Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.

Imaging neuroscience (Cambridge, Mass.) Pub Date : 2025-09-24 eCollection Date: 2025-01-01 DOI:10.1162/IMAG.a.162
Kirsten L Peterson, Ruben Sanchez-Romero, Ravi D Mill, Michael W Cole
{"title":"Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.","authors":"Kirsten L Peterson, Ruben Sanchez-Romero, Ravi D Mill, Michael W Cole","doi":"10.1162/IMAG.a.162","DOIUrl":null,"url":null,"abstract":"<p><p>Functional connectivity (FC) has been invaluable for understanding the brain's communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives-graphical lasso, graphical ridge, and principal component regression-against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion-common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461088/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Imaging neuroscience (Cambridge, Mass.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/IMAG.a.162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Functional connectivity (FC) has been invaluable for understanding the brain's communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives-graphical lasso, graphical ridge, and principal component regression-against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion-common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

正则化的部分相关提供了可靠的功能连接估计,同时纠正了广泛的混淆。
功能连接(FC)对于理解大脑的通信网络具有不可估量的价值,增强的FC方法具有强大的潜力,可以产生更多的见解。与fMRI场标准的两两相关方法不同,理论表明,部分相关可以在没有混淆和间接连接的情况下估计FC。然而,部分相关FC也可以显示低重复信度,损害个人估计的准确性。我们假设通过添加正则化可以提高可靠性,这可以减少部分相关等基于回归的方法对噪声的过拟合。因此,我们测试了几种正则化的替代方法——图形套索、图形脊和主成分回归——来对抗非正则化的偏相关和两两相关,并将它们应用于经验静息状态fMRI和模拟数据。正如假设的那样,正则化极大地提高了可靠性,使用会话间相似性和类内相关性进行量化。当根据结构连通性(经验数据)和地面真值网络(模拟)进行验证时,这种增强的可靠性可以大大提高个人FC估计的准确性。图形lasso在正则化方法中显示出特别高的准确性,似乎通过维护更有效的底层网络结构。我们还发现图形套索对噪声水平、数据量和受试者运动(常见的fMRI误差来源)具有鲁棒性。最后,我们证明了静息状态图形lasso FC可以有效地预测fMRI任务激活和个体行为差异,进一步建立了其可靠性、外部效度和表征任务相关功能的能力。我们推荐图形套索或类似的正则化方法来计算FC,因为它们可以比现场标准的两两相关产生更有效的无混淆连通性估计,同时克服了非正则化部分相关的差可靠性。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信