龙志颖
职称：副教授、博士生导师
研究领域 ：
电话：
通讯地址：北京师范大学京师大厦
邮编：100875
电子邮件：zlong＃bnu.edu.cn
课题组网址：

简 介
研究方向
研究人员
论文成果
图片


北京师范大学认知神经科学与学习国家重点实验室副教授。于2005年获北京师范大学心理学系基础心理学专业博士学位；1998年在北京师范大学获电子学学士学位。2005－2007年在美国加州大学圣地亚哥分校做博士后。主要研究方向为：基于磁共振成像的脑状态编解码计算模型的研究、磁共振成像的方法学研究、认知老化的动态脑网络研究。
主持的项目有：
1. 基于功能磁共振成像的三维运动编解码计算模型（**）国家自然科学基金面上项目，2017-2020，65万
2. 基于实时功能磁共振成像的脑状态解码研究(**) 国家自然科学基金面上项目，2013.1-2016.12， 82万
3. 独立成分分析法在功能磁共振成像数据分析中的若干问题(**), 国家自然科学基金青年基金，2009-2011, 20万
发表的文章
Li Y, Hou C, Yao L, Zhang C, Zheng H, Zhang J, Long Z*. Disparity level identification using the voxel-wise Gabor model of fMRI data. Hum Brain Mapp. 2019 Jun 15;40(9):2596-2610. doi: 10.1002/hbm.24547.
Long Z, Liu L, Gao Z, Chen M, Yao L. A semi-blind online dictionary learning approach for fMRI data. J Neurosci Methods. 2019 Jul 15;323:1-12.
Long Z, Wang Y, Liu X, Yao L. Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging. PLoS One. 2019 Apr 10;14(4):e**. doi: 10.1371/journal.pone.**.
Long Z., Wang Z., Zhang J., Zhao X., Yao L., BMC Medical Imaging (2019) Temporally constrained ICA with threshold and its application to fMRI data vol. 19:6 https://doi.org/10.1186/s12880-018-0300-6
Zhang, J., Zhang, C., Yao, L., Zhao, X., and Long, Z*.(2018). Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications. Computational Intelligence and Neuroscience. doi:10.1155/2018/**
Zhang C, Yao L, Song S, Wen X, Zhao X, Long Z.*(2018) Euler Elastica regularized Logistic Regression for whole-brain decoding of fMRI data, IEEE Trans Biomed Eng., vol. 65, page 1639-1653, PP. 655-673
Li, Y., Zhang, C., Hou, C., Yao L., Zhang J., Long Z*., (2017) BMC Neurosci , Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex, vol. 18: 80. https://doi.org/10.1186/s12868-017-0395-7
Wang Y, Zhang J, Zhang G, Yao L, Long Z* (2017), Changes in the brain’s intrinsic organization in the resting state with real-time fMRI neurofeedback training of PCC activity, Journal of Behavioral and Brain Science, Vol.7 No.13, PP. 655-673
Ge R,Wang Y,Zhang J,Yao L,Zhang H,Long Z*. Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources, Journal of Neuroscience Methods, 2016 Apr 1;263:103-14.
Zhang CC,Song TS,Wen X,Yao L,Long Z*(Apr 2015), Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data. Journal of Neuroscience Methods, 2015, 30(245):15-24
Ge RY,Yao L,Zhang H,Long Z*, (Sep 2015) A two-step super-Gaussian independent component analysis approach for fMRI data, Neuroimage, 2015, 118:344-58
Xia MG,Song ST,Yao L,Long Z*,(Aug 2015) An empirical comparison of different LDA methods in fMRI-based brain states decoding, Bio-medical Materials and Engineering,26 (2015) S1185–S1192
Ge R, Zhang H, Yao L,Long Z*, Motor Imagery Learning Induced Changes in Functional Connectivity of the Default Mode Network, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 23, NO. 1, (2015) pp: 138-148.
Hui M. Zhang H, Ge R, Yao L,Long Z*, Modulation of functional network with real-time fMRI feedback training of right premotor cortex activity, Neuropsychologia, 62 (2014) 111–123
Ge R, Fu Y, Wang D, Yao L andLong Z* (2014) Age-related alterations of brain network underlying the retrieval of emotional autobiographical memories: an fMRI study using independent component analysis.Front. Hum. Neurosci.8:629. doi: 10.3389/fnhum.2014.00629
Wang Z, Xia M, Jin Z, Yao L,Long Z* (2014) Temporally and Spatially Constrained ICA of fMRI Data Analysis. PLoS ONE 9(4): e94211. doi:10.1371/journal.pone.**
Wang Z, Xia M, Jin Z, Yao L,Long Z*, Temporally and Spatially Constrained ICA of fMRI Data Analysis, 2014, PLoS ONE 9(4): e94211.
Long Z, Li R, Chen K, Wen X, Jin Z, Yao L*, Separating 4D Multi-task fMRI Data of multiple subjects by Independent Component Analysis with Projection, Magnetic Resonance Imaging, 2013, Vol. 31(1), Pages 60–74.
Long Z, Li R, Hui M, Jin Z, Yao L*, An Improvement of Independent Component Analysis with Projection Method Applied to Multi-task fMRI Data, Computers in Biology and Medicine, 2013, Vol. 43(3):200-10 .
Zhang G, Zhang H, Li X, Zhao X, Yao L,Long Z*, Functional Alteration of the DMN by Learned Regulation of the PCC Using Real-Time fMRI, IEEE transactions on neural systems and rehabilitation engineering , 2013, vol. 21, 595－606.
Ma, X, Zhang H, Zhao X, Yao L,Long Z*, Semi-Blind Independent Component Analysis of fMRI Based on Real-Time fMRI System, IEEE transactions on neural systems and rehabilitation engineering, 2013, vol. 21, 416－426.
Hui M, Li R, Chen K, Jin Z, Yao L,Long Z* (2013) Improved Estimation of the Number of Independent Components for Functional Magnetic Resonance Data by a Whitening Filter, IEEE Journal of Biomedical and Health Informatics, vol. 17, 629－641.
Hui M, Li J, Wen X, Yao L,Long Z* (2011) An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data. PLoS ONE 6(12): e29274.
Zhang H, Xu L, Wang S, Xie B, Guo J,Long Z*, Yao L*. Behavioral improvements and brain functional alterations by motor imagery training. Brain Res, 2011; 1407: 38-46.
Long Z,Peng D, Chen K, Jin Z, Yao L*, Neural substrates in color processing: A comparison between painting majors and non-majors, Neuroscience Letters, 2011, 487: 191–195
Long Z, Chen K, Wu X, Reiman E, Peng D, Yao L，Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques, Human Brain Mapping 2009, 30(2): 417-31;