AIMC Topic: Autism Spectrum Disorder

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Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder.

Sensors (Basel, Switzerland)
With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can di...

Predicting brain age with complex networks: From adolescence to adulthood.

NeuroImage
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brai...

Ordered interpersonal synchronisation in ASD children via robots.

Scientific reports
Children with autistic spectrum disorders (ASD) experience persistent disrupted coordination in interpersonal synchronisation that is thought to be associated with deficits in neural connectivity. Robotic interventions have been explored for use with...

The study of the differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication and stereotyped behavior. Unlike typicall...

Optimal robot for intervention for individuals with autism spectrum disorders.

Psychiatry and clinical neurosciences
With recent rapid advances in technology, human-like robots have begun functioning in a variety of ways. As increasing anecdotal evidence suggests, robots may offer many unique opportunities for helping individuals with autism spectrum disorders (ASD...

The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy.

PloS one
We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and...

Self-initiations in young children with autism during Pivotal Response Treatment with and without robot assistance.

Autism : the international journal of research and practice
The initiation of social interaction is often defined as a core deficit of autism spectrum disorder. Optimizing these self-initiations is therefore a key component of Pivotal Response Treatment, an established intervention for children with autism sp...

Robot applications for autism: a comprehensive review.

Disability and rehabilitation. Assistive technology
PURPOSE: Technological advances in robotics have brought about exciting developments in different areas such as education, training, and therapy. Recent research has suggested that the robot can be even more effective in rehabilitation, therapy, and ...

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Computational and mathematical methods in medicine
Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specif...

Adherence and acceptability of a robot-assisted Pivotal Response Treatment protocol for children with autism spectrum disorder.

Scientific reports
The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD intervention research and robotics research, and critically evaluate its adherence and acceptability based o...