Surface-Based Display of Volume-Averaged Cerebellar Imaging Data.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Human Brain Anatomy

Background:

• Visualizing functional neuroimaging data of the cerebellum presents challenges due to its complex 3D structure.
• Existing methods often require complex reconstructions of individual cerebellar surfaces.

Purpose of the Study:

• To develop a standardized flat representation of the human cerebellum for enhanced visualization of functional imaging data.
• To create a tool that facilitates the analysis of both lobular and medial-lateral cerebellar organization.

Main Methods:

• A novel method utilizing white- and grey-matter surfaces derived from volume-averaged anatomical data.
• Functional data projection along corresponding vertices between the defined surfaces.
• Optimization of the 2D representation for proportional surface area to underlying grey matter volume.

Main Results:

• A flat map of the human cerebellum is presented, enabling visualization of functional data.
• The map maintains a proportional relationship between 2D surface area and 3D grey matter volume.
• Demonstrated visualization of resting-state networks and task-related activity on the flatmap.

Conclusions:

• The cerebellum flatmap offers a concise and effective way to view the complete cerebellar grey matter's activation state.
• The method is compatible with major neuroimaging packages and is freely available.
• Facilitates a comprehensive understanding of cerebellar functional organization.