Resting-State Brain Organization Revealed by Functional Covariance Networks

Background Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. Methodology and Principal Findings We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. Conclusion The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

[1]  B Horwitz,et al.  Intercorrelations of Glucose Metabolic Rates between Brain Regions: Application to Healthy Males in a State of Reduced Sensory Input , 1984, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[2]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

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

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

[5]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[6]  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.

[7]  P. Morosan,et al.  Regional cerebral blood flow correlations of somatosensory areas 3a, 3b, 1, and 2 in humans during rest: A PET and cytoarchitectural study , 2003, Human brain mapping.

[8]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

[9]  R. Malach,et al.  Intersubject Synchronization of Cortical Activity During Natural Vision , 2004, Science.

[10]  Colin Studholme,et al.  Positive and negative network correlations in temporal lobe epilepsy. , 2004, Cerebral cortex.

[11]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[12]  P. Fransson Spontaneous low‐frequency BOLD signal fluctuations: An fMRI investigation of the resting‐state default mode of brain function hypothesis , 2005, Human brain mapping.

[13]  Karl J. Friston,et al.  Structural Covariance in the Human Cortex , 2005, The Journal of Neuroscience.

[14]  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.

[15]  A. Fingelkurts,et al.  Functional connectivity in the brain—is it an elusive concept? , 2005, Neuroscience & Biobehavioral Reviews.

[16]  Chaozhe Zhu,et al.  Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI , 2007, NeuroImage.

[17]  Yuan Zhou,et al.  The relationship within and between the extrinsic and intrinsic systems indicated by resting state correlational patterns of sensory cortices , 2007, NeuroImage.

[18]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

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

[20]  Yaakov Stern,et al.  Structural MRI covariance patterns associated with normal aging and neuropsychological functioning , 2007, Neurobiology of Aging.

[21]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

[22]  Alan C. Evans,et al.  Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. , 2007, Cerebral cortex.

[23]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[24]  Rafael Malach,et al.  Extrinsic and intrinsic systems in the posterior cortex of the human brain revealed during natural sensory stimulation. , 2007, Cerebral cortex.

[25]  G. Glover,et al.  Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus , 2007, Biological Psychiatry.

[26]  S. Petersen,et al.  The maturing architecture of the brain's default network , 2008, Proceedings of the National Academy of Sciences.

[27]  Jae Sung Lee,et al.  Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults , 2008, European Journal of Nuclear Medicine and Molecular Imaging.

[28]  John D E Gabrieli,et al.  Resting in peace or noise: Scanner background noise suppresses default‐mode network , 2008, Human brain mapping.

[29]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

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

[31]  B. Miller,et al.  Neurodegenerative Diseases Target Large-Scale Human Brain Networks , 2009, Neuron.

[32]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

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

[34]  Alan C. Evans,et al.  Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans , 2009, PloS one.

[35]  Yufeng Zang,et al.  Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load , 2009, PloS one.

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

[37]  Gian Luca Romani,et al.  Noxious somatosensory stimulation affects the default mode of brain function: evidence from functional MR imaging. , 2009, Radiology.

[38]  Qiyong Gong,et al.  High-field MRI reveals an acute impact on brain function in survivors of the magnitude 8.0 earthquake in China , 2009, Proceedings of the National Academy of Sciences.

[39]  Bharat B. Biswal,et al.  The oscillating brain: Complex and reliable , 2010, NeuroImage.

[40]  Marc Joliot,et al.  The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state , 2010, Brain Research Bulletin.

[41]  M. Fox,et al.  Clinical Applications of Resting State Functional Connectivity , 2010, Front. Syst. Neurosci..

[42]  M. Raichle The Brain's Dark Energy , 2006, Science.

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

[44]  Edward T. Bullmore,et al.  Modular and Hierarchically Modular Organization of Brain Networks , 2010, Front. Neurosci..

[45]  M. Raichle,et al.  Disease and the brain's dark energy , 2010, Nature Reviews Neurology.

[46]  Gene E. Alexander,et al.  Age-related networks of regional covariance in MRI gray matter: Reproducible multivariate patterns in healthy aging , 2010, NeuroImage.

[47]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[48]  Huafu Chen,et al.  fMRI study of mesial temporal lobe epilepsy using amplitude of low‐frequency fluctuation analysis , 2010, Human brain mapping.

[49]  Efstathios D. Gennatas,et al.  Network-level structural covariance in the developing brain , 2010, Proceedings of the National Academy of Sciences.

[50]  Bharat B. Biswal,et al.  Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity , 2010, NeuroImage.

[51]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[52]  Marc Joliot,et al.  Brain activity at rest: a multiscale hierarchical functional organization. , 2011, Journal of neurophysiology.

[53]  Yong He,et al.  Erratum to “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI” [Brain Develop 29 (2) (2007) 83–91] , 2012, Brain and Development.