BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Annals of Applied Statistics Pub Date : 2022-09-01 Epub Date: 2022-07-19 DOI:10.1214/21-aoas1562
Guoqing Wang, Abhirup Datta, Martin A Lindquist
{"title":"BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS.","authors":"Guoqing Wang, Abhirup Datta, Martin A Lindquist","doi":"10.1214/21-aoas1562","DOIUrl":null,"url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization <i>after</i> anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subjects functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework, and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312483/pdf/nihms-1910200.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/21-aoas1562","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/19 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0

Abstract

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subjects functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework, and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

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
FMRI 激活图的贝叶斯功能配准。
功能磁共振成像(fMRI)为我们了解人类行为提供了宝贵的洞察力。然而,解剖配准后大脑解剖和功能定位方面的巨大个体间差异仍然是进行群体分析和群体推断的主要限制因素。本文针对这一问题,开发并验证了一种新的计算技术,通过将每个受试者的功能数据空间转换到一个共同的参考图,减少大脑功能系统中的个体间错位。我们提出的贝叶斯功能配准方法允许我们评估不同受试者大脑功能的差异以及激活拓扑的个体差异。它将基于强度的信息和基于特征的信息整合到一个综合框架中,并允许通过后验样本对转换进行推断。我们在一项模拟研究中对该方法进行了评估,并将其应用于一项热痛研究的数据中。我们发现,所提出的方法提高了组级推断的灵敏度。
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
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
期刊最新文献
PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK. A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19. A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation. BIVARIATE FUNCTIONAL PATTERNS OF LIFETIME MEDICARE COSTS AMONG ESRD PATIENTS. EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN.
×
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