Identifying relevant biomarkers of brain injury from structural MRI: Validation using automated approaches in children with unilateral cerebral palsy

Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions an...

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Bibliographic Details
Published in:PloS one 2017-08, Vol.12 (8), p.e0181605-e0181605
Main Authors: Pagnozzi, Alex M, Dowson, Nicholas, Doecke, James, Fiori, Simona, Bradley, Andrew P, Boyd, Roslyn N, Rose, Stephen
Format: Article
Language:English
Subjects:
Adolescent
Automation
Biological markers
Biology and Life Sciences
Biomarkers
Brain
Brain injuries
Brain Injuries - diagnostic imaging
Brain Injuries - pathology
Brain injury
Brain research
Cerebral palsy
Cerebral Palsy - pathology
Child
Child, Preschool
Children
Cognition & reasoning
Cognitive ability
Cohort Studies
Cortex
Diagnosis
Enlargement
Female
Gray Matter - pathology
Head injuries
Health aspects
Humans
Image Processing, Computer-Assisted
Lesions
Magnetic resonance
Magnetic Resonance Imaging
Male
Medical research
Medicine and Health Sciences
Motor task performance
NMR
Nuclear magnetic resonance
Paralysis
Pediatrics
People and Places
Reproducibility of Results
Research and Analysis Methods
Sensorimotor integration
Social Sciences
Structure-Activity Relationship
Structure-function relationships
Substantia grisea
Traumatic brain injury
Ventricle
Visual perception
White Matter - pathology
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Summary:Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions and ventricular enlargement, have been categorised, these injuries are commonly only assessed in a qualitative fashion. As a result, sMRI remains relatively underexploited for clinical assessments, despite its widespread use. In this study, several automated and validated techniques to automatically quantify these three classes of injury were generated in a large cohort of children (n = 139) aged 5-17, including 95 children diagnosed with unilateral CP. Using a feature selection approach on a training data set (n = 97) to find severity of injury biomarkers predictive of clinical function (motor, cognitive, communicative and visual function), cortical shape and regional lesion burden were most often chosen associated with clinical function. Validating the best models on the unseen test data (n = 42), correlation values ranged between 0.545 and 0.795 (p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0181605