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Autism Spectrum Disorder

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A hybrid CNN-SVM model for enhanced autism diagnosis.

PloS one
Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual's behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to...

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Interdisciplinary sciences, computational life sciences
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting th...

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in...

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

NeuroImage
Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms...

Graph Node Classification to Predict Autism Risk in Genes.

Genes
This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome...

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Medical & biological engineering & computing
Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach in...

Assisted Robots in Therapies for Children with Autism in Early Childhood.

Sensors (Basel, Switzerland)
Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). ...

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Journal of autism and developmental disorders
PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected indivi...

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Reviews in the neurosciences
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides ...