Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience
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...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1557879/full |
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| author | Yubin Huang Jun Liu Qi Yu |
| author_facet | Yubin Huang Jun Liu Qi Yu |
| author_sort | Yubin Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-23db619da9f9401e87ff9afad1690fec |
| institution | Kabale University |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-23db619da9f9401e87ff9afad1690fec2025-08-20T03:44:11ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-08-011910.3389/fninf.2025.15578791557879Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscienceYubin Huang0Jun Liu1Qi Yu2Department of Rehabilitation Medicine, Ganzhou People's Hospital, Ganzhou, ChinaGanzhou People's Hospital, Ganzhou, ChinaCollege of Art, Shaanxi University of Technology, Hanzhong, Shaanxi, ChinaIntroductionUnderstanding 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.https://www.frontiersin.org/articles/10.3389/fninf.2025.1557879/fullneuroinformaticscognitive phenotypeselite athletessystems neurosciencedeep learning |
| spellingShingle | Yubin Huang Jun Liu Qi Yu Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience Frontiers in Neuroinformatics neuroinformatics cognitive phenotypes elite athletes systems neuroscience deep learning |
| title | Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| title_full | Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| title_fullStr | Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| title_full_unstemmed | Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| title_short | Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| title_sort | leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience |
| topic | neuroinformatics cognitive phenotypes elite athletes systems neuroscience deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1557879/full |
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