When change is the only constant: The promise of longitudinal neuroimaging in understanding social anxiety disorder

•The human brain has a protracted developmental trajectory and is inherently adaptive.•A dynamic, developmental aspect is largely missing from neurobiological models of psychopathology.•Longitudinal data are key to progress in our understanding of social anxiety disorder.•Analytical recommendations...

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Bibliographic Details
Published in:Developmental cognitive neuroscience 2018-10, Vol.33, p.73-82
Main Authors: Haller, Simone P.W., Mills, Kathryn L., Hartwright, Charlotte E., David, Anthony S., Cohen Kadosh, Kathrin
Format: Article
Language:English
Subjects:
Adolescence
Brain Mapping - methods
Cross-Sectional Studies
Development
Female
fMRI
Humans
Longitudinal imaging
Longitudinal Studies
Magnetic Resonance Imaging - methods
Male
Neuroimaging - methods
Phobia, Social - diagnostic imaging
Psychopathology
Social anxiety
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Summary:•The human brain has a protracted developmental trajectory and is inherently adaptive.•A dynamic, developmental aspect is largely missing from neurobiological models of psychopathology.•Longitudinal data are key to progress in our understanding of social anxiety disorder.•Analytical recommendations for longitudinal imaging studies are made. Longitudinal studies offer a unique window into developmental change. Yet, most of what we know about the pathophysiology of psychiatric disorders is based on cross-sectional work. Here, we highlight the importance of adopting a longitudinal approach in order to make progress towards identifying the neurobiological mechanisms of social anxiety disorder (SAD). Using examples, we illustrate how longitudinal data can uniquely inform SAD etiology and timing of interventions. The brain’s inherently adaptive quality requires that we model risk correlates of disorders as dynamic in their expression. Developmental theories regarding timing of environmental events, cascading effects and (mal)adaptations of the developing brain will be crucial components of comprehensive, integrative models of SAD. We close by discussing analytical considerations when working with longitudinal, developmental data.
ISSN:1878-9293
1878-9307
DOI:10.1016/j.dcn.2018.05.005