%A Müller,Hans-Peter %A Behler,Anna %A Landwehrmeyer,G. Bernhard %A Huppertz,Hans-Jürgen %A Kassubek,Jan %D 2021 %J Frontiers in Neuroscience %C %F %G English %K Magnetic res- onance imaging,longitudinal study,Time-interval,Linear fit,Regression Analysis %Q %R 10.3389/fnins.2021.682812 %W %L %M %P %7 %8 2021-July-15 %9 Methods %# %! Arrange follow-up time-intervals %* %< %T How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases %U https://www.frontiersin.org/articles/10.3389/fnins.2021.682812 %V 15 %0 JOURNAL ARTICLE %@ 1662-453X %X BackgroundLongitudinal 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 conceptualization 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.MethodsDifferent 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).ResultsFor 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.ConclusionsThis 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.