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

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The choice of MRI contrast impacts FreeSurfer brain morphometrics. Using T1-MPRAGE with manual editing provides the most reliable results for neuroimaging studies.

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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.