A study published in Science (2010) demonstrates that functional connectivity magnetic resonance imaging (fcMRI) data can predict individual brain maturity. Using support vector machine-based multivariate pattern analysis (MVPA), researchers analyzed 238 resting-state fcMRI scans from typically developing volunteers aged 7 to 30 years. The analysis revealed that 5 minutes of fcMRI data could predict individual brain maturity with 91% accuracy, as measured by a functional connectivity maturation index (fcMI). The fcMI, derived from predicted brain age, showed a nonlinear asymptotic growth curve, with the greatest contribution to prediction coming from the weakening of short-range functional connections between major brain networks. The study used MVPA to analyze functional connectivity patterns, which are sensitive to changes in brain maturity. The results showed that functional brain maturity follows a growth curve similar to biological models of asymptotic growth, such as the Von Bertalanffy and Pearl-Reed curves. The fcMI was calculated by normalizing predicted brain age to a scale where typically developing young adults (18-30 years) had an fcMI of 1.0. The study also identified key brain regions contributing to brain maturity prediction, including the right anterior prefrontal cortex and the precuneus. Functional connections that weakened with age were more important for predicting brain maturity than those that strengthened. The study further showed that functional integration and segregation play critical roles in brain maturation, with long-range connections being more important for integration and short-range connections for segregation. The findings suggest that fcMRI-based maturation analyses can generalize across different populations and types of fcMRI data. The study highlights the potential of fcMRI in diagnosing developmental disorders and neuropsychiatric conditions, as it provides a reliable measure of brain maturity that is not affected by structural abnormalities. The results also indicate that functional brain maturation is a complex process involving both developmental and experiential factors. The study's approach could be applied to structural brain maturation studies, offering a functional counterpart to anatomical studies.A study published in Science (2010) demonstrates that functional connectivity magnetic resonance imaging (fcMRI) data can predict individual brain maturity. Using support vector machine-based multivariate pattern analysis (MVPA), researchers analyzed 238 resting-state fcMRI scans from typically developing volunteers aged 7 to 30 years. The analysis revealed that 5 minutes of fcMRI data could predict individual brain maturity with 91% accuracy, as measured by a functional connectivity maturation index (fcMI). The fcMI, derived from predicted brain age, showed a nonlinear asymptotic growth curve, with the greatest contribution to prediction coming from the weakening of short-range functional connections between major brain networks. The study used MVPA to analyze functional connectivity patterns, which are sensitive to changes in brain maturity. The results showed that functional brain maturity follows a growth curve similar to biological models of asymptotic growth, such as the Von Bertalanffy and Pearl-Reed curves. The fcMI was calculated by normalizing predicted brain age to a scale where typically developing young adults (18-30 years) had an fcMI of 1.0. The study also identified key brain regions contributing to brain maturity prediction, including the right anterior prefrontal cortex and the precuneus. Functional connections that weakened with age were more important for predicting brain maturity than those that strengthened. The study further showed that functional integration and segregation play critical roles in brain maturation, with long-range connections being more important for integration and short-range connections for segregation. The findings suggest that fcMRI-based maturation analyses can generalize across different populations and types of fcMRI data. The study highlights the potential of fcMRI in diagnosing developmental disorders and neuropsychiatric conditions, as it provides a reliable measure of brain maturity that is not affected by structural abnormalities. The results also indicate that functional brain maturation is a complex process involving both developmental and experiential factors. The study's approach could be applied to structural brain maturation studies, offering a functional counterpart to anatomical studies.