Longitudinal models based on neuroimaging data could help identify children early who are at risk for atypical brain development, suggest findings published August 7 in JAMA Network Open.
These conditional-longitudinal models uncovered deviations for individual children from expected brain growth trajectories, which could better assess differences in maturation, wrote researchers led by Eren Kafadar and Aaron Alexander-Bloch, MD, PhD, from the University of Pennsylvania (Penn) in Philadelphia.
“Conditional-longitudinal models hold promise for applications across psychiatric neuroscience, from development to aging,” Kafadar, Alexander-Bloch, and colleagues wrote.
Brain maturation is not uniform among adolescents, with variations observed between children. The researchers suggested that assessing differences in maturation directly in longitudinal pathways can show deviations from normal brain development patterns.
The Penn research team studied potential associations between longitudinal change in brain volumes and birth weight, gestational age, and longitudinal changes in psychopathology.
The team developed cross-sectional and longitudinal normative models for brain volumes from the ongoing Adolescent Brain Cognitive Development (ABCD) Study. This included baseline MRI data (2016 to 2018) and follow-up data (2019 to 2021). From the models, the team used sectional and longitudinal percentiles to quantify deviations in volumes.
The study included 10,830 study participants with neuroimaging data collected at baseline (average age, 9.9 years) and 7,262 participants with data collected at follow-up (average age, 12.0 years).
The researchers found that longitudinal percentiles were sensitive to individual-specific changes in brain volumes.
Lower birth weight showed ties to lower longitudinal percentiles. This suggests larger decreases in brain volumes over time (n = 27 regions, β range = 0.03 to 0.08). And lower longitudinal percentiles were associated with greater increases in psychopathology. This indicates decreasing brain volumes with increasing psychopathology scores (n = 37 regions, β range = −0.06 to −0.03).
Finally, the team reported no significant associations between changes in psychopathology and brain volumes at either time point when indexed by cross-sectional percentiles.
The study authors highlighted that all data and code are publicly available for future applications. They also called for future research to focus on other morphometric features, alternative parcellations, and studies of the aging brain.
“Our approach may prove useful for investigating deviations from normative trajectories in future clinical and preclinical samples, to ascertain how trajectories of psychiatric conditions relate to the dynamics of brain development,” the authors wrote. “Furthermore, the framework of conditional-longitudinal models can be widely applied to longitudinal data from both developing and aging populations, to improve characterization of how the brain continues to change throughout the lifespan.”
Read the full study here.