Trial reportScientific reports2018
Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.
Trial report in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 3 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
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Characterizing the relationship between functional network dynamics and the body mass index.Frontiers in nutrition · 2026Article
- Reconfiguration of Functional Brain Network Organization and Dynamics With Changing Cognitive Demands in Children With Attention-Deficit/Hyperactivity Disorder.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Time-resolved instantaneous functional loci estimation (TRIFLE): Estimating time-varying allocation of spatially overlapping sources in functional magnetic resonance imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.Frontiers in child and adolescent psychiatry · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.BMC psychiatry · 2024Observational
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis.Human brain mapping · 2023Article
- Altered neural flexibility in children with attention-deficit/hyperactivity disorder.Molecular psychiatry · 2022Article
- Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.PLoS biology · 2022Article
- Catecholaminergic and cholinergic neuromodulation in autism spectrum disorder: A comparison to attention-deficit hyperactivity disorder.Frontiers in neuroscience · 2022Review
- Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control.Developmental cognitive neuroscience · 2021Article
- Article
- A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning.Frontiers in genetics · 2021Article
- Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network.Frontiers in systems neuroscience · 2021Article
- Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach.Brain sciences · 2020Article
- Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.Soa--ch'ongsonyon chongsin uihak = Journal of child & adolescent psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear. Towards this end, 100 children with ADHD and 140 normal controls were obtained from the ADHD-200 Consortium. The raw features were derived from the temporal variability between intrinsic connectivity networks (ICNs) as well as the demographic and covariate variables. The diagnostic model was based on the support vector machines (SVMs). The performance of diagnostic model was analyzed using leave-one-out cross-validation (LOOCV) and 10-folds cross-validations (CVs). The diagnostic model based on inter-ICN variability outperformed that based on inter-ICN functional connectivity and inter-ICN phase synchrony. The LOOCV achieved total accuracy of 78.75%, the sensitivity of 76%, and the specificity of 80.71%. The 10-folds CVs achieved average prediction accuracy of 75.54% ± 1.34%, average sensitivity of 70.5% ± 2.34%, and average specificity of 77.44% ± 1.47%. In addition, the discriminative patterns for ADHD were discovered using SVMs. The discriminative patterns confirmed with previous findings. In summary, individuals with ADHD could be identified through inter-ICN variability, which could be potential biomarkers for diagnostic model of ADHD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.