Volume 14 Number 4
August 2017
Article Contents
Nie Lei, Yang Xian, M. Matthews Paul, Xu Zhi-Wei and Guo Yi-Ke. Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation. International Journal of Automation and Computing, vol. 14, no. 4, pp. 371-385, 2017. doi: 10.1007/s11633-017-1084-9
Cite as: Nie Lei, Yang Xian, M. Matthews Paul, Xu Zhi-Wei and Guo Yi-Ke. Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation. International Journal of Automation and Computing, vol. 14, no. 4, pp. 371-385, 2017.

Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation

Author Biography:
• Lei Nie received the B. Sc. degree in information and computing science from Sichuan University, China in 2009, and received the Ph. D. degree in computer science and technology from University of the Chinese Academy of Sciences, China in 2015. He was a visiting Ph. D. student in the Department of Computing, Imperial College London, UK from 2012 to 2014. He is currently a research associate in the Department of Computing, Imperial College London, UK.
His research interests include neuroimaging and machine learning.
E-mail:l.nie13@imperial.ac.uk,
ORCID iD:0000-0003-1115-5027

Xian Yang received the B. Eng. degree in electronic information engineering from Huazhong University of Science and Technology, China in 2008, received the M. Sc. degree in digital communication from University of Bath, UK in 2009, and received the Ph. D. degree in computing from Imperial College London, UK in 2016. She is currently a research associate in the Department of Computing, Imperial College London, UK.
Her research interests include bioinformatics, system biology, neuroimaging, data mining and health informatics.
E-mail:xian.yang08@imperial.ac.uk

Paul M. Matthews received the B. A. degree in chemistry from University of Oxford, UK in 1978, the D. Phil degree in biochemistry from University of Oxford, UK in 1982, and the M. D. degree from Stanford University School of Medicine, USA in 1987. He is the Edmond and Lily Safra Chair and head of the Division of Brain Sciences at Imperial College London, UK. Amongst other external activities, he is chair of the Imaging Enhancement Working Group and a member of the steering group for UK Biobank (https://www.ukbiobank.ac.uk/), which has initiated a programme to image the brain, heart, carotids, bones and body of 100 000 people to understand disease risk in later life. He was the founding director of the Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) (http://www.fmrib.ox.ac.uk/) and of the GSK Clinical Imaging Centre at the Hammersmith Hospital, for which he was a lead in spinning out Imanova Ltd., which is run as a public-private partnership between Imperial College, UCL, Kings College and the Medical Research Council (http://www.imanova.co.uk/).
His research interests include innovative translational applications of clinical imaging for the neurosciences. His broad area of research interest has been in molecular and functional neuroimaging and in neurological therapeutics development. A particular focus of his work has involved close collaboration with colleagues in computing and engineering to encourage effective translation of advanced imaging data to information.
E-mail:p.matthews@imperial.ac.uk

Zhi-Wei Xu received the B. Sc. degree from University of Electronic Science and Technology of China, in 1982, received the M. Sc. degree from Purdue University, USA in 1984, and received the Ph. D. degree from the University of Southern California, USA in 1987. He is a professor and CTO of the Institute of Computing Technology (ICT) of the Chinese Academy of Sciences (CAS). His prior industrial experience included chief engineer of Dawning Corporation (now Sugon as listed in Shanghai Stock Exchange), a leading high-performance computer vendor in China. He currently leads "Cloud-Sea Computing Systems", a strategic priority research project of the Chinese Academy of Sciences that aims at developing billion-thread computers with elastic processors.
His research interests include high-performance computer architecture and network computing science.
E-mail:zxu@ict.ac.cn

• Corresponding author: Yi-Ke Guo received the B. Sc. degree, the M. Sc. degree in computer science and technology from Tsinghua University, China in 1985, and the Ph. D. degree in computational logic from Imperial College London, UK in 1993. He is the founding director of the Data Science Institute, Imperial College London, UK. He is also a professor in the Department of Computing, Imperial College London, UK. During last 15 years, he has been leading a data science group to carry out many research projects, including discovery net on grid based data analysis for scientific discovery, MESSAGE on wireless mobile sensor network for environment monitoring, BAIR on system biology for diabetes study, iHealth on modern informatics infrastructure for healthcare decision making, UBIOPRED on large informatics platform for translational medicine research, digital city exchange on sensor information-based urban dynamics modelling, IC Cloud system for large scale collaborative scientific research. He is now the principal investigator of the eTRIKS project, a 23M Euro project in building a cloud-based translational informatics platform for global medical research.
His research interests include data mining, machine learning and bioinformatics
E-mail:y.guo@imperial.ac.uk (Corresponding author)
ORCID iD:0000-0002-3075-2161
• Accepted: 2017-02-21
• Published Online: 2017-06-13
• Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearson s correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berkson s paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging (fMRI). The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms others in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project.
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Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation

• Corresponding author:Yi-Ke Guo received the B. Sc. degree, the M. Sc. degree in computer science and technology from Tsinghua University, China in 1985, and the Ph. D. degree in computational logic from Imperial College London, UK in 1993. He is the founding director of the Data Science Institute, Imperial College London, UK. He is also a professor in the Department of Computing, Imperial College London, UK. During last 15 years, he has been leading a data science group to carry out many research projects, including discovery net on grid based data analysis for scientific discovery, MESSAGE on wireless mobile sensor network for environment monitoring, BAIR on system biology for diabetes study, iHealth on modern informatics infrastructure for healthcare decision making, UBIOPRED on large informatics platform for translational medicine research, digital city exchange on sensor information-based urban dynamics modelling, IC Cloud system for large scale collaborative scientific research. He is now the principal investigator of the eTRIKS project, a 23M Euro project in building a cloud-based translational informatics platform for global medical research.     His research interests include data mining, machine learning and bioinformatics     E-mail:y.guo@imperial.ac.uk (Corresponding author)    ORCID iD:0000-0002-3075-2161

Abstract: Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearson s correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berkson s paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging (fMRI). The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms others in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project.

Nie Lei, Yang Xian, M. Matthews Paul, Xu Zhi-Wei and Guo Yi-Ke. Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation. International Journal of Automation and Computing, vol. 14, no. 4, pp. 371-385, 2017. doi: 10.1007/s11633-017-1084-9
 Citation: Nie Lei, Yang Xian, M. Matthews Paul, Xu Zhi-Wei and Guo Yi-Ke. Inferring Functional Connectivity in fMRI Using Minimum Partial Correlation. International Journal of Automation and Computing, vol. 14, no. 4, pp. 371-385, 2017.
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