%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.