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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Predicting Developmental Norms from Baseline Cortical Thickness in Longitudinal Studies.

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New longitudinal brain models (B-Norms) improve predictions of cortical thickness changes in youth compared to standard cross-sectional models (C-Norms). B-Norms show greater sensitivity to developmental processes, enhancing understanding of normative brain development.

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Area of Science:

  • Computational psychiatry
  • Neuroimaging
  • Developmental neuroscience

Background:

  • Normative models are crucial for understanding individual differences in brain structure relative to population norms.
  • Existing models primarily use cross-sectional data, limiting their ability to capture longitudinal brain changes.
  • The Adolescent Brain Cognitive Development (ABCD) study provides valuable longitudinal data for developing advanced normative models.

Purpose of the Study:

  • To develop and validate sex-specific Baseline-Integrated Norms (B-Norms) for predicting longitudinal changes in cortical thickness.
  • To compare the predictive accuracy of B-Norms against standard Cross-Sectional Norms (C-Norms).
  • To assess the sensitivity of B-Norms to developmental processes in youth.

Main Methods:

  • Utilized longitudinal brain imaging data (cortical thickness) from the ABCD study across three time points (baseline, 2-year, 4-year follow-up).
  • Trained sex-specific B-Norms using baseline and 2-year follow-up data, incorporating baseline thickness, baseline age, and follow-up age.
  • Compared B-Norms with sex-specific C-Norms (age-based) using out-of-sample testing on independent longitudinal data.

Main Results:

  • B-Norms demonstrated consistently better fits and higher explained variance than C-Norms across nearly all cortical regions.
  • No significant differences in model performance were observed between the 2-year and 4-year follow-up time points.
  • B-Norms identified associations with pubertal changes in four cortical regions, a sensitivity not observed with C-Norms.

Conclusions:

  • Baseline-Integrated Norms (B-Norms) offer a more accurate and sensitive approach for modeling longitudinal structural brain changes in youth.
  • B-Norms have the potential to better capture normative variation and developmental trajectories in brain structure.
  • These findings advance the application of normative modeling in computational psychiatry and developmental neuroscience.