Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data
The current study examined whether machine learning features best distinguishing attention-
deficit/hyperactivity disorder (ADHD) from typically developing children (TDC) can explain …
deficit/hyperactivity disorder (ADHD) from typically developing children (TDC) can explain …
[HTML][HTML] Insights into multimodal imaging classification of ADHD
Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by
clinicians via subjective ADHD-specific behavioral instruments and by reports from the …
clinicians via subjective ADHD-specific behavioral instruments and by reports from the …
[HTML][HTML] Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children
H Lin, SP Haider, S Kaltenhauser, A Mozayan… - Frontiers in …, 2023 - frontiersin.org
Objectives Leveraging a large population-level morphologic, microstructural, and functional
neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit …
neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit …
[HTML][HTML] Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic …
Objective Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder,
but diagnosed by subjective clinical and rating measures. The study's aim was to apply …
but diagnosed by subjective clinical and rating measures. The study's aim was to apply …
ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework
An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity
disorder (ADHD) without manual intervention. The present study emphasises utilizing …
disorder (ADHD) without manual intervention. The present study emphasises utilizing …
[HTML][HTML] Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …
[HTML][HTML] Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder
Objectives Neuroimaging studies have independently demonstrated brain anatomical and
functional impairments in participants with ADHD. The aim of the current study was to …
functional impairments in participants with ADHD. The aim of the current study was to …
[HTML][HTML] Evaluation of pattern recognition and feature extraction methods in ADHD prediction
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being
one of the most prevalent psychiatric disorders in childhood. The neural substrates …
one of the most prevalent psychiatric disorders in childhood. The neural substrates …
Multimodal structural neuroimaging markers of brain development and ADHD symptoms
Objective: Attention deficit hyperactivity disorder (ADHD) is a multifactorial disorder with
diverse associated risk factors and comorbidities. In this study, the authors sought to …
diverse associated risk factors and comorbidities. In this study, the authors sought to …
Machine learning classification of attention-deficit/hyperactivity disorder using structural MRI data
Background Clinical symptoms-based ADHD diagnosis is considered “subjective”. Machine
learning (ML) classifiers have been explored to develop objective diagnosis of ADHD using …
learning (ML) classifiers have been explored to develop objective diagnosis of ADHD using …