Manually-parcellated gyral data accounting for all known anatomical variability

Area of Science:

• Neuroimaging
• Neuroanatomy
• Brain Morphometry

Background:

• Brain morphometric changes are crucial for understanding aging, disease, and identifying biomarkers.
• Accurate brain parcellation is essential for reliable morphometric analysis.
• Standard analysis packages may not fully capture anatomical variability.

Purpose of the Study:

• To present a manually-parcellated dataset of specific brain gyri (superior frontal, supramarginal, cingulate) from healthy middle-aged subjects.
• To provide a detailed protocol for manual brain parcellation, accounting for anatomical variability.
• To offer grey matter thickness, volume, and white matter surface area data for each parcel.

Main Methods:

• Manual drawing of gyral parcels on brain images of 10 healthy middle-aged subjects.
• Review of hand-drawn parcels by blinded specialists to ensure consistency.
• Development of a protocol based on two anatomical atlases, avoiding reliance on neighboring gyri for edge definition.

Main Results:

• A manually-parcellated dataset of the superior frontal, supramarginal, and cingulate gyri was created.
• A detailed protocol for consistent manual parcellation was established.
• Grey matter thickness, grey matter volume, and white matter surface area data were provided for each parcel.

Conclusions:

• The presented dataset and protocol offer a valuable resource for neuroimaging research.
• This resource can be used to assess the accuracy of automated parcellation tools and their impact on morphometric studies.
• The findings contribute to a better understanding of brain morphometry in healthy aging and disease contexts.