The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.

Dimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.

Objective: Although no longer required for a diagnosis, language delays are extremely common in children diagnosed with autism spectrum disorders (ASD). Factors associated with socioeconomic status (SES) have broad-reaching impact on language development in early childhood. Despite recent advances in characterizing autism in early childhood, the relationship between SES and language development in ASD has not received much attention. The objective of this study was to examine whether toddlers and preschoolers with ASD from low-resource families are more likely to experience language delays above and beyond those associated with autism itself.Methods: Developmental and diagnostic assessments including the Mullen Scales of Early Learning, the Autism Diagnostic Observation Schedule, Second Edition, and the Vineland Adaptive Behavior Scales were obtained from 62 young children with ASD and 45 typically developing children aged 15 to 64 months. Sociodemographic information including household income, maternal education, and racial/ethnic identity was obtained from caregivers. Multiple regression models were used to test for associations between socioeconomic indices and language scores.Results: Maternal education accounted for variability in expressive language (EL) and receptive language (RL), with lower SES indices associated with lower language skills, and more so in children with ASD. Conclusion:These results demonstrate that variability in EL and RL skills in young children with autism can be accounted for by socioeconomic variables. These findings highlight the necessity for targeted intervention and effective implementation strategies for children with ASD from low-resource households and communities and for policies designed to improve learning opportunities and access to services for these young children and their families.

Background Symptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood. Methods Resting‐state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17–45 months. Intrinsic functional connectivity (iFC) within and between resting‐state functional networks was derived with independent component analysis (ICA). Results Increased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age‐related weakening of the visual–auditory between‐network connectivity was observed in the ASD but not the TD group. Conclusions Taken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual–sensorimotor between‐network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

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