Abstract
Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages of abnormal change may be difficult to identify, particularly at an individual level. Brain age prediction models may have utility in assessing brain development in an individualized manner, as deviations between chronological age and predicted brain age could reflect one's divergence from typical development. Here, we built a support vector regression model to summarize high-dimensional neuroimaging as an index of brain age in both sexes. Using structural and functional MRI data from two large pediatric datasets and a third clinical dataset, we produced and validated a two-dimensional neural maturation index (NMI) that characterizes typical brain maturation patterns and identifies those who deviate from this trajectory. Examination of brain signatures associated with NMI scores revealed that elevated scores were related to significantly lower gray matter volume and significantly higher white matter volume, particularly in high-order regions such as the prefrontal cortex. Additionally, those with higher NMI scores exhibited enhanced connectivity in several functional brain networks, including the default mode network. Analysis of data from a sample of male and female patients with schizophrenia revealed an association between advanced NMI scores and schizophrenia diagnosis in participants aged 16-22, confirming the NMI's utility as a marker of atypicality. Altogether, our findings support the NMI as an individualized, interpretable measure by which neural development in adolescence may be assessed.SIGNIFICANCE STATEMENT The substantial neural restructuring that occurs during adolescence increases one's vulnerability to aberration. A brain index that is capable of capturing one's conformance with typical development will allow for individualized assessment and enhance our understanding of typical and atypical development. In this analysis, we produce a neural maturation index (NMI) using support vector regression and a large pediatric sample. This index generalizes across multiple cohorts and shows potential in the identification of clinical groups. We also implement a novel method for examining the developmental trajectory through data-driven analysis. The signatures identified by the NMI reflect key stages of the extensive neural development that occurs during adolescence and support its utility as a metric of typical brain development.