A multidimensional neural maturation index reveals reproducible developmental patterns in children and adolescents

Monica Truelove-Hill, Guray Erus, Vishnu Bashyam, Erdem Varol, Chiharu Sako, Ruben C. Gur, Raquel E. Gur, Nikolaos Koutsouleris, Chuanjun Zhuo, Yong Fan, Daniel H. Wolf, Theodore D. Satterthwaite, Christos Davatzikos

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish (US)
    Pages (from-to)1265-1275
    Number of pages11
    JournalJournal of Neuroscience
    Volume40
    Issue number6
    DOIs
    StatePublished - Feb 5 2020

    Keywords

    • Adolescence
    • Brain age
    • Brain development
    • FMRI
    • Machine learning
    • SMRI

    ASJC Scopus subject areas

    • General Medicine

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