Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept
Related Experiment Video
Updated: Dec 16, 2025
Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging
Published on: April 11, 2025
Cyrus A Raji1,2, Maxwell B Wang3, NhuNhu Nguyen3
1Neural Connectivity Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA, 94158, USA. cyrusraji@gmail.com.
Insights
Edge density imaging effectively distinguishes pediatric mild traumatic brain injury (TBI) from controls. This novel connectome mapping technique outperforms traditional neuroimaging and cognitive tests in diagnosing mild TBI in adolescents.
Area of Science:
- Neuroimaging
- Connectomics
- Pediatric Neurology
Background:
- Mild traumatic brain injury (TBI) in children can cause long-term effects, but conventional CT and MR imaging often show no abnormalities.
- Diagnosing pediatric mild TBI is challenging due to the lack of visible abnormalities on standard imaging.
- Identifying reliable biomarkers for pediatric mild TBI is crucial for timely intervention and management.
Purpose of the Study:
- To evaluate the efficacy of edge density imaging in differentiating pediatric mild TBI from typically developing controls.
- To compare the diagnostic performance of edge density imaging against diffusion tensor imaging (DTI) and neurocognitive assessments.
Main Methods:
- Utilized diffusion MR imaging on a 3-tesla scanner in 24 adolescents (14 with mild TBI, 10 controls).
- Computed edge density images using fiber tractography and analyzed with principal component analysis (PCA) and support vector machines (SVM).
- Assessed diagnostic accuracy using receiver operating characteristic (ROC) metrics and compared with neurocognitive tests (CVLT, ANT) and fractional anisotropy (FA) from DTI.
Main Results:
- Edge density imaging, analyzed by PCA and SVM, identified three white matter regions (tapetum, sagittal stratum, callosal splenium) that distinguished mild TBI from controls.
- Sensitivity was 79% and specificity was 100% for identifying mild TBI subjects.
- The area under the ROC curve (AUC) for edge density imaging was 94%, significantly outperforming neurocognitive tests (AUC 61-71%) and FA from DTI (AUC 48%).
Conclusions:
- Edge density imaging represents a novel connectome mapping approach for diagnosing pediatric mild TBI.
- This technique offers superior diagnostic accuracy compared to DTI and standard neurocognitive assessments.
- Edge density imaging shows promise as a sensitive and specific tool for identifying mild TBI in pediatric populations.
Background:
Although 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.
Objective:
To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls.
Materials And Methods:
Subjects 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.
Results:
Support 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%.
Conclusion:
In 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.
