Hemispheric multi-dimension features extraction analysis based on decoupled representation learning

The predominant approach in investigating brain structural asymmetry relies on predefined regions of interest, assessing variations between homologous brain regions through a single indicator, which is local, univariate, and relative. In response to this challenge, we employ decoupled representation learning from deep learning to extract hidden features containing hemisphere-specific information at a hemispheric systemic level. This novel approach enables a global and multivariate analysis of brain structural asymmetry. Our findings indicate a significant association between left-hemisphere-specific hidden features and language-related behavioral metrics, as well as a correlation between right-hemisphere-specific hidden features and social-related behavioral metrics. Tensor-based Morphometry results find the impact of left-hemisphere-specific features on the left inferior frontal sulcus within Brocas area, a crucial region for language processing. Additionally, right-hemisphere-specific features influenced the right rostral hippocampus, a region implicated in emotion regulation and spatial navigation. The findings from Neurosynth indicate that significant regions caused by left-hemisphere-specific features are correlated with language, while significant regions caused by right-hemisphere-specific features are associated with behaviors primarily governed by the right hemisphere. Furthermore, our study establishes a link between structural changes induced by hemisphere-specific features and several genes. Such findings demonstrate that the application of deep learning techniques allows for precise capture of hemisphere-specific information within individual hemispheres, offering a new perspective for future research on brain structural asymmetry.