Background: Regardless of the precise mechanism, all neurodevelopmental models of risk assume that, at the population level, there exist subgroups of individuals that share similar patterns of neural function and development-and that these subgroups somehow relate to psychiatric risk. However, the existence of multiple neurodevelopmental subgroups at the population level has not been assessed previously.
Methods: In the current study, cross-validated latent profile analysis was used to test for the presence of empirically derived, brain-based developmental subgroups using functional magnetic resonance imaging data from 6758 individuals (49.4% female; mean age = 9.94 years) in the Adolescent Brain and Cognitive Development (ABCD) study wave 1 release. Data were randomly split into training and testing samples.
Results: Analyses in the training sample (n = 3379) identified a seven-profile solution (entropy = 0.880) that was replicated in the held-out testing data (n = 3379, entropy = 0.890). Identified subgroups included a moderate group (66.8%), high reward (4.3%) and low reward (4.0%) groups, high inhibition (9.8%) and low inhibition (6.7%) groups, and high emotion regulation (4.0%) and low emotion regulation (4.3%) groups. Relative to the moderate group, other subgroups were characterized by more males (χ2 = 24.10, p = .0005), higher proportions of individuals from lower-income households (χ2 = 122.17, p < .0001), poorer cognitive performance (ps < .0001), more screen time (F = 6.80, p < .0001), heightened impulsivity (ps < .006), and higher rates of neurodevelopmental disorders (χ2 = 26.20, p = .0002).
Conclusions: These data demonstrate the existence of multiple, distinct neurodevelopmental subgroups at the population level. They indicate that these empirically derived, brain-based developmental profiles relate to differences in clinical features, even at a young age, and prior to the peak period of risk for the development of psychopathology.
Keywords: Adolescence; Latent profile analysis; Psychopathology; Risk factors; Split-half validation; fMRI.
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