How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases.
linear fit
longitudinal study
magnetic resonance imaging
regression analysis
time-interval
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2021
2021
Historique:
received:
19
03
2021
accepted:
08
06
2021
entrez:
2
8
2021
pubmed:
3
8
2021
medline:
3
8
2021
Statut:
epublish
Résumé
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. 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). 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. 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.
Sections du résumé
BACKGROUND
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 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.
METHODS
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
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
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.
Identifiants
pubmed: 34335162
doi: 10.3389/fnins.2021.682812
pmc: PMC8319674
doi:
Types de publication
Journal Article
Langues
eng
Pagination
682812Informations de copyright
Copyright © 2021 Müller, Behler, Landwehrmeyer, Huppertz and Kassubek.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Neuroimage. 2020 Oct 15;220:117129
pubmed: 32640273
J Neurol Neurosurg Psychiatry. 2018 Apr;89(4):374-381
pubmed: 29101254
Neurology. 2020 Aug 25;95(8):e943-e952
pubmed: 32646955
Schizophr Res. 2005 Oct 1;78(1):45-60
pubmed: 15979287
Neuroimage. 2007 Jul 1;36(3):491-6
pubmed: 17467296
Neuroimage Clin. 2016 Mar 16;11:408-414
pubmed: 27104135
Front Psychol. 2015 Mar 24;6:272
pubmed: 25852596
Arch Neurol. 2012 Jul;69(7):856-67
pubmed: 22409939
Am J Psychiatry. 2011 Sep;168(9):894-903
pubmed: 21724665
Neurology. 2005 Mar 22;64(6):1032-9
pubmed: 15781822
J Neurol Neurosurg Psychiatry. 2015 Dec;86(12):1291-8
pubmed: 25669748
Arch Neurol. 2011 Aug;68(8):1040-8
pubmed: 21825241
Neuroimage. 2010 Feb 1;49(3):2216-24
pubmed: 19878722
Hum Brain Mapp. 2021 Feb 15;42(3):737-752
pubmed: 33103324
J Neurol. 2021 May;268(5):1913-1926
pubmed: 33399966
J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S287-91
pubmed: 21029055
Parkinsonism Relat Disord. 2019 Jun;63:179-184
pubmed: 30846243
Neuroimage. 2013 Feb 1;66:249-60
pubmed: 23123680
Lancet Neurol. 2009 Sep;8(9):791-801
pubmed: 19646924
NeuroRx. 2005 Apr;2(2):348-60
pubmed: 15897955