Comparison of BOLD, diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm.

The blood oxygen level-dependent (BOLD) effect is extensively used for functional MRI (fMRI) but presents some limitations. Diffusion-weighted fMRI (DfMRI) has been proposed as a method more tightly linked to neuronal activity. This work proposes a protocol of DfMRI acquired for several b-values and diffusion directions that is compared to gradient-echo BOLD (GE-BOLD) and to repeated spin-echo BOLD (SE-BOLD, acquisitions performed with b=0s/mm2), which was also used to ensure the reproducibility of the response. A block stimulation paradigm of the primary visual system (V1) was performed in 12 healthy subjects with checkerboard alternations (2Hz frequency). DfMRI was performed at 3T with 5 b-values (b=1500, 1000, 500, 250, 0s/mm2) with TR/TE=1004/93ms, Δ/δ=45.4ms/30ms, and 6 spatial directions for diffusion measures. GE-BOLD was performed with a similar block stimulation design timing. Apparent Diffusion Coefficient (ADC)-fMRI was computed with all b-values used. An identical Z-score level was used for all fMRI modalities for the comparison of volumes of activation. ADC-fMRI and SE-BOLD fMRI activation locations were compared in a voxel-based analysis to a cytoarchitectural probability map of V1. SE-BOLD activation volumes represented only 55% of the GE-BOLD activation volumes (P<0.0001). DfMRI activation volumes averaged for all b-values acquired represented only 12% of GE-BOLD (P<0.0001) and only 22% of SE-BOLD activation volumes (P<0.005). Compared to SE-BOLD-fMRI, ADC-fMRI activations showed fewer pixels outside of V1 and a higher average probability of belonging to V1. DfMRI and ADC-fMRI acquisition at 3T could be easily post-processed with common neuro-imaging software. DfMRI and ADC-fMRI activation volumes were significantly smaller than those obtained with SE-BOLD. ADC-fMRI activations were more precisely localized in V1 than those of SE-BOLD-fMRI. This validated the increased capability of ADC-fMRI compared to BOLD to enhance the precision of localizing an fMRI activation in the cyto-architectural zone V1, thereby justifying the use of ADC-fMRI for neuro-scientific studies.

[1]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[2]  Mark W. Woolrich,et al.  Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.

[3]  Stephen M. Smith,et al.  General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.

[4]  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.

[5]  Allen W Song,et al.  Improved spatial localization based on flow‐moment‐nulled and intra‐voxel incoherent motion‐weighted fMRI , 2003, NMR in biomedicine.

[6]  Julia Hocking,et al.  Comparison of block and event‐related experimental designs in diffusion‐weighted functional MRI , 2014, Journal of magnetic resonance imaging : JMRI.

[7]  Keith J. Worsley,et al.  Statistical analysis of activation images , 2001 .

[8]  Denis Le Bihan,et al.  Diffusion, confusion and functional MRI , 2012, NeuroImage.

[9]  Mark W. Woolrich,et al.  Robust group analysis using outlier inference , 2008, NeuroImage.

[10]  V. Wedeen,et al.  Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.

[11]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[12]  Hidenao Fukuyama,et al.  Comparison of diffusion-weighted fMRI and BOLD fMRI responses in a verbal working memory task , 2013, NeuroImage.

[13]  S. Schoenberg,et al.  Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters , 2007, Journal of magnetic resonance imaging : JMRI.

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

[15]  J B Poline,et al.  Transient decrease in water diffusion observed in human occipital cortex during visual stimulation , 2001, Proceedings of the National Academy of Sciences of the United States of America.

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

[17]  Allen W Song,et al.  B factor dependence of the temporal characteristics of brain activation using dynamic apparent diffusion coefficient contrast , 2004, Magnetic resonance in medicine.

[18]  René S. Kahn,et al.  Functional diffusion tensor imaging at 3 Tesla , 2013, Front. Microbiol..

[19]  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.

[20]  M. Altbach,et al.  Isotropic diffusion weighting in radial fast spin‐echo magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[21]  K. Amunts,et al.  Centenary of Brodmann's Map — Conception and Fate , 2022 .

[22]  Bassem Hiba,et al.  Accuracies and Contrasts of Models of the Diffusion-Weighted-Dependent Attenuation of the MRI Signal at Intermediate b-values , 2015, Magnetic resonance insights.

[23]  Dae-Shik Kim,et al.  Functional activation using apparent diffusion coefficient-dependent contrast allows better spatial localization to the neuronal activity: evidence using diffusion tensor imaging and fiber tracking , 2003, NeuroImage.

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

[25]  Sheng-Kwei Song,et al.  White-matter diffusion fMRI of mouse optic nerve , 2013, NeuroImage.

[26]  Rebecca J. Williams,et al.  Functional localization of the human color center by decreased water displacement using diffusion‐weighted fMRI , 2015, Brain and behavior.

[27]  Denis Le Bihan,et al.  The ‘wet mind’: water and functional neuroimaging , 2007 .

[28]  S. Haker,et al.  Complimentary aspects of diffusion imaging and fMRI: II. Elucidating contributions to the fMRI signal with diffusion sensitization. , 2007, Magnetic resonance imaging.

[29]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[30]  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.

[31]  Gregory McCarthy,et al.  Enhanced Spatial Localization of Neuronal Activation Using Simultaneous Apparent-Diffusion-Coefficient and Blood-Oxygenation Functional Magnetic Resonance Imaging , 2002, NeuroImage.

[32]  Hidenao Fukuyama,et al.  An intrinsic diffusion response function for analyzing diffusion functional MRI time series , 2009, NeuroImage.

[33]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.