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Development and validation of a brain maturation index using longitudinal neuroanatomical scans.
Cao, Bo; Mwangi, Benson; Hasan, Khader M; Selvaraj, Sudhakar; Zeni, Cristian P; Zunta-Soares, Giovana B; Soares, Jair C.
Afiliação
  • Cao B; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA.
  • Mwangi B; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA. Electronic address: Benson.Irungu@uth.tmc.edu.
  • Hasan KM; Department of Diagnostic & Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Selvaraj S; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA.
  • Zeni CP; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA.
  • Zunta-Soares GB; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA.
  • Soares JC; Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, USA.
Neuroimage ; 117: 311-8, 2015 Aug 15.
Article em En | MEDLINE | ID: mdl-26037051
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=1.69years), and a high correlation between the second-visit chronological age and brain maturation index (r=0.83, MAE=1.71years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27years. 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Imageamento por Ressonância Magnética / Indicadores Básicos de Saúde / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Imageamento por Ressonância Magnética / Indicadores Básicos de Saúde / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Neuroimage Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos