Estimation of resting-state functional connectivity using random subspace based partial correlation: A novel method for reducing global artifacts

Intrinsic functional connectivity analysis using resting-state functional magnetic resonance imaging (rsfMRI) has become a powerful tool for examining brain functional organization. Global artifacts such as physiological noise pose a significant problem in estimation of intrinsic functional connectivity. Here we develop and test a novel random subspace method for functional connectivity (RSMFC) that effectively removes global artifacts in rsfMRI data. RSMFC estimates the partial correlation between a seed region and each target brain voxel using multiple subsets of voxels sampled randomly across the whole brain. We evaluated RSMFC on both simulated and experimental rsfMRI data and compared its performance with standard methods that rely on global mean regression (GSReg) which are widely used to remove global artifacts. Using extensive simulations we demonstrate that RSMFC is effective in removing global artifacts in rsfMRI data. Critically, using a novel simulated dataset we demonstrate that, unlike GSReg, RSMFC does not artificially introduce anti-correlations between inherently uncorrelated networks, a result of paramount importance for reliably estimating functional connectivity. Furthermore, we show that the overall sensitivity, specificity and accuracy of RSMFC are superior to GSReg. Analysis of posterior cingulate cortex connectivity in experimental rsfMRI data from 22 healthy adults revealed strong functional connectivity in the default mode network, including more reliable identification of connectivity with left and right medial temporal lobe regions that were missed by GSReg. Notably, compared to GSReg, negative correlations with lateral fronto-parietal regions were significantly weaker in RSMFC. Our results suggest that RSMFC is an effective method for minimizing the effects of global artifacts and artificial negative correlations, while accurately recovering intrinsic functional brain networks.

[1]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[2]  Kaustubh Supekar,et al.  Developmental Maturation of Dynamic Causal Control Signals in Higher-Order Cognition: A Neurocognitive Network Model , 2012, PLoS Comput. Biol..

[3]  Habib Benali,et al.  Partial correlation for functional brain interactivity investigation in functional MRI , 2006, NeuroImage.

[4]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[5]  M. Czisch,et al.  Development of the brain's default mode network from wakefulness to slow wave sleep. , 2011, Cerebral cortex.

[6]  Catie Chang,et al.  Effects of model-based physiological noise correction on default mode network anti-correlations and correlations , 2009, NeuroImage.

[7]  Daniel L. Rubin,et al.  Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease , 2008, PLoS Comput. Biol..

[8]  Jonathan D. Cohen,et al.  Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold , 1995, Magnetic resonance in medicine.

[9]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[10]  Rasmus M. Birn,et al.  The role of physiological noise in resting-state functional connectivity , 2012, NeuroImage.

[11]  A. Braun,et al.  Decoupling of the brain's default mode network during deep sleep , 2009, Proceedings of the National Academy of Sciences.

[12]  Jeffrey S Anderson,et al.  Network anticorrelations, global regression, and phase‐shifted soft tissue correction , 2011, Human brain mapping.

[13]  D. Edwards.  Introduction to graphical modelling , 1995 .

[14]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[15]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[16]  Justin L. Vincent,et al.  Precuneus shares intrinsic functional architecture in humans and monkeys , 2009, Proceedings of the National Academy of Sciences.

[17]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[18]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Shie Mannor,et al.  Outlier-Robust PCA: The High-Dimensional Case , 2013, IEEE Transactions on Information Theory.

[20]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[21]  Kaustubh Supekar,et al.  Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty , 2012, NeuroImage.

[22]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[23]  M. Greicius,et al.  Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation , 2004, Journal of Cognitive Neuroscience.

[24]  Thomas T. Liu,et al.  Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI , 2012, NeuroImage.

[25]  Juan José Rodríguez Diez,et al.  Random Subspace Ensembles for fMRI Classification , 2010, IEEE Transactions on Medical Imaging.

[26]  Irene Tracey,et al.  Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal , 2004, NeuroImage.

[27]  Jonathan E. Taylor,et al.  Empirical null and false discovery rate analysis in neuroimaging , 2009, NeuroImage.

[28]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[29]  M. Corbetta,et al.  Temporal dynamics of spontaneous MEG activity in brain networks , 2010, Proceedings of the National Academy of Sciences.

[30]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[32]  K. Kiehl,et al.  Removal of Confounding Effects of Global Signal in Functional MRI Analyses , 2001, NeuroImage.

[33]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[34]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Robert P. W. Duin,et al.  Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.

[36]  Moo K. Chung,et al.  Sparse Brain Network Recovery Under Compressed Sensing , 2011, IEEE Transactions on Medical Imaging.

[37]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[38]  Rajesh Kumar,et al.  A method for removal of global effects from fMRI time series , 2004, NeuroImage.

[39]  D. Paré,et al.  Contrasting Activity Profile of Two Distributed Cortical Networks as a Function of Attentional Demands , 2009, The Journal of Neuroscience.

[40]  David C. Hoyle,et al.  Accuracy of Pseudo-Inverse Covariance Learning—A Random Matrix Theory Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  A. Anderson,et al.  Respiratory effects in human functional magnetic resonance imaging due to bulk susceptibility changes. , 2001, Physics in medicine and biology.

[42]  Habib Benali,et al.  Using partial correlation to enhance structural equation modeling of functional MRI data. , 2007, Magnetic resonance imaging.

[43]  J. Haxby,et al.  Localization of Cardiac-Induced Signal Change in fMRI , 1999, NeuroImage.

[44]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[45]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[46]  Michael D. Greicius,et al.  Development of functional and structural connectivity within the default mode network in young children , 2010, NeuroImage.

[47]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[48]  Jeff H. Duyn,et al.  Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal , 2007, NeuroImage.

[49]  M. Lowe,et al.  Functional Connectivity in Single and Multislice Echoplanar Imaging Using Resting-State Fluctuations , 1998, NeuroImage.

[50]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[51]  Jing Li,et al.  Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation , 2010, NeuroImage.

[52]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[53]  G. Glover,et al.  Spiral‐in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts , 2001, Magnetic resonance in medicine.