Individualized prediction models in ADHD: a systematic review and meta-regression
There have been increasing efforts to develop prediction models supporting personalised
detection, prediction, or treatment of ADHD. We overviewed the current status of prediction …
detection, prediction, or treatment of ADHD. We overviewed the current status of prediction …
Machine learning and MRI-based diagnostic models for ADHD: are we there yet?
Y Zhang-James, AS Razavi… - Journal of Attention …, 2023 - journals.sagepub.com
Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging
(MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This …
(MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This …
TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights
J Anbarasi, R Kumari, M Ganesh, R Agrawal - BMC neurology, 2024 - Springer
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly
intricate and organized networks of neurons. The advent of neuroimaging has led to …
intricate and organized networks of neurons. The advent of neuroimaging has led to …
Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in
attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has …
attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has …
ADHD classification combining biomarker detection with attention auto-encoding neural network
Y Chen, Y Gao, A Jiang, Y Tang, C Wang - Biomedical Signal Processing …, 2023 - Elsevier
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is one of most prevalent
neurodevelopmental disorders in children. In decades, various neurobiological diagnosis …
neurodevelopmental disorders in children. In decades, various neurobiological diagnosis …
[HTML][HTML] Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain …
Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for
various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be …
various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be …
TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
the brain's structure relates to cognitive function. However, the accuracy of prediction using …
DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants
Abstract Background and Objective Very preterm infants are susceptible to
neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for …
neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for …
Applications of deep learning to neurodevelopment in pediatric imaging: Achievements and challenges
Deep learning has achieved remarkable progress, particularly in neuroimaging analysis.
Deep learning applications have also been extended from adult to pediatric medical images …
Deep learning applications have also been extended from adult to pediatric medical images …