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The authors sought to optimize clinically applicable growth charts that would facilitate improved treatment and predictive management for conditions such as hydrocephalus.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>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 fit 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>Each model curve fit and the corresponding statistics were presented and analyzed. The Weibull fit 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 fit parameters for each model were provided to assure reproducibility.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSIONS<\/jats:title><jats:p>The authors identified the Weibull model as the most effective growth curve fit 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.<\/jats:p><\/jats:sec>","DOI":"10.3171\/2017.10.peds17141","type":"journal-article","created":{"date-parts":[[2018,3,2]],"date-time":"2018-03-02T15:11:52Z","timestamp":1520003512000},"page":"478-485","source":"Crossref","is-referenced-by-count":29,"title":["Normative human brain volume growth"],"prefix":"10.3171","volume":"21","author":[{"given":"Mallory","family":"Peterson","sequence":"first","affiliation":[{"name":"The Center for Neural Engineering and"},{"name":"Departments of Biomedical Engineering,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin C.","family":"Warf","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Boston Children\u2019s Hospital and Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven J.","family":"Schiff","sequence":"additional","affiliation":[{"name":"The Center for Neural Engineering and"},{"name":"Engineering Science and Mechanics, Neurosurgery, and Physics, The Pennsylvania State University, University Park, Pennsylvania; and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"IEEE Trans Automat Contr","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike H","year":"1974","unstructured":"Akaike H: A new look at the statistical model identification. 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