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Neuroimage Clin. 2015 Jul 2;8:606-10. doi: 10.1016/j.nicl.2015.06.009. eCollection 2015.

Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models.

Author information

1
Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA.
2
Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA ; Department of Neurology, Harvard Medical School, Boston, MA, USA.
3
Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA ; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
4
Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA ; Department of Neurology, Harvard Medical School, Boston, MA, USA ; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
5
Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA, USA ; Department of Neurology, Harvard Medical School, Boston, MA, USA ; Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA.

Abstract

Magnetic resonance imaging (MRI) of the brain provides important outcome measures in the longitudinal evaluation of disease activity and progression in MS subjects. Two common measures derived from brain MRI scans are the brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), and these measures are routinely assessed longitudinally in clinical trials and observational studies. When measuring each outcome longitudinally, observed changes may be potentially confounded by variability in MRI acquisition parameters between scans. In order to accurately model longitudinal change, the acquisition parameters should thus be considered in statistical models. In this paper, several models for including protocol as well as individual MRI acquisition parameters in linear mixed models were compared using a large dataset of 3453 longitudinal MRI scans from 1341 subjects enrolled in the CLIMB study, and model fit indices were compared across the models. The model that best explained the variance in BPF data was a random intercept and random slope with protocol specific residual variance along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. The model that best explained the variance in T2LV was a random intercept and random slope along with the following fixed-effects: baseline age, baseline disease duration, protocol and study time. In light of these findings, future studies pertaining to BPF and T2LV outcomes should carefully account for the protocol factors within longitudinal models to ensure that the disease trajectory of MS subjects can be assessed more accurately.

KEYWORDS:

Brain atrophy; MRI; Mixed-effect models; Multiple sclerosis; T2 lesion

PMID:
26199872
PMCID:
PMC4506959
DOI:
10.1016/j.nicl.2015.06.009
[Indexed for MEDLINE]
Free PMC Article

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