Abstract

OBJECTIVE While there is a long history of interest in measuring brain growth, as of yet there is no defnitive model for normative human brain volume growth. The goal of this study was to analyze a variety of candidate models for such growth and select the model that provides the most statistically applicable ft. The authors sought to optimize clinically applicable growth charts that would facilitate improved treatment and predictive management for conditions such as hydrocephalus. METHODS The Weibull, two-term power law, West ontogenic, and Gompertz models were chosen as potential models. Normative brain volume data were compiled from the NIH MRI repository, and the data were ft using a nonlinear least squares regression algorithm. Appropriate statistical measures were analyzed for each model, and the best model was characterized with prediction bound curves to provide percentile estimates for clinical use. RESULTS Each model curve ft and the corresponding statistics were presented and analyzed. The Weibull ft had the best statistical results for both males and females, while the two-term power law generated the worst scores. The statistical measures and goodness of ft parameters for each model were provided to assure reproducibility. CONCLUSIONS The authors identifed the Weibull model as the most effective growth curve ft for both males and females. Clinically usable growth charts were developed and provided to facilitate further clinical study of brain volume growth in conditions such as hydrocephalus. The authors note that the homogenous population from which the normative MRI data were compiled limits the study. Gaining a better understanding of the dynamics that underlie childhood brain growth would yield more predictive growth curves and improved neurosurgical management of hydrocephalus.

Original languageEnglish (US)
Pages (from-to)478-485
Number of pages8
JournalJournal of Neurosurgery: Pediatrics
Volume21
Issue number5
DOIs
StatePublished - May 2018

All Science Journal Classification (ASJC) codes

  • Surgery
  • Pediatrics, Perinatology, and Child Health
  • Clinical Neurology

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