Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience - Huang, Yubin; Liu, Jun; Yu, Qi (Frontiers in Neuroinformatics, 2025)
IntroductionUnderstanding the cognitive phenotypes of elite athletes offers a unique perspective on the intricate interplay between neurological traits and high-performance behaviors. This study aligns with advancing neuroinformatics by proposing a novel framework designed to capture and analyze the multi-dimensional dependencies of cognitive phenotypes using systems neuroscience methodologies. Traditional approaches often face limitations in disentangling the latent factors influencing cognitive variability or in preserving interpretable data structures.MethodsTo address these challenges, we developed the Latent Cognitive Embedding Network (LCEN), an innovative model that combines biologically inspired constraints with state-of-the-art neural architectures. The model features a specialized embedding mechanism for disentangling latent factors and a tailored optimization strategy incorporating domain-specific priors and regularization techniques.ResultsExperimental evaluations demonstrate LCEN's superiority in predicting and interpreting cognitive phenotypes across diverse datasets, providing deeper insights into the neural underpinnings of elite performance.DiscussionThis work bridges computational modeling, neuroscience, and psychology, contributing to the broader understanding of cognitive variability in specialized populations.