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Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's
Simon Rechberger1, Yong Li2, Sebastian J Kopetzky2,3
1Viscovery Software GmbH, Vienna, Austria.
Abstract:
Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.
Insights
This study introduces an automated MRI analysis pipeline for neurodegenerative diseases. The advanced method improves detection of subtle brain changes, even in small patient groups, aiding clinical trials.
Area of Science:
- Neuroimaging
- Neurodegenerative Diseases
- Biostatistics
Background:
- Longitudinal MRI studies are crucial for tracking neurodegenerative diseases and drug efficacy.
- Manual assessments are time-consuming and often yield suboptimal results.
Purpose of the Study:
- To develop and validate a precise, automated analysis pipeline for longitudinal MRI studies.
- To enhance the detection of subtle neurodegenerative changes in small cohorts.
Main Methods:
- Automated image processing including surface and voxel-based morphometry with detailed brain atlases (HCP MMP 1.0).
- Statistical analysis using a multiplicative model of annual percent change (APC).
- Multiple testing correction adapted from genome-wide association studies.
Main Results:
- The pipeline identified 22 significant morphometric changes (cortical volume, area, thickness) using surface-based morphometry (SBM) in a small Alzheimer's cohort.
- Compared to VBM, SBM detected more significant changes, highlighting its sensitivity.
- A 1-year decrease in brain morphometry correlated with increased clinical disability and cognitive decline.
Conclusions:
- Automated MRI analysis with precise morphometry and statistical correction yields significant outcomes even in small study cohorts.
- The proposed pipeline is reliable and suitable for routine use in clinical trials for neurodegenerative diseases.
