Related Experiment Video
Updated: Jun 14, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal
Mehul P Sampat1, Brian C Healy, Dominik S Meier
1Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
Abstract:
The measurement of brain atrophy from magnetic resonance imaging (MRI) has become an established method of estimating disease severity and progression in multiple sclerosis (MS). Most commonly reported in the form of brain parenchymal fraction (BPF), it is more sensitive to the degenerative component of the disease and shows progression more reliably than lesion burden. Typically, the reliability of BPF and other morphometric measurements is assessed by evaluating scan-rescan experiments. While these experiments provide good estimates of real-life error related to imperfect patient repositioning in the MRI scanner, measurement variance due to physiological and reversible pathological fluctuations in brain volume are not taken into account. In this work, we propose a new model for estimating variability in serial morphometry, particularly the BPF measurement. Specifically, we attempt to detect and explicitly model the remaining sources of error to more accurately describe the overall variability in BPF measurements. Our results show that sources of variability beyond subject repositioning error are important and cannot be ignored. We demonstrate that scan-rescan experiments only provide a lower bound on the true error in repeated measurements of patients' BPF. We have estimated the variance due to patient repositioning during scan-rescan (sigma(sr)(2) = 3.0e-06), variance assigned to physiological fluctuations (sigma(p)(2) = 5.74e-06) and the variance associated with lesion activity (sigma(les)(2) = 1.09e-05). These variance components can be used to determine the relative impact of their sources on sample size estimates for studies investigating change over time in MS patients. Our results demonstrate that sample size calculations based exclusively on scan-rescan variability (sigma(sr)) are likely to underestimate the number of patients required. If the physiological variability (sigma(p)) is incorporated in sample size calculations, the required sample size would increase by a factor of 5.69 based on standard t-test sample size calculation.
Insights
Magnetic resonance imaging (MRI) brain atrophy measurements in multiple sclerosis (MS) are affected by more than just repositioning errors. Accounting for physiological fluctuations and lesion activity is crucial for accurate sample size calculations in MS studies.
Area of Science:
- Neuroimaging
- Biostatistics
- Neurology
Background:
- Brain atrophy measurement using MRI, particularly brain parenchymal fraction (BPF), is vital for tracking multiple sclerosis (MS) progression.
- Current reliability assessments rely on scan-rescan experiments, which primarily account for patient repositioning errors.
- These experiments overlook other significant sources of variability, such as physiological fluctuations and lesion activity.
Purpose of the Study:
- To develop a novel model for estimating variability in serial morphometric measurements, specifically BPF in MS patients.
- To identify and model error sources beyond patient repositioning in MRI scans.
- To provide a more accurate estimation of overall variability in BPF measurements for improved MS research.
Main Methods:
- Proposed a new statistical model to detect and quantify sources of variability in serial brain atrophy measurements.
- Analyzed scan-rescan experiments to estimate variance components.
- Quantified variance attributed to patient repositioning (σ(sr)²), physiological fluctuations (σ(p)²), and lesion activity (σ(les)²).
Main Results:
- Scan-rescan experiments underestimate the true error in repeated BPF measurements.
- Identified and quantified key sources of variability: σ(sr)² = 3.0e-06, σ(p)² = 5.74e-06, and σ(les)² = 1.09e-05.
- Demonstrated that ignoring physiological variability leads to underestimation of required sample sizes for MS studies.
Conclusions:
- Variability in BPF measurements in MS extends beyond repositioning errors and includes physiological and lesion-related factors.
- Accurate sample size calculations for longitudinal MS studies must incorporate all sources of measurement variability.
- Incorporating physiological variability (σ(p)²) into sample size calculations could increase the required patient cohort by a factor of 5.69.
