LOGISMOS-B for Primates: Primate Cortical Surface Reconstruction and Thickness Measurement

Area of Science:

• Neuroimaging
• Comparative Anatomy
• Computational Neuroscience

Background:

• Cortical thickness and surface area are critical neuroimaging metrics for understanding brain development and disease.
• Automated cortical surface reconstruction from 3D MRI is complex, especially for non-human primate brains due to anatomical variability.
• Existing human brain methods often lack direct applicability to primate neuroanatomy.

Purpose of the Study:

• To adapt and validate an advanced cortical reconstruction algorithm (LOGISMOS-B) for primate brain analysis.
• To assess the accuracy of Laplace-based cortical thickness measurements in macaque brains.
• To provide a robust tool for quantitative morphological studies in primate neuroscience.

Main Methods:

• Utilized the LOGISMOS-B algorithm for cortical surface reconstruction from 3D MRI scans.
• Employed a Laplace-based method for measuring cortical thickness.
• Quantitatively evaluated performance using T1- and T2-weighted MRI data from 12-month-old macaques with expert anatomical labeling as ground truth.

Main Results:

• The LOGISMOS-B algorithm achieved high accuracy in macaque brain cortical reconstruction.
• Average signed surface error was 0.01 ± 0.03mm, and unsigned surface error was 0.42 ± 0.03mm across the whole brain.
• Excluding the temporal pole improved unsigned surface distance to 0.34 ± 0.03mm, demonstrating algorithm robustness.

Conclusions:

• The adapted LOGISMOS-B algorithm provides accurate and robust cortical surface reconstruction and thickness measurement for primate brains.
• This method holds significant potential for advancing quantitative neuroimaging research in primate models.
• The findings highlight the algorithm's suitability for challenging developmental datasets and comparative neurological studies.