PSYCH OpenIR  > 健康与遗传心理学研究室
A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction
Tu, Y. H.1,2,3,4; Fu, Z. N.1,2; Tan, A.2; Huang, G.1; Hu, L.5; Hung, Y. S.2; Zhang, Z. G.1
2018-01-17
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号273期号:0页码:373-384
摘要

Dimension reduction is essential in fMRI decoding, but the complex relationship between fMRI data and class labels is often unknown or not well modeled so that the most effective dimension reduction (e.d.r.) directions can hardly be identified. In the present study, we introduce a novel fMRI decoding approach based on an effective and general dimension reduction method, namely sliced inverse regression (SIR), which can exploit class information for estimating e.d.r. directions even when the relationship between fMRI data and class labels is not explicitly known. We incorporate singular value decomposition (SVD) into SIR to overcome SIR's limitation in dealing with ultra-high-dimensional data, and integrate SVD-SIR into a pattern classifier to enable quantification of the contributions of fMRI voxels to class labels. The resultant new SIR decoding analysis (SIR-DA) approach is capable of decoding behavioral responses and identifying predictive fMRI patterns. Simulation results showed that SIR-DA can more accurately detect e.d.r. directions and achieve higher classification accuracy than decoding approaches based on conventional dimension reduction methods. Further, we applied SIR-DA on real-world pain-evoked fMRI data to decode the level of pain perception and showed that SIR-DA can achieve higher accuracy in pain prediction than conventional methods. These results suggest that SIR-DA is an effective data-driven technique to decode behavioral or cognitive states from fMRI data and to uncover unknown brain patterns associated with behavior or cognitive responses. (C) 2017 Elsevier B.V. All rights reserved.

关键词Fmri Decoding Dimension Reduction Sliced Inverse Regression Pain Prediction
DOI10.1016/j.neucom.2017.07.045
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61640002) ; Shenzhen Peacock Plan(KQTD2016053112051497) ; Hong Kong RGC GRF(HKU 785913 M)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000414762100036
WOS标题词Science & Technology ; Technology
关键词[WOS]MULTIVARIATE PATTERN-ANALYSIS ; DIMENSION REDUCTION ; DATA VISUALIZATION ; BRAIN RESPONSES ; PERCEPTION ; NETWORK ; DYNAMICS ; MODEL
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/22005
专题健康与遗传心理学研究室
作者单位1.Shenzhen Univ, Sch Biomed Engn, Hlth Sci Ctr, Shenzhen, Peoples R China
2.Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Hong Kong, Peoples R China
3.Massachusetts Gen Hosp, Dept Psychiat, Charlestown, MA USA
4.Harvard Med Sch, Charlestown, MA USA
5.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Tu, Y. H.,Fu, Z. N.,Tan, A.,et al. A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction[J]. NEUROCOMPUTING,2018,273(0):373-384.
APA Tu, Y. H..,Fu, Z. N..,Tan, A..,Huang, G..,Hu, L..,...&Zhang, Z. G..(2018).A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction.NEUROCOMPUTING,273(0),373-384.
MLA Tu, Y. H.,et al."A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction".NEUROCOMPUTING 273.0(2018):373-384.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
A novel and effectiv(3633KB)期刊论文出版稿限制开放CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Tu, Y. H.]的文章
[Fu, Z. N.]的文章
[Tan, A.]的文章
百度学术
百度学术中相似的文章
[Tu, Y. H.]的文章
[Fu, Z. N.]的文章
[Tan, A.]的文章
必应学术
必应学术中相似的文章
[Tu, Y. H.]的文章
[Fu, Z. N.]的文章
[Tan, A.]的文章
相关权益政策
暂无数据
收藏/分享
文件名: A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。