{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:21:03Z","timestamp":1787026863819,"version":"build-2736575974"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s disease patients, detected by atlas-based volumetry (ABV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2021.682812","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T06:04:15Z","timestamp":1626329055000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases"],"prefix":"10.3389","volume":"15","author":[{"given":"Hans-Peter","family":"M\u00fcller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Behler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. 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