Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering

Area of Science:

• Neuroimaging
• Human Brain Anatomy
• Diffusion MRI

Background:

• The amygdala is crucial for fear and emotional learning.
• Identifying amygdala subnuclei in vivo is challenging.
• Existing methods lack efficiency and objectivity.

Purpose of the Study:

• To develop an efficient, data-driven imaging method for in vivo amygdala subnuclei identification.
• To leverage diffusion MRI for microstructural characterization of amygdala subfields.

Main Methods:

• High angular and spatial resolution diffusion MRI was employed.
• Orientation distribution functions (ODFs) were generated to capture microstructural features.
• Spherical harmonic decomposition and spectral k-mean clustering were used for subfield identification.

Main Results:

• The method successfully identified three distinct amygdala subfields in 32 healthy volunteers.
• Identified regions include medial, posterior-superior lateral, and anterior-inferior lateral amygdala.
• The approach demonstrated objective and efficient assessment of amygdala subfields.

Conclusions:

• This novel diffusion MRI technique enables precise in vivo identification of amygdala subnuclei.
• The findings provide a valuable tool for studying the role of amygdala subfields in emotional processing.
• This method advances neuroimaging capabilities for human brain anatomy research.