Autism

MCBERT: A multi-modal framework for the diagnosis of autism spectrum disorder.

Within the domain of neurodevelopmental disorders, autism spectrum disorder (ASD) emerges as a distinctive neurological condition characterized by multifaceted challenges. The delayed identification of ASD poses a considerable hurdle in effectively managing its impact and mitigating its severity. Addressing these complexities requires a nuanced understanding of data modalities and the underlying p...

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.

Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. T...

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier metho...

The constrained-disorder principle defines the functions of systems in nature.

The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, the intrinsic variability...

Identification of autism spectrum disorder using electroencephalography and machine learning: a review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by communication barriers, societal disengagement, and monotonous actio...

Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.

Alzheimer's disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along wi...

Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy.

OBJECTIVES: Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnet...

A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics.

Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many effo...

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification.

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitiv...

Optimization of extraction in supercritical fluids in obtaining seed oil by response surface methodology and artificial neuronal network coupled with a genetic algorithm.

When processing lucuma (), waste such as shells and seeds is generated, which is a source of bioactive compounds. Recently, lucuma seed (LS), especial...

Limbic/paralimbic connection weakening in preschool autism-spectrum disorder based on diffusion basis spectrum imaging.

This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and nor...

Unraveling pathogenesis and potential biomarkers for autism spectrum disorder associated with HIF1A pathway based on machine learning and experiment validation.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a high social burden and limited treatments. Hypoxic condition of the...

AI-enabled clinical decision support tools for mental healthcare: A product review.

The review seeks to promote transparency in the availability of regulated AI-enabled Clinical Decision Support Systems (AI-CDSS) for mental healthcare...

Editorial: Improving autism spectrum disorder diagnosis using machine learning techniques.

A pilot evaluation of school-based LEGO® robotics therapy for autistic students.

There is emerging evidence that LEGO® therapy is an effective way of supporting younger autistic children develop their communication and social skill...

Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.

This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and...

Using deep learning to classify developmental differences in reaching and placing movements in children with and without autism spectrum disorder.

Autism Spectrum Disorder (ASD) is among the most prevalent neurodevelopmental disorders, yet the current diagnostic procedures rely on behavioral anal...

Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often ...

Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.

Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individual...