A critical assessment of data quality and venous effects in sub-millimeter fMRI

ABSTRACT Advances in hardware, pulse sequences, and reconstruction techniques have made it possible to perform functional magnetic resonance imaging (fMRI) at sub‐millimeter resolution while maintaining high spatial coverage and acceptable signal‐to‐noise ratio. Here, we examine whether sub‐millimeter fMRI can be used as a routine method for obtaining accurate measurements of fine‐scale local neural activity. We conducted fMRI in human visual cortex during a simple event‐related visual experiment (7T, gradient‐echo EPI, 0.8‐mm isotropic voxels, 2.2‐s sampling rate, 84 slices), and developed analysis and visualization tools to assess the quality of the data. Our results fall along three lines of inquiry. First, we find that the acquired fMRI images, combined with appropriate surface‐based processing, provide reliable and accurate measurements of fine‐scale blood oxygenation level dependent (BOLD) activity patterns. Second, we show that the highly folded structure of cortex causes substantial biases on spatial resolution and data visualization. Third, we examine the well‐recognized issue of venous contributions to fMRI signals. In a systematic assessment of large sections of cortex measured at a fine scale, we show that time‐averaged T2*‐weighted EPI intensity is a simple, robust marker of venous effects. These venous effects are unevenly distributed across cortex, are more pronounced in gyri and outer cortical depths, and are, to a certain degree, in consistent locations across subjects relative to cortical folding. Furthermore, we show that these venous effects are strongly correlated with BOLD responses evoked by the experiment. We conclude that sub‐millimeter fMRI can provide robust information about fine‐scale BOLD activity patterns, but special care must be exercised in visualizing and interpreting these patterns, especially with regards to the confounding influence of the brain's vasculature. To help translate these methodological findings to neuroscience research, we provide practical suggestions for both high‐resolution and standard‐resolution fMRI studies.

[1]  Chris J. Martin Contributions and complexities from the use of in vivo animal models to improve understanding of human neuroimaging signals , 2014, Front. Neurosci..

[2]  Siegfried Trattnig,et al.  A method for the dynamic correction of B0-related distortions in single-echo EPI at 7 T , 2016, NeuroImage.

[3]  Stefan Pollmann,et al.  The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7T , 2016, NeuroImage.

[4]  Stephen C Cunnane,et al.  The morphology of the human cerebrovascular system , 2018, Human brain mapping.

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

[6]  Xu Wang,et al.  Quantifying interindividual variability and asymmetry of face-selective regions: A probabilistic functional atlas , 2015, NeuroImage.

[7]  Essa Yacoub,et al.  Mechanisms underlying decoding at 7 T: Ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye , 2010, NeuroImage.

[8]  Kawin Setsompop,et al.  Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field , 2017, NeuroImage.

[9]  Bruce R. Rosen,et al.  Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals , 2018, Neuron.

[10]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[11]  Karl J. Friston,et al.  Functional MRI , 1997 .

[12]  M. Fukunaga,et al.  Negative BOLD-fMRI Signals in Large Cerebral Veins , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[13]  Roel H. R. Deckers,et al.  Quantifying the spatial resolution of the gradient echo and spin echo BOLD response at 3 Tesla , 2005, Magnetic resonance in medicine.

[14]  Arno Villringer,et al.  High resolution atlasing of the venous brain vasculature from 7T quantitative susceptibility , 2018, bioRxiv.

[15]  Steen Moeller,et al.  Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project , 2017, NeuroImage.

[16]  Dimo Ivanov,et al.  Impact of acquisition and analysis strategies on cortical depth-dependent fMRI , 2017, NeuroImage.

[17]  Kamil Ugurbil,et al.  Magnetic Resonance Imaging at Ultrahigh Fields , 2014, IEEE Transactions on Biomedical Engineering.

[18]  Benedikt A. Poser,et al.  Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals , 2017, NeuroImage.

[19]  Xiaoping Hu,et al.  Potential pitfalls of functional MRI using conventional gradient‐recalled echo techniques , 1994, NMR in biomedicine.

[20]  Klaus Scheffler,et al.  Functional MRI in human subjects with gradient‐echo and spin‐echo EPI at 9.4 T , 2014, Magnetic resonance in medicine.

[21]  Lars Muckli,et al.  Laminar fMRI: Applications for cognitive neuroscience , 2017, NeuroImage.

[22]  Rainer Goebel,et al.  Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment , 2012, NeuroImage.

[23]  Hellmut Merkle,et al.  Sensory and optogenetically driven single-vessel fMRI , 2016, Nature Methods.

[24]  Essa Yacoub,et al.  High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4 T , 2018, NeuroImage.

[25]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[26]  Russell A. Poldrack,et al.  FMRIPrep: a robust preprocessing pipeline for functional MRI , 2018 .

[27]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[28]  R. Goebel,et al.  Frequency preference and attention effects across cortical depths in the human primary auditory cortex , 2015, Proceedings of the National Academy of Sciences.

[29]  Jack L. Gallant,et al.  Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI , 2015, Front. Neurosci..

[30]  Nikos K Logothetis,et al.  Laminar specificity in monkey V1 using high-resolution SE-fMRI. , 2006, Magnetic resonance imaging.

[31]  Swarnodeep HomRoy Impact of Acquisition on CEO Pay , 2012 .

[32]  Wietske van der Zwaag,et al.  Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function , 2017, NeuroImage.

[33]  Tobias Kober,et al.  MP2RAGE, a self-bias field corrected sequence for improved segmentation at high field , 2008 .

[34]  Roland N. Boubela,et al.  fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions , 2015, Scientific Reports.

[35]  Ravi S. Menon The great brain versus vein debate , 2012, NeuroImage.

[36]  Essa Yacoub,et al.  The impact of ultra-high field MRI on cognitive and computational neuroimaging , 2017, NeuroImage.

[37]  Jean Gotman,et al.  Anatomically informed interpolation of fMRI data on the cortical surface , 2006, NeuroImage.

[38]  Michael Eickenberg,et al.  Seeing it all: Convolutional network layers map the function of the human visual system , 2017, NeuroImage.

[39]  Markus Barth,et al.  A cortical vascular model for examining the specificity of the laminar BOLD signal , 2016, NeuroImage.

[40]  N. Ramsey,et al.  Cortical Depth-Dependent Temporal Dynamics of the BOLD Response in the Human Brain , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[41]  B. Wandell,et al.  Mapping Hv4 and Ventral Occipital Cortex: the Venous Eclipse , 2022 .

[42]  Gary F. Egan,et al.  Combining images and anatomical knowledge to improve automated vein segmentation in MRI , 2017, NeuroImage.

[43]  Anna Devor,et al.  Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe , 2015, The Journal of Neuroscience.

[44]  Natalia Petridou,et al.  Laminar imaging of positive and negative BOLD in human visual cortex at 7T , 2018, NeuroImage.

[45]  J. Winawer,et al.  Linking Electrical Stimulation of Human Primary Visual Cortex, Size of Affected Cortical Area, Neuronal Responses, and Subjective Experience , 2016, Neuron.

[46]  Jonathan Winawer,et al.  GLMdenoise: a fast, automated technique for denoising task-based fMRI data , 2013, Front. Neurosci..

[47]  Essa Yacoub,et al.  Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns , 2017, Proceedings of the National Academy of Sciences.

[48]  Essa Yacoub,et al.  Pushing the spatio-temporal limits of MRI and fMRI , 2018, NeuroImage.

[49]  D. Norris,et al.  Layer‐specific BOLD activation in human V1 , 2010, Human brain mapping.

[50]  Essa Yacoub,et al.  Sub-millimeter T2 weighted fMRI at 7 T: comparison of 3D-GRASE and 2D SE-EPI , 2015, Front. Neurosci..

[51]  P. Kara,et al.  Neural correlates of single vessel hemodynamic responses in vivo , 2016, Nature.

[52]  R. Kauppinen,et al.  Venous blood effects in spin‐echo fMRI of human brain , 1999, Magnetic resonance in medicine.

[53]  Kamil Ugurbil,et al.  Imaging at ultrahigh magnetic fields: History, challenges, and solutions , 2017, NeuroImage.

[54]  Jennifer M. D. Yoon,et al.  Functionally Defined White Matter Reveals Segregated Pathways in Human Ventral Temporal Cortex Associated with Category-Specific Processing , 2015, Neuron.

[55]  Jun Hua,et al.  Implementation of vascular‐space‐occupancy MRI at 7T , 2013, Magnetic resonance in medicine.

[56]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[57]  Liang Wang,et al.  Probabilistic Maps of Visual Topography in Human Cortex. , 2015, Cerebral cortex.

[58]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[59]  Katrin Amunts,et al.  Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation , 2017, NeuroImage.

[60]  Lawrence L. Wald,et al.  Automatic cortical surface reconstruction of high-resolution T 1 echo planar imaging data , 2016, NeuroImage.

[61]  Essa Yacoub,et al.  Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla , 2007, NeuroImage.

[62]  Pierre-Louis Bazin,et al.  Anatomically motivated modeling of cortical laminae , 2014, NeuroImage.

[63]  Johan D. Carlin,et al.  Adjudicating between face-coding models with individual-face fMRI responses , 2015, bioRxiv.

[64]  Essa Yacoub,et al.  Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field , 2018, NeuroImage.

[65]  Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity , 2016 .

[66]  Nikolaus Kriegeskorte,et al.  How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex spatiotemporal filter? , 2010, NeuroImage.

[67]  E. Haacke,et al.  Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1 , 2008, American Journal of Neuroradiology.

[68]  R. Gruetter,et al.  Mapping and characterization of positive and negative BOLD responses to visual stimulation in multiple brain regions at 7T , 2018, Human brain mapping.

[69]  D. Tank,et al.  4 Tesla gradient recalled echo characteristics of photic stimulation‐induced signal changes in the human primary visual cortex , 1993 .

[70]  G. Glover,et al.  Retinotopic organization in human visual cortex and the spatial precision of functional MRI. , 1997, Cerebral cortex.

[71]  Robert Turner,et al.  How Much Cortex Can a Vein Drain? Downstream Dilution of Activation-Related Cerebral Blood Oxygenation Changes , 2002, NeuroImage.

[72]  Wolfgang Bogner,et al.  Key clinical benefits of neuroimaging at 7 T , 2016, NeuroImage.

[73]  David J. Heeger,et al.  The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring , 2007, NeuroImage.

[74]  Lawrence L. Wald,et al.  In vivo B0 field shimming methods for MRI at 7T , 2017, NeuroImage.

[75]  E. Haacke,et al.  Identification of vascular structures as a major source of signal contrast in high resolution 2D and 3D functional activation imaging of the motor cortex at l.5T preliminary results , 1993, Magnetic resonance in medicine.

[76]  S. Ogawa,et al.  Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.

[77]  K. Uğurbil,et al.  Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1 , 2012, PloS one.

[78]  Jean A. Tkach,et al.  2D and 3D high resolution gradient echo functional imaging of the brain: Venous contributions to signal in motor cortex studies , 1994, NMR in biomedicine.

[79]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[80]  Laurentius Huber,et al.  High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1 , 2017, Neuron.

[81]  Kalanit Grill-Spector,et al.  Temporal Processing Capacity in High-Level Visual Cortex Is Domain Specific , 2015, The Journal of Neuroscience.

[82]  Jack L. Gallant,et al.  Pycortex: an interactive surface visualizer for fMRI , 2015, Front. Neuroinform..

[83]  PatternsTim Menzies,et al.  Potential Pitfalls for , 1996 .

[84]  Kâmil Uludag,et al.  Linking brain vascular physiology to hemodynamic response in ultra-high field MRI , 2017, NeuroImage.

[85]  Shahin Nasr,et al.  Interdigitated Color- and Disparity-Selective Columns within Human Visual Cortical Areas V2 and V3 , 2016, The Journal of Neuroscience.

[86]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[87]  Roberto Viviani,et al.  A Digital Atlas of Middle to Large Brain Vessels and Their Relation to Cortical and Subcortical Structures , 2016, Front. Neuroanat..

[88]  K. Uğurbil,et al.  Microvascular BOLD contribution at 4 and 7 T in the human brain: Gradient‐echo and spin‐echo fMRI with suppression of blood effects , 2003, Magnetic resonance in medicine.

[89]  B. Wandell,et al.  Compressive spatial summation in human visual cortex. , 2013, Journal of neurophysiology.

[90]  Brian K. Rutt,et al.  Gradient and shim technologies for ultra high field MRI , 2016, NeuroImage.

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

[92]  P. Jezzard,et al.  Correction for geometric distortion in echo planar images from B0 field variations , 1995, Magnetic resonance in medicine.

[93]  Xiaojian Kang,et al.  Improving the resolution of functional brain imaging: analyzing functional data in anatomical space. , 2007, Magnetic resonance imaging.

[94]  Adrian T. Lee,et al.  Discrimination of Large Venous Vessels in Time‐Course Spiral Blood‐Oxygen‐Level‐Dependent Magnetic‐Resonance Functional Neuroimaging , 1995, Magnetic resonance in medicine.

[95]  Jeff H. Duyn,et al.  Effects of spatial fMRI resolution on the classification of naturalistic movies , 2017, NeuroImage.

[96]  Mark E. Ladd,et al.  SAR Simulations & Safety , 2017, NeuroImage.

[97]  Jonathan R. Polimeni,et al.  Analysis strategies for high-resolution UHF-fMRI data , 2017, NeuroImage.

[98]  Dimo Ivanov,et al.  Cortical depth profiles of luminance contrast responses in human V1 and V2 using 7 T fMRI , 2018, Human brain mapping.

[99]  R. Goebel,et al.  Cortical Depth Dependent Functional Responses in Humans at 7T: Improved Specificity with 3D GRASE , 2013, PloS one.

[100]  C. J. McGrath,et al.  Effect of exchange rate return on volatility spill-over across trading regions , 2012 .

[101]  Alex R. Wade,et al.  Visual areas and spatial summation in human visual cortex , 2001, Vision Research.

[102]  Denis Schluppeck,et al.  7 Tesla fMRI Reveals Systematic Functional Organization for Binocular Disparity in Dorsal Visual Cortex , 2015, The Journal of Neuroscience.

[103]  Jeffrey M. Zacks,et al.  Searchlight analysis: Promise, pitfalls, and potential , 2013, NeuroImage.

[104]  Amir Shmuel,et al.  Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns , 2018, NeuroImage.

[105]  Klaas E. Stephan,et al.  A hemodynamic model for layered BOLD signals , 2016, NeuroImage.

[106]  Kamil Ugurbil,et al.  What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[107]  Justin L. Gardner,et al.  Is cortical vasculature functionally organized? , 2010, NeuroImage.