Functionally specialized spectral organization of the resting human cortex

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2025-01-27 DOI:10.1016/j.neunet.2025.107195
Wenjun Bai , Okito Yamashita , Junichiro Yoshimoto
{"title":"Functionally specialized spectral organization of the resting human cortex","authors":"Wenjun Bai ,&nbsp;Okito Yamashita ,&nbsp;Junichiro Yoshimoto","doi":"10.1016/j.neunet.2025.107195","DOIUrl":null,"url":null,"abstract":"<div><div>Ample studies across various neuroimaging modalities have suggested that the human cortex at rest is hierarchically organized along the spectral and functional axes. However, the relationship between the spectral and functional organizations of the human cortex remains largely unexplored. Here, we reveal the confluence of functional and spectral cortical organizations by examining the functional specialization in spectral gradients of the cortex. These spectral gradients, derived from functional magnetic resonance imaging data at rest using our temporal de-correlation method to enhance spectral resolution, demonstrate regional frequency biases. The grading of spectral gradients across the cortex – aligns with many existing brain maps – is found to be highly functionally specialized through discovered frequency-specific resting-state functional networks, functionally distinctive spectral profiles, and an intrinsic coordinate system that is functionally specialized. By demonstrating the functionally specialized spectral gradients of the cortex, we shed light on the close relation between functional and spectral organizations of the resting human cortex.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"185 ","pages":"Article 107195"},"PeriodicalIF":6.3000,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025000747","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Ample studies across various neuroimaging modalities have suggested that the human cortex at rest is hierarchically organized along the spectral and functional axes. However, the relationship between the spectral and functional organizations of the human cortex remains largely unexplored. Here, we reveal the confluence of functional and spectral cortical organizations by examining the functional specialization in spectral gradients of the cortex. These spectral gradients, derived from functional magnetic resonance imaging data at rest using our temporal de-correlation method to enhance spectral resolution, demonstrate regional frequency biases. The grading of spectral gradients across the cortex – aligns with many existing brain maps – is found to be highly functionally specialized through discovered frequency-specific resting-state functional networks, functionally distinctive spectral profiles, and an intrinsic coordinate system that is functionally specialized. By demonstrating the functionally specialized spectral gradients of the cortex, we shed light on the close relation between functional and spectral organizations of the resting human cortex.
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
静止的人类皮层的功能特化的光谱组织。
各种神经成像模式的大量研究表明,休息时的人类皮层是沿着光谱和功能轴分层组织的。然而,人类皮层的光谱和功能组织之间的关系在很大程度上仍未被探索。在这里,我们通过检查皮层光谱梯度中的功能专门化来揭示功能和光谱皮层组织的融合。这些频谱梯度来自于静态功能磁共振成像数据,使用我们的时间去相关方法来提高光谱分辨率,显示了区域频率偏差。通过发现特定频率的静息状态功能网络、功能独特的光谱轮廓和功能专门化的内在坐标系统,我们发现,整个皮层的光谱梯度分级——与许多现有的大脑图谱一致——具有高度的功能专门化。通过展示皮层的功能专门化光谱梯度,我们揭示了静息人类皮层的功能和光谱组织之间的密切关系。
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
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
期刊最新文献
Incremental multi-subreservoirs echo state network control for uncertain aeration process. CMMDL: Cross-modal multi-domain learning method for image fusion. RepAttn3D: Re-parameterizing 3D attention with spatiotemporal augmentation for video understanding. Continual few-shot named entity recognition against catastrophic forgetting and overfitting. Locally adaptive compensation for analytic class-incremental learning.
×
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:604180095
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