ArticleNeuroimage. Reports2021
Participant followup rate can bias structural imaging measures in longitudinal studies.
Article in Neuroimage. Reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Increased Cortical Thickness in Alzheimer's Disease.Annals of neurology · 2024Article
- Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS.NeuroImage. Clinical · 2023Article
- Cardiometabolic risk factors associated with brain age and accelerate brain ageing.Human brain mapping · 2022Article
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Authors and funding
7 authors.
Funding
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Abstract
Longitudinal MRI analysis is essential to accurately describe neuroanatomical changes over time. Loss of participants to followup (dropout) in longitudinal studies is inevitable and can lead to great difficulty in interpretation of statistical results if dropout is correlated with a study outcome or exposure. Beyond this, technical aspects of longitudinal MRI analysis require specialised processing pipelines to improve reliability while avoiding bias towards individual timepoints. In this article we test whether there is an additional problem that must be considered in longitudinal imaging studies, namely whether dropout has an impact on the function of FreeSurfer, a popular software pipeline used to estimate important structural brain metrics. We find that the number of acquisitions available per individual can impact the estimation of cortical thickness and brain volume using the FreeSurfer longitudinal pipeline, and can induce group differences in brain metrics. The effect on trajectories of brain metrics is smaller than the effect on brain metrics.
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