Automated diffusion-based parcellation of the hypothalamus reveals subunit-specific associations with obesity.

The hypothalamus is a small, yet highly versatile structure mainly involved in bodily functions such as control of food intake and endocrine activity. Functional anatomy of different hypothalamic areas is mainly investigated using structural MRI, validated by ex-vivo histological studies. Based on diffusion-weighted imaging (DWI), recent automated clustering methods provide robust tools for parcellation. Using data of 100 healthy adults provided by the Human Connectome Project Database, we applied DWI-based automated clustering to the hypothalamus and related microstructural properties in these hypothalamic compartments to obesity. Our results suggest that the hypothalamus can be reliably partitioned into four clusters in each hemisphere using diffusion-based parcellation. These correspond to an anterior-superior, anterior-inferior, intermediate, and posterior cluster. Obesity was predicted by mean diffusivity of the anterior-superior cluster, suggesting altered inhibition of food intake. The proposed method provides an automated hypothalamic parcellation technique based on DWI data to explore anatomy and function of hypothalamic subunits in vivo in humans.