Abstract
Within the field of developmental cognitive neuroscience, there is an increasing interest
in studying individual differences in human brain development in order to predict
mental health outcomes. So far, however, most longitudinal neuroimaging studies focus
on group-level estimates. In this review, we highlight longitudinal neuroimaging studies
that have moved beyond group-level estimates to illustrate the heterogeneity in patterns
of brain development. We provide practical methodological recommendations on how longitudinal
neuroimaging datasets can be used to understand heterogeneity in human brain development.
Finally, we address how taking an individual-differences approach in developmental
neuroimaging studies could advance our understanding of why some individuals develop
mental health disorders.
Keywords
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References
- Age of onset of mental disorders: A review of recent literature.Curr Opin Psychiatry. 2007; 20: 359-364
- Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication.Arch Gen Psychiatry. 2005; 62: 593-602
- Prediction complements explanation in understanding the developing brain.Nat Commun. 2018; 9: 589
- Schizophrenia as a disorder of neurodevelopment.Annu Rev Neurosci. 2002; 25: 409-432
- Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories.Hum Brain Mapp. 2010; 31: 917-925
- Life-Span Developmental Psychology: Introduction to Research Methods.Erlbaum, Hillsdale, NJ1977
- Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline.Dev Psychol. 1987; 23: 611-626
- How can we learn about developmental processes from cross-sectional studies, or can we?.Am J Psychiatry. 2000; 157: 163-171
- Evaluating convergence of within-person change and between-person age differences in age-heterogeneous longitudinal studies.Res Hum Dev. 2010; 7: 45-60
- The necessity of longitudinal imaging for characterizing brain maturation. Presented at the Organization for Human Brain Mapping 2019 Annual Meeting, June 9–13, Rome, Italy.(Available at:)https://doi.org/10.5281/zenodo.3244378(Accessed December 18, 2019)Date: 2019
- Unique developmental trajectories of cortical thickness and surface area.NeuroImage. 2014; 87: 120-126
- A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood.NeuroImage. 2013; 82: 393-402
- How does your cortex grow?.J Neurosci. 2011; 31: 7174-7177
- Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume.Hum Brain Mapp. 2016; 37: 2027-2038
- Sex differences in thickness, and folding developments throughout the cortex.NeuroImage. 2013; 82: 200-207
- Structural brain development: A review of methodological approaches and best practices.Dev Cogn Neurosci. 2018; 33: 129-148
- Structural brain development between childhood and adulthood: Convergence across four longitudinal samples.NeuroImage. 2016; 141: 273-281
- Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes.NeuroImage. 2018; 172: 194-205
- Development of the cerebral cortex across adolescence: A multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness.J Neurosci. 2017; 37: 3402-3412
- Methodological considerations for developmental longitudinal fMRI research.Dev Cogn Neurosci. 2018; 33: 149-160
- Longitudinal changes in adolescent risk-taking: A comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior.J Neurosci. 2015; 35: 7226-7238
- Longitudinal growth curves of brain function underlying inhibitory control through adolescence.J Neurosci. 2013; 33: 18109-18124
- Longitudinal development of frontoparietal activity during feedback learning: Contributions of age, performance, working memory and cortical thickness.Dev Cogn Neurosci. 2016; 19: 211-222
- Individual differences in functional brain connectivity predict temporal discounting preference in the transition to adolescence.Dev Cogn Neurosci. 2018; 34: 101-113
- Developmental maturation of the precuneus as a functional core of the default mode network.J Cogn Neurosci. 2019; 31: 1506-1519
- A three-wave longitudinal study of subcortical-cortical resting-state connectivity in adolescence: Testing age- and puberty-related changes.Hum Brain Mapp. 2019; 40: 3769-3783
- Mapping subcortical brain maturation during adolescence: Evidence of hemisphere- and sex-specific longitudinal changes.Dev Sci. 2013; 16: 772-791
- Longitudinal development of human brain wiring continues from childhood into adulthood.J Neurosci. 2011; 31: 10937-10947
- Accelerated longitudinal cortical thinning in adolescence.NeuroImage. 2015; 104: 138-145
- Longitudinal changes in social brain development: Processing outcomes for friend and self.Child Dev. 2017; 88: 1952-1965
- Amygdala–orbitofrontal connectivity predicts alcohol use two years later: A longitudinal neuroimaging study on alcohol use in adolescence.Dev Sci. 2017; 20e12448
- Evaluating the power of latent growth curve models to detect individual differences in change.Struct Equ Modeling. 2008; 15: 541-563
- Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology.Dev Cogn Neurosci. 2018; 33: 54-72
- Have multilevel models been structural equation models all along?.Multivariate Behav Res. 2003; 38: 529-569
- An introduction to latent class growth analysis and growth mixture modeling.Soc Personal Psychol Compass. 2008; 2: 302-317
- Twelve frequently asked questions about growth curve modeling.J Cogn Dev. 2010; 11: 121-136
- Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data.Stat Methods Med Res. 2017; 26: 374-398
- The ABC’s of LGM: An introductory guide to latent variable growth curve modeling.Soc Personal Psychol Compass. 2009; 3: 979-991
- The effects of the number of cohorts, degree of overlap among cohorts, and frequency of observation on power in accelerated longitudinal designs.Methodology. 2011; 7: 11-24
- Longitudinal development of hippocampal subregions from childhood to adulthood.Dev Cogn Neurosci. 2018; 30: 212-222
- Latent Curve Models: A Structural Equation Perspective.1st ed. Wiley-Interscience, Hoboken, NJ2006
- Goal-directed correlates and neurobiological underpinnings of adolescent identity: A multimethod multisample longitudinal approach.Child Dev. 2018; 89: 823-836
- Purpose as a form of identity capital for positive youth adjustment.Dev Psychol. 2011; 47: 1196-1206
- Group-based trajectory modeling in clinical research.Annu Rev Clin Psychol. 2010; 6: 109-138
- Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups.Int J Behav Dev. 2009; 33: 565-576
- Longitudinal trajectories of depression symptoms in adolescence: Psychosocial risk factors and outcomes.Child Psychiatry Hum Dev. 2017; 48: 554-571
- Female and male antisocial trajectories: From childhood origins to adult outcomes.Dev Psychopathol. 2008; 20: 673-716
- Trajectories of childhood aggression and inattention/hyperactivity: Differential effects on substance abuse in adolescence.J Am Acad Child Adolesc Psychiatry. 2008; 47: 1158-1165
- Child personality facets and overreactive parenting as predictors of aggression and rule-breaking trajectories from childhood to adolescence.Dev Psychopathol. 2016; 28: 399-413
- The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.Dev Cogn Neurosci. 2018; 32: 43-54
- The quest for identity in adolescence: Heterogeneity in daily identity formation and psychosocial adjustment across 5 years.Dev Psychol. 2016; 52: 2010-2021
- Neurocognitive and functional heterogeneity in depressed youth.bioRxiv. 2019; https://doi.org/10.1101/778878
- The myth of optimality in clinical neuroscience.Trends Cogn Sci. 2018; 22: 241-257
- Conceptualizing mental disorders as deviations from normative functioning.Mol Psychiatry. 2019; 24: 1415-1424
- The heterogeneity problem: Approaches to identify psychiatric subtypes.Trends Cogn Sci. 2019; 23: 584-601
- Understanding correlates of change by modeling individual differences in growth.Psychometrika. 1985; 50: 203-228
- Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples.Psychology Press, London2015
Article Info
Publication History
Published online: February 10, 2020
Accepted:
January 24,
2020
Received in revised form:
December 18,
2019
Received:
July 2,
2019
Identification
Copyright
© 2020 Society of Biological Psychiatry.