Common and distinct roles of amygdala subregional functional connectivity in non-motor symptoms of Parkinson's disease.

Neuroimaging studies suggest a pivotal role of amygdala dysfunction in non-motor symptoms (NMS) of Parkinson's disease (PD). However, the relationship between amygdala subregions (the centromedial (CMA), basolateral (BLA) and superficial amygdala (SFA)) and NMS has not been delineated. We used resting-state functional MRI to examine the PD-related alterations in functional connectivity for amygdala subregions. The left three subregions and right BLA exhibited between-group differences, and were commonly hypo-connected with the frontal, temporal, insular cortex, and putamen in PD. Each subregion displayed distinct hypoconnectivity with the limbic systems. Partial least-squares analysis revealed distinct amygdala subregional involvement in diverse NMS. Hypo-connectivity of all four subregions was associated with emotion, pain, olfaction, and cognition. Hypo-connectivity of the left SFA was associated with sleepiness. Our findings highlight the hypofunction of the amygdala subregions in PD and their preliminary associations with NMS, providing new insights into the pathogenesis of NMS.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.