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Time-to-event voxel-based techniques to assess regional atrophy associated with MCI risk of progression to AD
Prashanthi Vemuri1, Stephen D Weigand, David S Knopman
1Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Objective:
When using imaging to predict time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), time-to-event statistical methods account for varying lengths of follow-up times among subjects whereas two-sample t-tests in voxel-based morphometry (VBM) do not. Our objectives were to apply a time-to-event voxel-based analytic method to identify regions on MRI where atrophy is associated with significantly increased risk of future progression to AD in subjects with MCI and to compare it to traditional voxel-level patterns obtained by applying two-sample methods. We also compared the power required to detect an association using time-to-event methods versus two-sample approaches.
Methods:
Subjects with MCI at baseline were followed prospectively. The event of interest was clinical diagnosis of AD. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on rank-transformed voxel-level gray matter density (GMD) estimates.
Results:
The greatest risk of progression to AD was associated with atrophy of the medial temporal lobes. Patients ranked at the 25th percentile of GMD in these regions had more than a doubling of risk of progression to AD at a given time point compared to patients at the 75th percentile. Power calculations showed the time-to-event approach to be more efficient than the traditional two-sample approach.
Conclusions:
We present a new voxel-based analytic method that incorporates time-to-event statistical methods. In the context of a progressive disease like AD, time-to-event VBM seems more appropriate and powerful than traditional two-sample methods.
Insights
A new time-to-event voxel-based morphometry method improves prediction of Alzheimer's disease progression from mild cognitive impairment. This approach is more powerful and efficient than traditional methods for identifying at-risk brain regions.
Area of Science:
- Neuroimaging
- Neurology
- Biostatistics
Background:
- Predicting Alzheimer's disease (AD) progression from mild cognitive impairment (MCI) is crucial for timely intervention.
- Traditional voxel-based morphometry (VBM) methods using t-tests do not adequately account for varying follow-up times in longitudinal studies.
- Time-to-event statistical methods offer a more appropriate framework for analyzing progression in diseases with variable onset.
Purpose of the Study:
- To apply a novel time-to-event VBM method to identify brain regions associated with increased risk of MCI to AD progression.
- To compare the efficacy and power of this new method against traditional two-sample t-test VBM approaches.
- To assess the efficiency of time-to-event VBM in detecting associations between regional atrophy and AD progression.
Main Methods:
- Prospective follow-up of subjects diagnosed with MCI.
- Utilized Cox proportional hazards models adjusted for demographic factors (age, sex, education).
- Estimated relative hazard of progression based on rank-transformed voxel-level gray matter density (GMD).
Main Results:
- Medial temporal lobe atrophy was most strongly associated with increased risk of progression to AD.
- Individuals with lower GMD (25th percentile) in these regions showed over a twofold increased risk compared to those with higher GMD (75th percentile).
- Power calculations demonstrated superior efficiency for the time-to-event VBM approach over traditional two-sample methods.
Conclusions:
- Introduced a novel voxel-based analytic method integrating time-to-event statistics for neuroimaging research.
- Time-to-event VBM is a more appropriate and statistically powerful tool for analyzing progressive neurodegenerative diseases like Alzheimer's disease.
- This method enhances the ability to predict disease progression and identify critical at-risk brain regions in MCI patients.
