Prediction of brain maturity based on cortical thickness at different spatial resolutions

Brain Development Cooperative Group, Budhachandra S. Khundrakpam, Jussi Tohka, Alan C. Evans, William S. Ball, Anna Weber Byars, Mark Schapiro, Wendy Bommer, April Carr, April German, Scott Dunn, Michael J. Rivkin, Deborah Waber, Robert Mulkern, Sridhar Vajapeyam, Abigail Chiverton, Peter Davis, Julie Koo, Jacki Marmor, Christine MrakotskyRichard Robertson, Gloria McAnulty, Michael E. Brandt, Jack M. Fletcher, Larry A. Kramer, Grace Yang, McCormack Cara McCormack, Kathleen M. Hebert, Hilda Volero, Kelly Botteron, Robert C. McKinstry, William Warren, Tomoyuki Nishino, C. Robert Almli, Richard Todd, John Constantino, James T. McCracken, Jennifer Levitt, Jeffrey Alger, Joseph O'Neil, Arthur Toga, Robert Asarnow, David Fadale, Laura Heinichen, Cedric Ireland, Dah Jyuu Wang, Edward Moss, Robert A. Zimmerman, Brooke Bintliff, Ruth Bradford, Janice Newman

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.

Original languageEnglish
Pages (from-to)350-359
Number of pages10
StatePublished - May 1 2015


  • Brain maturation
  • Cortical thickness
  • Elastic-net regularized regression
  • Prediction model
  • Structural magnetic resonance imaging


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