How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

• Published in: Frontiers in neuroscience Vol. 15; p. 682812
• Main Authors: Müller, Hans-Peter, Behler, Anna, Landwehrmeyer, G. Bernhard, Huppertz, Hans-Jürgen, Kassubek, Jan
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 15-07-2021; Frontiers Media S.A
• Subjects: Atrophy; Growth models; Huntingtons disease; Investigations; linear fit; Longitudinal studies; longitudinal study; Magnetic resonance imaging; Medical imaging; Neostriatum; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neurological diseases; Neuroscience; Regression analysis; time-interval; Trends
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2021.682812

Background: Longitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualisation of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans. Methods: Different analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV). Results: For the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis. Conclusions: This theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.