The pituitary gland is a critical endocrine organ that regulates numerous vital bodily functions, including growth, metabolism, reproduction, and stress response. Disorders of the pituitary gland can lead to significant clinical symptoms due to hormone imbalances or structural abnormalities. While previous studies have explored correlations between pituitary gland and health conditions, such as obesity, challenges remain in accurately capturing functional signals due to image distortions and contamination from surrounding structures. This study aimed to address these limitations by improving the extraction of functional signals from the pituitary gland using advanced methods. We employed a two-step approach: implementing the recently developed BOLD-filter to reduce noise and extracting signals at the individual voxel level in native 2D functional image space, avoiding the distortions introduced by normalization and smoothing in 3D space. Our results demonstrated that the BOLD-filter effectively minimized noise in resting-state fMRI data and that signal extraction at 2D native space produced higher-quality functional connectivity maps of the pituitary gland with other brain areas. These findings highlight the utility of our strategy in advancing the study of pituitary gland function and its potential for broader applications in health monitoring and disease diagnosis.
Sung et al. (Mon,) studied this question.