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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
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume273Issue:0Pages:373-384
Abstract

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.

KeywordFmri Decoding Dimension Reduction Sliced Inverse Regression Pain Prediction
DOI10.1016/j.neucom.2017.07.045
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61640002) ; Shenzhen Peacock Plan(KQTD2016053112051497) ; Hong Kong RGC GRF(HKU 785913 M)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000414762100036
WOS HeadingsScience & Technology ; Technology
WOS KeywordMULTIVARIATE PATTERN-ANALYSIS ; DIMENSION REDUCTION ; DATA VISUALIZATION ; BRAIN RESPONSES ; PERCEPTION ; NETWORK ; DYNAMICS ; MODEL
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/22005
Collection健康与遗传心理学研究室
Affiliation1.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
Recommended Citation
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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