Population-based studies of brain imaging patterns in cerebral palsy
Article first published online: 12 AUG 2013
© 2013 Mac Keith Press
Developmental Medicine & Child Neurology
How to Cite
Reid, S. M., Dagia, C. D., Ditchfield, M. R., Carlin, J. B. and Reddihough, D. S. (2013), Population-based studies of brain imaging patterns in cerebral palsy. Developmental Medicine & Child Neurology. doi: 10.1111/dmcn.12228
- Article first published online: 12 AUG 2013
- Manuscript Accepted: 13 MAY 2013
- William Henry and Vera Ellen Houston Memorial Trust Fund and the CP Alliance
- Australian National Health and Medical Research Council
The aim of this study was to review the distribution of neuroimaging findings from a contemporary population cohort of individuals with cerebral palsy (CP) and to facilitate standardization of imaging classification.
Publications from 1995 to 2012 reporting imaging findings in population cohorts were selected through a literature search, and review of the titles, abstracts, and content of studies. Relevant data were extracted, including unpublished data from Victoria, Australia. The proportions for each imaging pattern were tabulated, and heterogeneity was assessed for all individuals with CP, and for subgroups based on gestational age, CP subtype, and Gross Motor Function Classification System level.
Studies from three geographic regions met the inclusion criteria for individuals with CP, and two additional studies reported on specific CP subtypes. Brain abnormalities were observed in 86% of scans, but were observed least often in children with ataxia (24–57%). White matter injury was the most common imaging pattern (19–45%), although the proportions showed high heterogeneity. Additional patterns were grey matter injury (21%), focal vascular insults (10%), malformations (11%), and miscellaneous findings (4–22%).
This review suggests areas where further dialogue will facilitate progress towards standardization of neuroimaging classification. Standardization will enable future collaborations aimed at exploring the relationships among magnetic resonance imaging patterns, risk factors, and clinical outcomes, and, ultimately, lead to better understanding of causal pathways and opportunities for prevention.