Prediction complements explanation in understanding the developing brain

Monica D. Rosenberg (), B. J. Casey and Avram J. Holmes
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Monica D. Rosenberg: Yale University
B. J. Casey: Yale University
Avram J. Holmes: Yale University

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Date: 2018
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DOI: 10.1038/s41467-018-02887-9

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