初潮、青春期时间与大脑:超越年龄相关发育的女性特有大脑成熟模式。

IF 4.9 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Biology of Sex Differences Pub Date : 2024-03-26 DOI:10.1186/s13293-024-00604-4
Nina Gottschewsky, Dominik Kraft, Tobias Kaufmann
{"title":"初潮、青春期时间与大脑:超越年龄相关发育的女性特有大脑成熟模式。","authors":"Nina Gottschewsky, Dominik Kraft, Tobias Kaufmann","doi":"10.1186/s13293-024-00604-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.</p><p><strong>Methods: </strong>We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status.</p><p><strong>Results: </strong>The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche.</p><p><strong>Conclusions: </strong>This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.</p>","PeriodicalId":8890,"journal":{"name":"Biology of Sex Differences","volume":"15 1","pages":"25"},"PeriodicalIF":4.9000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964568/pdf/","citationCount":"0","resultStr":"{\"title\":\"Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development.\",\"authors\":\"Nina Gottschewsky, Dominik Kraft, Tobias Kaufmann\",\"doi\":\"10.1186/s13293-024-00604-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.</p><p><strong>Methods: </strong>We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status.</p><p><strong>Results: </strong>The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche.</p><p><strong>Conclusions: </strong>This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.</p>\",\"PeriodicalId\":8890,\"journal\":{\"name\":\"Biology of Sex Differences\",\"volume\":\"15 1\",\"pages\":\"25\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964568/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biology of Sex Differences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13293-024-00604-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology of Sex Differences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13293-024-00604-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

摘要

背景:青春期是一个从社会因素到生物因素发生深刻而多因素变化的时期。虽然人们大多从年龄的角度研究青少年的大脑发育,但最近的证据表明,青春期测量可能对研究青少年的神经发育更敏感,然而,有关青春期时间与大脑发育关系的研究仍然很少:我们研究了是否可以利用机器学习对青少年脑认知发展(ABCD)队列中年龄严格匹配的青少年女性的皮层和皮层下结构磁共振成像(MRI)数据进行初潮前与初潮后状态的分类。为了将已确定的初潮相关模式与神经发育的年龄相关模式进行比较,我们在费城神经发育队列的数据上训练了一个脑年龄预测模型,并将其应用于相同的 ABCD 数据,得出了预测年龄与实际年龄之间的差异,即脑年龄差距。我们测试了这两个框架对青春期成熟度测量的敏感性,特别是月经初潮年龄和青春期状态:结果:机器学习模型在月经初潮分类任务中取得了中等但具有统计学意义的准确性,每个受试者的分类概率从 0(月经初潮前)到 1(月经初潮后)不等。与脑年龄预测进行比较后发现,两种方法都能捕捉到神经发育的共同和独特模式。连续初潮等级概率与脑年龄差距呈正相关,但只有初潮等级概率而非脑年龄差距与初潮年龄相关:本研究展示了如何利用机器学习模型从结构性核磁共振成像数据中对月经初潮状态进行分类,同时考虑到与年龄相关的神经发育。鉴于该模型对青春期时间测量的敏感性,我们的研究表明,月经初潮等级概率可发展成为基于大脑的青春期发育客观标记。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development.

Background: Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.

Methods: We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status.

Results: The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche.

Conclusions: This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biology of Sex Differences
Biology of Sex Differences ENDOCRINOLOGY & METABOLISM-GENETICS & HEREDITY
CiteScore
12.10
自引率
1.30%
发文量
69
审稿时长
14 weeks
期刊介绍: Biology of Sex Differences is a unique scientific journal focusing on sex differences in physiology, behavior, and disease from molecular to phenotypic levels, incorporating both basic and clinical research. The journal aims to enhance understanding of basic principles and facilitate the development of therapeutic and diagnostic tools specific to sex differences. As an open-access journal, it is the official publication of the Organization for the Study of Sex Differences and co-published by the Society for Women's Health Research. Topical areas include, but are not limited to sex differences in: genomics; the microbiome; epigenetics; molecular and cell biology; tissue biology; physiology; interaction of tissue systems, in any system including adipose, behavioral, cardiovascular, immune, muscular, neural, renal, and skeletal; clinical studies bearing on sex differences in disease or response to therapy.
期刊最新文献
Sex differences in the human brain related to visual motion perception. A call for inclusive research, policies, and leadership to close the global women's health gap. Sex differences in contextual fear conditioning and extinction after acute and chronic nicotine treatment. Sex dimorphism and tissue specificity of gene expression changes in aging mice. The Four Core Genotypes mouse model: evaluating the impact of a recently discovered translocation.
×
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