Contrast input and manual interventions significantly affect FreeSurfer morphometry and clinical correlations

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Radiology

Background:

• FreeSurfer is a standard tool for brain morphometric analysis in aging and neurodegeneration research.
• Variability in MRI contrast selection and manual editing procedures can influence FreeSurfer outcomes.
• The impact of these choices on clinically relevant findings is not well understood.

Purpose of the Study:

• To investigate how different MRI contrasts (T1-MPRAGE, T1+T2-FLAIR, T1+T2-SPACE) affect FreeSurfer morphometric measurements.
• To assess the role of manual editing in mitigating contrast-related biases.
• To determine the optimal input contrast and editing strategy for reliable morphometric analysis.

Main Methods:

• Analysis of FreeSurfer morphometrics (cortical thickness, surface area, volume) from T1-MPRAGE, T1+T2-FLAIR, and T1+T2-SPACE data.
• Comparison of results with and without manual editing in two independent cohorts (CAM and ADNI).
• Evaluation of segmentation quality and influence on age- and smoking-related associations.

Main Results:

• Input MRI contrast significantly altered FreeSurfer estimates of cortical thickness, surface area, and volume across lobar regions.
• T1+T2-FLAIR and T1+T2-SPACE yielded greater cortical thickness and smaller surface areas compared to T1-MPRAGE.
• Manual editing improved segmentation quality and reduced contrast-specific biases, leading to more consistent morphometric outcomes.

Conclusions:

• MRI contrast selection introduces significant non-biological variation into FreeSurfer morphometric analyses.
• T1-MPRAGE as input contrast, combined with manual editing, offers the most reliable and reproducible morphometric outcomes.
• Standardized reporting of post-processing protocols is crucial for accurate interpretation of neuroimaging findings across studies.