ArticlePediatric radiology2020
Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.
Article in Pediatric radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- The second decade of DTI in TBI part 1: a systematic review of mild TBI.Frontiers in neurologyPooled it
- Diagnostic and prognostic applications of machine learning in paediatric traumatic brain injury: a systematic review of single and multimodal approaches.Frontiers in neurologyPooled it
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Identifying obesity and dementia risk: body adiposity and neural connectivity in cognitively normal, mid-life adults.NPJ dementia · 2025Article
- Sex differences in pain perception and modulation in the brain: effects of insular cortex stimulation on chronic pain relief.Brain communications · 2025Article
- Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition.Aging and disease · 2024Article
- Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives.Biomedicines · 2024Review
- MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.Neuroinformatics · 2024Article
- Changes in the Networks of Attention across the Lifespan: A Graphical Meta-Analysis.Journal of Intelligence · 2024Review
- Classification accuracy of structural and functional connectomes across different depressive phenotypes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.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
- Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study.Brain communications · 2023Article
- The role of artificial intelligence in paediatric neuroradiology.Pediatric radiology · 2022Review
- Structural connectome differences in pediatric mild traumatic brain and orthopedic injury.Human brain mapping · 2022Article
- Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion.Frontiers in sports and active living · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundAlthough acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities.
objectiveTo determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND
methodsSubjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics.
resultsSupport vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%.
conclusionIn this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.
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