[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Mental health is a basic need for a sustainable and developing society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …
financial burden of mental illness have increased globally, and especially in response to …
[HTML][HTML] Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review
In recent years, there has been a great interest in utilizing technology in mental health
research. The rapid technological development has encouraged researchers to apply …
research. The rapid technological development has encouraged researchers to apply …
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis
Purpose Recently, functional brain networks (FBN) have been used for the classification of
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals
Background The most prevalent neuropsychiatric disorder among children is attention deficit
hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders …
hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders …
Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and …
N Talebi, AM Nasrabadi - Computers in biology and medicine, 2022 - Elsevier
Background Analysis of effective connectivity among brain regions is an important key to
decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity …
decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity …
Synchronization in functional brain networks of children suffering from ADHD based on Hindmarsh-Rose neuronal model
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is one of the common psychological
disorders in children, which causes improper recognition of others' emotions. These children …
disorders in children, which causes improper recognition of others' emotions. These children …
Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis
S Liu, R Gui - Biomedical Signal Processing and Control, 2024 - Elsevier
Depression stands as one of the most pernicious mental disorders in contemporary society,
characterized by a highly intricate pathological mechanism. Specifically, individuals …
characterized by a highly intricate pathological mechanism. Specifically, individuals …
Functional brain network based multi-domain feature fusion of hearing-Impaired EEG emotion identification
J Wang, Y Song, Q Gao, Z Mao - Biomedical Signal Processing and Control, 2023 - Elsevier
In the past, Electroencephalography (EEG)-based emotion identification research mainly
focused on healthy, disordered, or depressed subjects. Due to the lack of an emotion …
focused on healthy, disordered, or depressed subjects. Due to the lack of an emotion …
[HTML][HTML] Low-intensity transcranial ultrasound stimulation improves memory behavior in an ADHD rat model by modulating cortical functional network connectivity
M Wang, Z Xie, T Wang, S Dong, Z Ma, X Zhang, X Li… - NeuroImage, 2024 - Elsevier
Working memory in attention deficit hyperactivity disorder (ADHD) is closely related to
cortical functional network connectivity (CFNC), such as abnormal connections between the …
cortical functional network connectivity (CFNC), such as abnormal connections between the …
[HTML][HTML] Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy
Background: EEG signals are commonly used in ADHD diagnosis, but they are often
affected by noise and artifacts. Effective preprocessing and segmentation methods can …
affected by noise and artifacts. Effective preprocessing and segmentation methods can …