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

PLoS One. 2017 Aug 1;12(8):e0181605. doi: 10.1371/journal.pone.0181605. eCollection 2017.

Abstract

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<0.008), indicating significant associations with clinical function. The measured prevalence of injury, including ventricular enlargement (70%), white and grey matter lesions (55%) and cortical malformations (30%), were similar to the prevalence observed in other cohorts of children with unilateral CP. These findings support the early characterisation of injury from sMRI into previously defined aetiologies as part of standard clinical assessment. Furthermore, the strong and significant association between quantifications of injury observed on structural MRI and multiple clinical scores accord with empirically established structure-function relationships.

Publication types

  • Validation Study

MeSH terms

  • Adolescent
  • Automation
  • Brain Injuries / diagnostic imaging*
  • Brain Injuries / pathology*
  • Cerebral Palsy / pathology*
  • Child
  • Child, Preschool
  • Cohort Studies
  • Female
  • Gray Matter / pathology
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male
  • Reproducibility of Results
  • Structure-Activity Relationship
  • White Matter / pathology

Grants and funding

Alex M. Pagnozzi is supported by the Australian Postgraduate Award (APA) from The University of Queensland, and the Commonwealth Scientific Industrial and Research Organisation (CSIRO). Roslyn N. Boyd is supported by a Foundation for Children Grant, NHMRC Research Fellowship (2015–2020) and a NHMRC Project Grant COMBIT (1003887). Roslyn N. Boyd and Stephen Rose are supported by the by the Smart Futures Co-Investment Program Grant. Andrew Bradley is supported by an ARC Future fellowship (FT110100623). The funding bodies have not contributed to the study design, the collection, management, analysis and interpretation of data, the writing of final reports or the decision to submit findings for publication.