Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy

doi:10.1016/j.jneumeth.2020.108836

	Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy
	Choupan, Jeiran 1,2,3,4; Douglas, Pamela K.6,7,8; Gal, Yaniv 5; Cohen, Mark S.9,10,11,12,13,14,15,16; Reutens, David C.1; Yang, Zhengyi1,5,17
发表期刊	JOURNAL OF NEUROSCIENCE METHODS
ISSN	0165-0270
	2020-11-01
卷号	345 页码:13
通讯作者	Choupan, Jeiran(choupan@usc.edu)
摘要	Background: In fMRI decoding, temporal embedding of spatial features of the brain allows the incorporation of brain activity dynamics into the multivariate pattern classification process, and provides enriched information about stimulus-specific response patterns and potentially improved prediction accuracy. New method: This study investigates the possibility of enhancing the classification performance by exploring temporal embedding, to identify the optimum combination of spatiotemporal features based on their classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby study (Haxby et al., 2001). Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 s. Comparison with existing methods: A wide range of spatiotemporal observations were created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers prediction accuracies for these combinations were then compared with the single spatial multivariate pattern approach that uses only a single temporal observation. Results: Our findings showed that, on average, spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent. Conclusions: As expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until - 4 s after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design optimal approaches that incorporate spatiotemporal dependencies into feature selection for decoding.
关键词	fMRI Multi-variate pattern analysis Spatiotemporal feature selection Multiband EPI Random forest Support vector machine
DOI	10.1016/j.jneumeth.2020.108836
关键词[WOS]	SUPPORT VECTOR MACHINES ; FMRI ; REPRESENTATIONS ; CLASSIFICATION ; RESPONSES ; STIMULUS ; DISCRIMINATION ; EXPECTATION ; INFORMATION ; REGRESSION
收录类别	SCI
语种	英语
资助项目	W.M. Keck Foundation ; National Institute of Health[5T90DA022768] ; Staglin Center for Center for Cognitive Neuroscience ; University of Queensland International PhD Scholarship
项目资助者	W.M. Keck Foundation ; National Institute of Health ; Staglin Center for Center for Cognitive Neuroscience ; University of Queensland International PhD Scholarship
WOS研究方向	Biochemistry & Molecular Biology ; Neurosciences & Neurology
WOS类目	Biochemical Research Methods ; Neurosciences
WOS记录号	WOS:000580629000001
出版者	ELSEVIER
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42142
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Choupan, Jeiran
作者单位	1.Univ Queensland, Ctr Adv Imaging, Brisbane, Qld, Australia 2.Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia 3.Univ Southern Calif, USC Dornsife Coll Letters Arts & Sci, Dept Psychol, Los Angeles, CA 90007 USA 4.Univ Southern Calif, Keck Sch Med, USC Stevens Neuroimaging & Informat Inst, Lab Neuro Imaging, Los Angeles, CA 90007 USA 5.Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia 6.Univ Calif Los Angeles, Ctr Cognit Neurosci, Los Angeles, CA USA 7.UCF, Modeling & Simulat Dept, Orlando, FL USA 8.UCF, Comp Sci Dept, Orlando, FL USA 9.Univ Calif Los Angeles, Inst Neuropsychiat, 760 Westwood Plaza, Los Angeles, CA 90024 USA 10.Univ Calif Los Angeles, Dept Psychiat & Behav Sci, Los Angeles, CA USA 11.Univ Calif Los Angeles, Dept Neurol, Los Angeles, CA 90024 USA 12.Univ Calif Los Angeles, Dept Radiol Sci, Los Angeles, CA 90024 USA 13.Univ Calif Los Angeles, Dept Biomed Phys, Los Angeles, CA USA 14.Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA USA 15.Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA USA 16.Univ Calif Los Angeles, Sch Med, Calif Nanosyst Inst, Los Angeles, CA USA 17.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Choupan, Jeiran,Douglas, Pamela K.,Gal, Yaniv,et al. Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy[J]. JOURNAL OF NEUROSCIENCE METHODS,2020,345:13.
APA	Choupan, Jeiran,Douglas, Pamela K.,Gal, Yaniv,Cohen, Mark S.,Reutens, David C.,&Yang, Zhengyi.(2020).Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy.JOURNAL OF NEUROSCIENCE METHODS,345,13.
MLA	Choupan, Jeiran,et al."Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy".JOURNAL OF NEUROSCIENCE METHODS 345(2020):13.