HIP: Homotopic Individual Brain Parcellation with Dynamic Threshold Region Growing.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Brain Mapping

Background:

• Brain organization is understood through regional mapping, essential for function and organization analysis.
• Existing group atlases miss individual differences, while individual atlases lack hemisphere symmetry, hindering personalized analyses.
• Interhemispheric homotopic correspondence is crucial for understanding brain lateralization and function.

Purpose of the Study:

• To introduce a novel homotopic individual brain parcellation (HIP) method.
• To enhance interhemispheric correspondence in individual brain atlases.
• To improve the accuracy and reliability of individualized brain mapping.

Main Methods:

• Developed a homotopic individual brain parcellation (HIP) algorithm using dynamic threshold region growing.
• Utilized a reference atlas for seed selection and incorporated a homotopic weight term for interhemispheric alignment.
• Employed dynamic thresholding to optimize parcel boundaries and functional homogeneity.

Main Results:

• HIP generated highly reproducible brain atlases with superior functional homogeneity and clustering performance compared to existing methods.
• Ablation studies confirmed the significant contributions of the homotopic weight and dynamic thresholding components.
• The method demonstrated effectiveness across multiple cortical and subcortical atlases.

Conclusions:

• HIP provides a robust and precise method for creating individualized brain parcellations.
• The approach successfully captures individual variability while ensuring interhemispheric homotopic correspondence.
• HIP supports personalized brain imaging analysis, prediction, and clinical diagnosis.