Static and dynamic characteristics of cerebral blood flow during the resting state

In this study, the static and dynamic characteristics of cerebral blood flow (CBF) in the resting state were investigated using an arterial spin labeling (ASL) perfusion imaging technique. Consistent with previous PET results, static CBF measured by ASL was significantly higher in the posterior cingulate cortex (PCC), thalamus, insula/superior temporal gyrus (STG) and medial prefrontal cortex (MPFC) than the average CBF of the brain. The dynamic measurement of CBF fluctuations showed high correlation (functional connectivity) between components in the default mode network. These brain regions also had high local temporal synchrony and high fluctuation amplitude, as measured by regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) analyses. The spatial pattern of the static CBF correlated well with that of the dynamic indices. The high static and dynamic activities in the PCC, MPFC, insula/STG and thalamus suggest that these regions play a vital role in maintaining and facilitating fundamental brain functions.

[1]  B. Biswal,et al.  Simultaneous assessment of flow and BOLD signals in resting‐state functional connectivity maps , 1997, NMR in biomedicine.

[2]  B. Mazoyer,et al.  Cortical networks for working memory and executive functions sustain the conscious resting state in man , 2001, Brain Research Bulletin.

[3]  Karl J. Friston,et al.  Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging , 1997, NeuroImage.

[4]  Michael A Kraut,et al.  Inverse correlation between cerebral blood flow measured by continuous arterial spin-labeling (CASL) MRI and neurocognitive function in children with sickle cell anemia (SCA). , 2006, Blood.

[5]  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.

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

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

[8]  N. Schuff,et al.  Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. , 2005, Radiology.

[9]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[10]  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.

[11]  Maurice G. Kendall,et al.  Rank Correlation Methods , 1949 .

[12]  Bruce R. Rosen,et al.  Spatio-temporal characteristics of low-frequency BOLD signal fluctuations in isoflurane-anesthetized rat brain , 2008, NeuroImage.

[13]  Bharat Biswal,et al.  Slow vasomotor fluctuation in fMRI of anesthetized child brain , 2000, Magnetic resonance in medicine.

[14]  R. B. Buxton,et al.  Quantitative imaging of perfusion using a single subtraction (QUIPSS) , 1996, NeuroImage.

[15]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[16]  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.

[17]  V. Calhoun,et al.  Changes in the interaction of resting‐state neural networks from adolescence to adulthood , 2009, Human brain mapping.

[18]  Tianzi Jiang,et al.  Decreased regional homogeneity in schizophrenia: a resting state functional magnetic resonance imaging study , 2006, Neuroreport.

[19]  R. Buxton,et al.  Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II) , 1998 .

[20]  Thomas T. Liu,et al.  Caffeine reduces the activation extent and contrast-to-noise ratio of the functional cerebral blood flow response but not the BOLD response , 2008, NeuroImage.

[21]  S. Warach,et al.  Noninvasive perfusion imaging of human brain tumors with EPISTAR , 2004, European Radiology.

[22]  Peter van Gelderen,et al.  A comparison of fast MR scan techniques for cerebral activation studies at 1.5 Tesla , 1998, Magnetic resonance in medicine.

[23]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[24]  XN Zuo,et al.  Establishing the Reliability of Amplitude Measures for Spontaneous Fluctuations in the Resting Brain , 2009, NeuroImage.

[25]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

[26]  Lester Melie-García,et al.  Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory , 2008, NeuroImage.

[27]  M. Hallett,et al.  Regional homogeneity changes in patients with Parkinson's disease , 2009, Human brain mapping.

[28]  M. Corbetta,et al.  Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex , 1997, Journal of Cognitive Neuroscience.

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

[30]  Jeff Duyn,et al.  H215O PET validation of steady‐state arterial spin tagging cerebral blood flow measurements in humans , 2000, Magnetic resonance in medicine.

[31]  Jeff H. Duyn,et al.  Mapping resting-state functional connectivity using perfusion MRI , 2008, NeuroImage.

[32]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[33]  Yufeng Zang,et al.  Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study , 2006, Neuroreport.

[34]  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.

[35]  Marcus E Raichle,et al.  Neuroscience. The brain's dark energy. , 2006, Science.

[36]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[37]  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.

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

[39]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[40]  Yihong Yang,et al.  Frequency specificity of functional connectivity in brain networks , 2008, NeuroImage.

[41]  J. Detre,et al.  Noninvasive MRI evaluation of cerebral blood flow in cerebrovascular disease , 1998, Neurology.

[42]  Thomas T. Liu,et al.  A signal processing model for arterial spin labeling functional MRI , 2005, NeuroImage.

[43]  H. Gu,et al.  Association of nicotine addiction and nicotine's actions with separate cingulate cortex functional circuits. , 2009, Archives of general psychiatry.

[44]  Donald S. Williams,et al.  Perfusion imaging , 1992, Magnetic resonance in medicine.

[45]  G. Aguirre,et al.  Experimental Design and the Relative Sensitivity of BOLD and Perfusion fMRI , 2002, NeuroImage.

[46]  N. Filippini,et al.  Group comparison of resting-state FMRI data using multi-subject ICA and dual regression , 2009, NeuroImage.

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

[48]  M. Greicius.  Resting-state functional connectivity in neuropsychiatric disorders , 2008, Current opinion in neurology.

[49]  Yufeng Wang,et al.  Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder , 2008, NeuroImage.

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

[51]  Y. Zang,et al.  Normal aging decreases regional homogeneity of the motor areas in the resting state , 2007, Neuroscience Letters.

[52]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

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

[54]  Tianzi Jiang,et al.  Regional coherence changes in the early stages of Alzheimer’s disease: A combined structural and resting-state functional MRI study , 2007, NeuroImage.

[55]  R J Roman,et al.  Spontaneous Flow Oscillations in the Cerebral Cortex during Acute Changes in Mean Arterial Pressure , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[56]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

[57]  Yihong Yang,et al.  Mapping functional connectivity based on synchronized CMRO2 fluctuations during the resting state , 2009, NeuroImage.

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

[59]  Liang Wang,et al.  Default mode network as revealed with multiple methods for resting-state functional MRI analysis , 2008, Journal of Neuroscience Methods.

[60]  Justin L. Vincent,et al.  Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[61]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[62]  R. Bluhm,et al.  Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. , 2007, Schizophrenia bulletin.