Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept

Abstract

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.

Keywords: Brain; Children; Concussion; Diffusion tensor imaging; Edge density imaging; Magnetic resonance imaging; Traumatic brain injury.

Fig. 1

Edge density imaging map shows specific white matter regions of group results (arrows) that distinguish children with mild traumatic brain injury (TBI) from controls, projected onto the standard single-subject Montreal Neurological Institute (MNI) brain template [38]. (Table 1 details participant demographics.) In this figure, light blue regions are associated with higher edge density in controls (mean age 14.2 years, 50% female) compared to those with mild TBI (mean age 14.2 years, 36% female). Dark blue regions are associated with the opposite. The bilateral tapetum, sagittal stratum, and the splenium of the corpus callosum have higher edge densities in controls compared to children with mild TBI, and these differences are the most predictive features for accurate classification

Fig. 2

Receiver operating characteristic (ROC) curve for support vector machine (SVM)–principal component analysis (PCA) predictors in differentiating pediatric mild traumatic brain injury (TBI) cases from controls. SVM and PCA were used to generate a “TBI predictor” under a leave-one-out cross-validation, and the ROC curves based on these predictors are plotted here. For edge density imaging, the area under the curve (AUC) was 94%. Fractional anisotropy (FA) values resulted in an AUC of 48% in distinguishing children with mild TBI from controls. Neurocognitive testing yielded an AUC of distinguishing children with mild TBI from controls ranging from 61% with the Attention Network Task (ANT) to 71% with the California Verbal Learning Test (CVLT). No statistically significant correlations were observed between neurocognitive test results and either the diffusion tensor imaging (DTI) scalars or the edge density imaging maps

Fig. 3

Individual control and mild traumatic brain injury (TBI) cases and their distances from the support vector machine hyperplane (gray line). A positive distance in this figure is noted with controls, and a negative distance with mild TBI cases. EDI edge density imaging, SVM-PCA support vector machine–principal component analysis