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 | |
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 |
2018-01-17 | |
语种 | 英语 |
DOI | 10.1016/j.neucom.2017.07.045 |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 273期号:0页码:373-384 |
期刊论文类型 | Article |
收录类别 | SCI |
WOS关键词 | MULTIVARIATE PATTERN-ANALYSIS ; DIMENSION REDUCTION ; DATA VISUALIZATION ; BRAIN RESPONSES ; PERCEPTION ; NETWORK ; DYNAMICS ; MODEL |
WOS标题词 | Science & Technology ; Technology |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000414762100036 |
资助机构 | National Natural Science Foundation of China(61640002) ; Shenzhen Peacock Plan(KQTD2016053112051497) ; Hong Kong RGC GRF(HKU 785913 M) |
引用统计 | |
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. |
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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 | 请求全文 |
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