Neuronal or Hemodynamic? Grappling with the Functional MRI Signal

Magnetic resonance imaging (MRI) and functional MRI (fMRI) continue to advance because creative physicists, engineers, neuroscientists, clinicians, and physiologists find new ways for extracting more information from the signal. Innovations in pulse sequence design, paradigm design, and processing methods have advanced the field and firmly established fMRI as a cornerstone for understanding the human brain. In this article, the field of fMRI is described through consideration of the central problem of separating hemodynamic from neuronal information. Discussed here are examples of how pulse sequences, activation paradigms, and processing methods are integrated such that novel, high-quality information can be obtained. Examples include the extraction of information such as activation onset latency, metabolic rate, neuronal adaptation, vascular patency, vessel diameter, vigilance, and subvoxel activation. Experimental measures include time series latency, hemodynamic shape, MR phase, multivoxel patterns, ratios of activation-related R2* to R2, metabolic rate changes, fluctuation correlations and frequencies, changes in fluctuation correlations and frequencies over time, resting correlation states, echo time dependence, and more.

[1]  Vince D. Calhoun,et al.  A review of multivariate methods for multimodal fusion of brain imaging data , 2012, Journal of Neuroscience Methods.

[2]  C. Beckmann,et al.  Spectral characteristics of resting state networks. , 2011, Progress in brain research.

[3]  Hidenao Fukuyama,et al.  Water-Diffusion Slowdown in the Human Visual Cortex on Visual Stimulation Precedes Vascular Responses , 2009, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[4]  Essa Yacoub,et al.  High-field fMRI unveils orientation columns in humans , 2008, Proceedings of the National Academy of Sciences.

[5]  David G Norris,et al.  Exploring the post‐stimulus undershoot with spin‐echo fMRI: Implications for models of neurovascular response , 2011, Human brain mapping.

[6]  Wen-Ming Luh,et al.  Accurate decoding of sub-TR timing differences in stimulations of sub-voxel regions from multi-voxel response patterns , 2013, NeuroImage.

[7]  R. Buxton,et al.  Modeling the hemodynamic response to brain activation , 2004, NeuroImage.

[8]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[9]  Jens Frahm,et al.  The post-stimulation undershoot in BOLD fMRI of human brain is not caused by elevated cerebral blood volume , 2008, NeuroImage.

[10]  Bharat B. Biswal,et al.  Resting state fMRI: A personal history , 2012, NeuroImage.

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

[12]  Karla L Miller,et al.  Evidence for a vascular contribution to diffusion FMRI at high b value , 2007, Proceedings of the National Academy of Sciences.

[13]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[14]  Shy Shoham,et al.  Neural substrates of tactile object recognition: An fMRI study , 2004, Human brain mapping.

[15]  Robert Turner,et al.  Whole-brain mapping of venous vessel size in humans using the hypercapnia-induced BOLD effect , 2010, NeuroImage.

[16]  Anders M. Dale,et al.  Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy , 2004, NeuroImage.

[17]  David A. Boas,et al.  A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans , 2006, NeuroImage.

[18]  R. Buxton,et al.  A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[19]  G. Ojemann,et al.  Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex , 2013, Front. Hum. Neurosci..

[20]  Peter J. Koopmans,et al.  Whole brain, high resolution spin-echo resting state fMRI using PINS multiplexing at 7T , 2012, NeuroImage.

[21]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

[22]  Allen W. Song,et al.  Diffusion modulation of the fMRI signal: Early investigations on the origin of the BOLD signal , 2012, NeuroImage.

[23]  Noah D. Brenowitz,et al.  Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis , 2012, Proceedings of the National Academy of Sciences.

[24]  Noah D. Brenowitz,et al.  Integrated strategy for improving functional connectivity mapping using multiecho fMRI , 2013, Proceedings of the National Academy of Sciences.

[25]  Wen-Ming Luh,et al.  Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI , 2012, NeuroImage.

[26]  Stephen D. Mayhew,et al.  Evidence that the negative BOLD response is neuronal in origin: A simultaneous EEG–BOLD–CBF study in humans , 2014, NeuroImage.

[27]  B. Rosen,et al.  Dynamic functional imaging of relative cerebral blood volume during rat forepaw stimulation , 1998, Magnetic resonance in medicine.

[28]  David A. Boas,et al.  Twenty years of functional near-infrared spectroscopy: introduction for the special issue , 2014, NeuroImage.

[29]  Hans-Jochen Heinze,et al.  Integration of ultra-high field MRI and histology for connectome based research of brain disorders , 2013, Front. Neuroanat..

[30]  J A Frank,et al.  Functional magnetic resonance imaging in medicine and physiology. , 1990, Science.

[31]  K. Uğurbil,et al.  The Spatial Dependence of the Poststimulus Undershoot as Revealed by High-Resolution BOLD- and CBV-Weighted fMRI , 2005, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[32]  Essa Yacoub,et al.  Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla , 2007, NeuroImage.

[33]  E C Wong,et al.  A hypercapnia‐based normalization method for improved spatial localization of human brain activation with fMRI , 1997, NMR in biomedicine.

[34]  T. L. Davis,et al.  Calibrated functional MRI: mapping the dynamics of oxidative metabolism. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Kalanit Grill-Spector,et al.  Selectivity of Adaptation in Single Units: Implications for fMRI Experiments , 2006, Neuron.

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

[37]  Murray H. Loew,et al.  Neuronal current imaging using MRI: a feasibility study , 2001, SPIE Medical Imaging.

[38]  Peter R Luijten,et al.  BOLD Consistently Matches Electrophysiology in Human Sensorimotor Cortex at Increasing Movement Rates: A Combined 7T fMRI and ECoG Study on Neurovascular Coupling , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[39]  Wen-Ming Luh,et al.  The effect of spatial smoothing on fMRI decoding of columnar-level organization with linear support vector machine , 2013, Journal of Neuroscience Methods.

[40]  David A. Boas,et al.  A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation , 2002, NeuroImage.

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

[42]  P. Jezzard,et al.  Quantitative measurement of cerebral physiology using respiratory-calibrated MRI , 2012, NeuroImage.

[43]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Matthew J. Brookes,et al.  Measuring functional connectivity using MEG: Methodology and comparison with fcMRI , 2011, NeuroImage.

[45]  P. Bandettini,et al.  The effect of respiration variations on independent component analysis results of resting state functional connectivity , 2008, Human brain mapping.

[46]  Omer Tal,et al.  The amplitude of the resting state fMRI global signal is related to EEG vigilance measures , 2013 .

[47]  Yihong Yang,et al.  Simultaneous MRI acquisition of blood volume, blood flow, and blood oxygenation information during brain activation , 2004, Magnetic resonance in medicine.

[48]  Daniel A. Handwerker,et al.  Periodic changes in fMRI connectivity , 2012, NeuroImage.

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

[50]  P. Bandettini,et al.  Spatial Heterogeneity of the Nonlinear Dynamics in the FMRI BOLD Response , 2001, NeuroImage.

[51]  A. Song,et al.  Diffusion weighted fMRI at 1.5 T , 1996, Magnetic resonance in medicine.

[52]  D. Le Bihan,et al.  Direct and fast detection of neuronal activation in the human brain with diffusion MRI. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[53]  R. Turner,et al.  Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations , 2002, NeuroImage.

[54]  Peter J. Koopmans,et al.  Whole brain, high resolution multiband spin-echo EPI fMRI at 7T: A comparison with gradient-echo EPI using a color-word Stroop task , 2014, NeuroImage.

[55]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[56]  D. Leopold,et al.  Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest , 2008, Human brain mapping.

[57]  E C Wong,et al.  Processing strategies for time‐course data sets in functional mri of the human brain , 1993, Magnetic resonance in medicine.

[58]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[59]  G. Crelier,et al.  Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[60]  Stephen D. Mayhew,et al.  Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity , 2013, Proceedings of the National Academy of Sciences.

[61]  Peter A. Bandettini,et al.  The effect of stimulus duty cycle and “off” duration on BOLD response linearity , 2005, NeuroImage.

[62]  H. Shibasaki Human brain mapping: Hemodynamic response and electrophysiology , 2008, Clinical Neurophysiology.

[63]  R. Goebel,et al.  Tracking cognitive processes with functional MRI mental chronometry , 2003, Current Opinion in Neurobiology.

[64]  J. Pekar,et al.  Functional magnetic resonance imaging based on changes in vascular space occupancy , 2003, Magnetic resonance in medicine.

[65]  R. Buxton,et al.  A review of calibrated blood oxygenation level‐dependent (BOLD) methods for the measurement of task‐induced changes in brain oxygen metabolism , 2013, NMR in biomedicine.

[66]  X. Hu,et al.  Evaluation of the early response in fMRI in individual subjects using short stimulus duration , 1997, Magnetic resonance in medicine.

[67]  N. Logothetis,et al.  High-Resolution fMRI Reveals Laminar Differences in Neurovascular Coupling between Positive and Negative BOLD Responses , 2012, Neuron.

[68]  J. R. Baker,et al.  The intravascular contribution to fmri signal change: monte carlo modeling and diffusion‐weighted studies in vivo , 1995, Magnetic resonance in medicine.

[69]  Lawrence L. Wald,et al.  Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1 , 2010, NeuroImage.

[70]  Risto A. Kauppinen,et al.  Quantitative assessment of blood flow, blood volume and blood oxygenation effects in functional magnetic resonance imaging , 1998, Nature Medicine.

[71]  D. Plenz,et al.  Direct magnetic resonance detection of neuronal electrical activity , 2006, Proceedings of the National Academy of Sciences.

[72]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[73]  A. Oeltermann,et al.  Hippocampal–cortical interaction during periods of subcortical silence , 2012, Nature.

[74]  David G. Norris,et al.  Spin-echo fMRI: The poor relation? , 2012, NeuroImage.

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

[76]  Ravi S. Menon,et al.  Mental chronometry using latency-resolved functional MRI. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[77]  J R Reichenbach,et al.  In vivo measurement of blood oxygen saturation using magnetic resonance imaging: A direct validation of the blood oxygen level‐dependent concept in functional brain imaging , 1997, Human brain mapping.

[78]  P. Bandettini,et al.  Understanding neural system dynamics through task modulation and measurement of functional MRI amplitude, latency, and width , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[79]  J. Mandeville,et al.  The Accuracy of Near Infrared Spectroscopy and Imaging during Focal Changes in Cerebral Hemodynamics , 2001, NeuroImage.

[80]  N. Logothetis,et al.  Ultra High-Resolution fMRI in Monkeys with Implanted RF Coils , 2002, Neuron.

[81]  Kâmil Uludağ,et al.  Transient and sustained BOLD responses to sustained visual stimulation. , 2008, Magnetic resonance imaging.

[82]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[83]  Hanzhang Lu,et al.  Quantitative evaluation of oxygenation in venous vessels using T2‐Relaxation‐Under‐Spin‐Tagging MRI , 2008, Magnetic resonance in medicine.

[84]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[85]  E. Wong New developments in arterial spin labeling pulse sequences , 2013, NMR in biomedicine.

[86]  K. Grill-Spector,et al.  fMR-adaptation: a tool for studying the functional properties of human cortical neurons. , 2001, Acta psychologica.

[87]  M E Raichle,et al.  Coupling between changes in human brain temperature and oxidative metabolism during prolonged visual stimulation. , 2000, Proceedings of the National Academy of Sciences of the United States of America.