Preserved but Un-Sustained Responses to Bids for Dyadic Engagement in School-Age Children with Autism.

Purpose: Dynamic eye-tracking paradigms are an engaging and increasingly used method to study social attention in autism. While prior research has focused primarily on younger populations, there is a need for developmentally appropriate tasks for older children.

Methods: This study introduces a novel eye-tracking task designed to assess school-aged children's attention to speakers involved in conversation.

Early- and Late-Stage Auditory Processing of Speech Versus Non-Speech Sounds in Children With Autism Spectrum Disorder: An ERP and Oscillatory Activity Study.

Individuals with autism spectrum disorder (ASD) often exhibit greater sensitivity to non-speech sounds, reduced sensitivity to speech, and increased variability in cortical activity during auditory speech processing. We assessed differences in cortical responses and variability in early and later processing stages of auditory speech versus non-speech sounds in typically developing (TD) children and children with ASD. Twenty-eight 4- to 9-year-old children (14 ASDs) listened to speech and non-speech sounds during an electroencephalography session.

Implementing Pivotal Response Treatment to Teach Question Asking to High School Students with Autism Spectrum Disorder.

The purpose of this study was to test the use of Pivotal Response Treatment (PRT) in the secondary school setting. There were two main goals: (a) to evaluate secondary education providers' ability to implement PRT with fidelity following a PRT training program; and (b) to evaluate the effects of school-implemented PRT on the social communication skills of adolescents and young adults with ASD, specifically, question-asking behavior. This concurrent multiple baseline design study across dyads investigated the use of PRT in the secondary school setting with adolescents with ASD.

The eTHINK Study: Cognitive and Behavioral Outcomes in Children with Hemophilia.

Objective: To assess cognitive, behavioral, and adaptive functions in children and young adults with hemophilia treated according to contemporary standards of care.

Study Design: Evolving Treatment of Hemophilia's Impact on Neurodevelopment, Intelligence, and Other Cognitive Functions (eTHINK) is a US-based, prospective, cross-sectional, observational study (September 2018 through October 2019). Males (aged 1-21 years) with hemophilia A or B of any severity, with or without inhibitors, were eligible.

Feasibility and Acceptability of Delivering Pivotal Response Treatment for Autism Spectrum Disorder via Telehealth: Pilot Pre-Post Study.

Background: Pivotal response treatment (PRT), an evidence-based and parent-delivered intervention, is designed to improve social communication in autistic individuals.

Objective: The aim of this study was to assess the feasibility, acceptability, and clinical effects of an online model of PRT delivered via MindNest Health, a telehealth platform that aims to provide self-directed and engaging online modules, real-time coaching and feedback, and accessible stepped-care to large populations of parents seeking resources for their autistic children.

Methods: Male and female autistic children, aged 2-7 years with single-word to phrase-level speech, and their parents were eligible to participate in the study.

Gaboxadol in Fragile X Syndrome: A 12-Week Randomized, Double-Blind, Parallel-Group, Phase 2a Study.

Fragile X syndrome (FXS), the most common single-gene cause of intellectual disability and autism spectrum disorder (ASD), is caused by a >200-trinucleotide repeat expansion in the 5' untranslated region of the fragile X mental retardation 1 () gene. Individuals with FXS can present with a range of neurobehavioral impairments including, but not limited to: cognitive, language, and adaptive deficits; ASD; anxiety; social withdrawal and avoidance; and aggression. Decreased expression of the γ-aminobutyric acid type A (GABA) receptor subunit and deficient GABAergic tonic inhibition could be associated with symptoms of FXS.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis.

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI.

Brief Report: Preliminary Evidence of the N170 as a Biomarker of Response to Treatment in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by primary difficulties in social function. Individuals with ASD display slowed neural processing of faces, as indexed by the latency of the N170, a face-sensitive event-related potential. Currently, there are no objective biomarkers of ASD useful in clinical care or research.

Functional Network Development in Sagittal Craniosynostosis Treated With Whole Vault Cranioplasty.

Introduction: In this study, the authors seek to clarify the neurological changes before and after whole vault cranioplasty (WVC) in patients born with sagittal craniosynostosis.

Methods: A case control study design was performed that included thirty functional MRI scans, from 25 individual patients. Functional MRI and diffusion tension imaging data were analyzed with BioImageSuite (Yale University, USA).

Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection.

Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g.

Graph Neural Network for Interpreting Task-fMRI Biomarkers.

Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e.

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site.

Invertible Network for Classification and Biomarker Selection for ASD.

Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions.

PREDICTION OF TREATMENT OUTCOME FOR AUTISM FROM STRUCTURE OF THE BRAIN BASED ON SURE INDEPENDENCE SCREENING.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is limited progress in understanding the effect of each type of treatment. In this project, we aim to detect structural changes in the brain after treatment and select structural features associated with treatment outcomes.

ESTIMATING REPRODUCIBLE FUNCTIONAL NETWORKS ASSOCIATED WITH TASK DYNAMICS USING UNSUPERVISED LSTMS.

We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.

Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty.

Complex deep learning models have shown their impressive power in analyzing high-dimensional medical image data. To increase the trust of applying deep learning models in medical field, it is essential to understand why a particular prediction was reached. Data feature importance estimation is an important approach to understand both the model and the underlying properties of data.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information.

Discrepancies between parent and child ratings of anxiety in children with autism spectrum disorder.

Co-occurring anxiety is common in children with autism spectrum disorder (ASD). However, inconsistencies across parent and child reports of anxiety may complicate the assessment of anxiety in this population. The present study examined parent and child anxiety ratings in children with ASD with and without anxiety disorders and tested the association between parent-child anxiety rating discrepancy and ASD symptom severity.

Altered Neural Connectivity in Females, But Not Males with Autism: Preliminary Evidence for the Female Protective Effect from a Quality-Controlled Diffusion Tensor Imaging Study.

Previous studies using diffusion tensor imaging (DTI) to investigate white matter (WM) structural connectivity have suggested widespread, although inconsistent WM alterations in autism spectrum disorder (ASD), such as greater reductions in fractional anisotropy (FA). However, findings may lack generalizability because: (a) most have focused solely on the ASD male brain phenotype, and not sex-differences in WM integrity; (b) many lack stringent and transparent data quality control such as controlling for head motion in analysis. This study addressed both issues by using Tract-Based Spatial Statistics (TBSS) to separately compare WM differences in 81 ASD (56 male, 25 female; 4-21 years old) and 39 typically developing (TD; 23 males, 16 females; 5-18 years old) children and young people, carefully group-matched on sex, age, cognitive abilities, and head motion.