Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.
Adolescent
Anisotropy
Brain Injuries, Traumatic
/ diagnostic imaging
Case-Control Studies
Child
Connectome
Diffusion Magnetic Resonance Imaging
Female
Humans
Male
Mental Status and Dementia Tests
Neuroimaging
/ methods
Principal Component Analysis
Proof of Concept Study
Prospective Studies
Severity of Illness Index
Support Vector Machine
Tomography, X-Ray Computed
Brain
Children
Concussion
Diffusion tensor imaging
Edge density imaging
Magnetic resonance imaging
Traumatic brain injury
Journal
Pediatric radiology
ISSN: 1432-1998
Titre abrégé: Pediatr Radiol
Pays: Germany
ID NLM: 0365332
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
19
12
2019
accepted:
24
05
2020
revised:
26
04
2020
pubmed:
2
7
2020
medline:
16
6
2021
entrez:
2
7
2020
Statut:
ppublish
Résumé
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. To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. 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. 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%. 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.
Sections du résumé
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.
Identifiants
pubmed: 32607611
doi: 10.1007/s00247-020-04743-9
pii: 10.1007/s00247-020-04743-9
pmc: PMC7501221
mid: NIHMS1608523
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1594-1601Subventions
Organisme : NCATS NIH HHS
ID : KL2 TR002346
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS060776
Pays : United States
Organisme : NIBIB NIH HHS
ID : T32 EB001631
Pays : United States
Organisme : NIH HHS
ID : KL2 TR000450
Pays : United States
Organisme : NCATS NIH HHS
ID : KL2 TR000450
Pays : United States
Organisme : NIH HHS
ID : R01 NS060776
Pays : United States
Organisme : NIH HHS
ID : T32 EB001631
Pays : United States
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