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Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple
Alicia S Chua1, Svetlana Egorova2, Mark C Anderson1
1Partners Multiple Sclerosis Center, Brigham and Women's 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.
Insights
Accurate measurement of brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV) in multiple sclerosis (MS) requires accounting for MRI acquisition parameters in statistical models. This ensures reliable tracking of disease progression in MS patients.
Area of Science:
- Neuroimaging
- Neurology
- Biostatistics
Background:
- Brain Magnetic Resonance Imaging (MRI) is crucial for assessing disease activity and progression in Multiple Sclerosis (MS).
- Key MRI outcome measures include brain parenchymal fraction (BPF) and T2 hyperintense lesion volume (T2LV), vital for longitudinal studies.
- Variability in MRI acquisition parameters can confound longitudinal assessments of BPF and T2LV, impacting disease progression accuracy.
Purpose of the Study:
- To compare statistical models for incorporating MRI acquisition parameters into longitudinal analyses of BPF and T2LV in MS.
- To identify the optimal statistical approach for accurately modeling disease progression using longitudinal brain MRI data.
Main Methods:
- Utilized a large dataset of 3453 longitudinal MRI scans from 1341 MS subjects in the CLIMB study.
- Compared various linear mixed-effects models to assess the inclusion of protocol and individual MRI acquisition parameters.
- Evaluated model fit indices to determine the best performing models for BPF and T2LV.
Main Results:
- The best model for BPF variance included random intercepts and slopes, protocol-specific residual variance, and fixed effects for baseline age, disease duration, protocol, and study time.
- The optimal model for T2LV variance also featured random intercepts and slopes, with fixed effects for baseline age, disease duration, protocol, and study time.
- Both BPF and T2LV longitudinal modeling benefited significantly from accounting for protocol factors.
Conclusions:
- Future longitudinal studies on BPF and T2LV in MS must incorporate protocol factors into statistical models.
- Accurate assessment of MS disease trajectory relies on robust statistical methods that account for MRI acquisition variability.
