome impediments, changing their pedagogical approach from teacher-directed to student-engaged approaches over time. Each shared their particular circumstances for needing to change their teaching approach. But in all instances, student performance and outcomes drove and supported the continued expansion of the change process. Future empirical work in teacher development and in-service pedagogical change based on results is suggested to further this line of inquiry.

This study investigated the extent to which sensory responsivity in infancy contributes to adaptive behavior development among toddlers at high-familial likelihood for autism. Prospective, longitudinal data were analyzed for 218 children, 58 of whom received an autism diagnosis. Results indicated that sensory profiles at age one year (hyperresponsivity, sensory seeking) were negatively associated with later adaptive behavior, particularly for socialization, at age 3 years regardless of diagnostic status. These results suggest that early differences in sensory responsivity may have downstream developmental consequences related to social development among young children with high-familial likelihood for autism.

Background Neurobiological research in autism spectrum disorders (ASD) has paid little attention on brain mechanisms that cause and maintain restricted and repetitive behaviors and interests (RRBIs). Evidence indicates an imbalance in the brain’s reward system responsiveness to social and non-social stimuli may contribute to both social deficits and RRBIs. Thus, this study’s central aim was to compare brain responsiveness to individual RRBI (i.e., circumscribed interests), with social rewards (i.e., social approval), in youth with ASD relative to typically developing controls (TDCs). Methods We conducted a 3T functional magnetic resonance imaging (fMRI) study to investigate the blood-oxygenation-level-dependent effect of personalized circumscribed interest rewards versus social rewards in 39 youth with ASD relative to 22 TDC. To probe the reward system, we employed short video clips as reinforcement in an instrumental incentive delay task. This optimization increased the task’s ecological validity compared to still pictures that are often used in this line of research. Results Compared to TDCs, youth with ASD had stronger reward system responses for CIs mostly within the non-social realm (e.g., video games) than social rewards (e.g., approval). Additionally, this imbalance within the caudate nucleus’ responsiveness was related to greater social impairment. Conclusions The current data support the idea of reward system dysfunction that may contribute to enhanced motivation for RRBIs in ASD, accompanied by diminished motivation for social engagement. If a dysregulated reward system indeed supports the emergence and maintenance of social and non-social symptoms of ASD, then strategically targeting the reward system in future treatment endeavors may allow for more efficacious treatment practices that help improve outcomes for individuals with ASD and their families.