TY - JOUR
T1 - Development and validation of a brain maturation index using longitudinal neuroanatomical scans
AU - Cao, Bo
AU - Mwangi, Benson
AU - Hasan, Khader M.
AU - Selvaraj, Sudhakar
AU - Zeni, Cristian P.
AU - Zunta-Soares, Giovana B.
AU - Soares, Jair C.
N1 - Funding Information:
Supported in part by NIMH grant R01 085667 , the Dunn Research Foundation and the Pat Rutherford, Jr. Endowed Chair in Psychiatry (Jair C. Soares).
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/8/5
Y1 - 2015/8/5
N2 - Background: Major psychiatric disorders are increasingly being conceptualized as 'neurodevelopmental', because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. Methods: T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2years. The least absolute shrinkage and selection operator algorithm (LASSO) was 'trained' to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. Results: We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69. years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71. years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27. years. Conclusions: The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders.
AB - Background: Major psychiatric disorders are increasingly being conceptualized as 'neurodevelopmental', because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. Methods: T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2years. The least absolute shrinkage and selection operator algorithm (LASSO) was 'trained' to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. Results: We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69. years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71. years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27. years. Conclusions: The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders.
KW - Brain maturation index
KW - Longitudinal brain imaging
KW - Machine learning
KW - Neurodevelopmental trajectories
KW - Neuroimaging
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U2 - 10.1016/j.neuroimage.2015.05.071
DO - 10.1016/j.neuroimage.2015.05.071
M3 - Article
C2 - 26037051
AN - SCOPUS:84936863475
SN - 1053-8119
VL - 117
SP - 311
EP - 318
JO - NeuroImage
JF - NeuroImage
ER -