ArticleNeuropsychiatric disease and treatment2020
Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach.
Article in Neuropsychiatric disease and treatment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.Brain research bulletin · 2026Article
- Development of a multimodal magnetic resonance imaging-based machine learning prediction model for flight cadets.Scientific reports · 2026Article
- Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.Frontiers in human neuroscience · 2026Article
- Investigation of quantitative synthetic MRI in the evaluation of attention-deficit/hyperactivity disorder.Quantitative imaging in medicine and surgery · 2026Article
- Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.PloS one · 2025Article
- Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.Scientific reports · 2024Article
- Altered insular functional activity among electronic cigarettes users with nicotine dependence.Translational psychiatry · 2024Article
- Altered dynamic functional connectivity of insular subdivisions among male cigarette smokers.Frontiers in psychiatry · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis.Human brain mapping · 2023Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy.Human brain mapping · 2023Article
- Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study.Frontiers in neuroscience · 2023Article
- Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.Sensors (Basel, Switzerland) · 2022Article
- Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.Frontiers in neuroinformatics · 2022Article
- Aberrant Effective Connectivity of the Ventral Putamen in Boys With Attention-Deficit/Hyperactivity Disorder.Psychiatry investigation · 2021Article
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Authors and funding
5 authors.
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Abstract
purposeIn recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. PATIENTS AND
methodsWe measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests.
resultsThe results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (
conclusionThis study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.
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