Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-07-30 DOI:10.1007/s12021-024-09680-8
Rachel Edelstein, Sterling Gutterman, Benjamin Newman, John Darrell Van Horn
{"title":"Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?","authors":"Rachel Edelstein, Sterling Gutterman, Benjamin Newman, John Darrell Van Horn","doi":"10.1007/s12021-024-09680-8","DOIUrl":null,"url":null,"abstract":"<p><p>Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroinformatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12021-024-09680-8","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
评估女运动员的运动性脑震荡:神经信息学的作用?
在过去的十年中,女性运动员中与运动相关的脑震荡的复杂性已变得显而易见。传统的脑震荡临床诊断方法在应用于女运动员时存在局限性,往往无法捕捉到大脑结构和功能的细微变化。先进的神经信息学技术和机器学习模型已成为这方面的宝贵财富。虽然这些技术已被广泛应用于了解男性运动员的脑震荡情况,但我们对其对女性运动员的有效性的理解仍有很大差距。凭借出色的数据分析能力,机器学习为弥补这一不足提供了一条大有可为的途径。通过利用机器学习的强大功能,研究人员可以将观察到的表型神经影像数据与性别特异性生物机制联系起来,从而揭开女运动员脑震荡的神秘面纱。此外,在机器学习中嵌入方法,可以超越传统的解剖参考框架,检查大脑结构及其变化。反过来,研究人员也能更深入地了解脑震荡的动态变化、治疗反应和恢复过程。本文致力于解决多模态神经成像实验设计和机器学习方法在女性运动员群体中的性别差异这一关键问题,最终确保她们在面对脑震荡挑战时获得所需的定制护理。通过更好的数据整合、特征识别、知识表示、验证等,神经信息学家非常适合为男性和女性运动相关头部损伤的研究带来清晰度、背景和可解释性,并帮助确定康复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury. Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value. A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1