The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
Pushing the limits of in vivo diffusion MRI for the Human Connectome Project
Open accessAbstract
Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with G(max) = 300 mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The MGH-UCLA HCP is using extremely powerful MRI gradients to generate leading-edge 3T diffusion MRI data.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. M. (1999) · Annals of Neurology · doi:10.1002/1531-8249(199902)45:2 · Cited by 3,513
GUIDELINES FOR LIMITING EXPOSURE TO TIME-VARYING ELECTRIC AND MAGNETIC FIELDS (1 Hz TO 100 kHz)
International Commission on Non-Ionizing Radiation Protection (2010) · Health Physics · doi:10.1097/hp.0b013e3181f06c86 · Cited by 2,167
Handbook of MRI Pulse Sequences
Bernstein, M. A., King, K. F., & Zhou, X. J. (2004) · Elsevier eBooks · doi:10.1016/b978-0-12-092861-3.x5000-6 · Cited by 1,641
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635
Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., & Uğurbil, K. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22361 · Cited by 1,586
Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015710 · Cited by 1,296
Setsompop, K., Gagoski, B. A., Polimeni, J. R., Witzel, T., Wedeen, V. J., & Wald, L. L. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.23097 · Cited by 1,285
Self-citationLin, J., Saunders, R. A., Schulmeister, K., Söderberg, P. G., Stuck, B. E., Swerdlow, A. J., Taki, M., Veyret, B., Ziegelberger, G., Repacholi, M. H., et al. (2010) · HAL · Cited by 1,261
Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo
Reese, T. G., Heid, O., Weisskoff, R. M., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10308 · Cited by 1,229
Self-citation7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images
Vaughan, J. T., Garwood, M., Collins, C. M., Liu, W., DelaBarre, L., Adriany, G., Andersen, P., Merkle, H., Goebel, R., Smith, M. B., et al. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1156 · Cited by 890
Research works printed but not linked: 1
- Moeller, 2008, fMRI with 16 fold reduction using multibanded multislice sampling, Proc. of ISMRM, 16, 2366
Cited by
Sorted by citations
Andersson, J., & Sotiropoulos, S. N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.019 · Cited by 3,439
Denoising of diffusion MRI using random matrix theory
Veraart, J., Novikov, D. S., Christiaens, D., Ades-aron, B., Sijbers, J., & Fieremans, E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.08.016 · Cited by 1,905
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090
Functional connectomics from resting-state fMRI
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952
Advances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos, S. N., Jbabdi, S., Xu, J., Andersson, J., Moeller, S., Auerbach, E. J., Glasser, M. F., Hernandez, M., Sapiro, G., Jenkinson, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.057 · Cited by 889
Andersson, J., Graham, M. S., Zsoldos, E., & Sotiropoulos, S. N. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.058 · Cited by 752
Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.068 · Cited by 739
Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725
Fox, M. D., Buckner, R. L., Liu, H., Chakravarty, M. M., Lozano, A. M., & Pascual-Leone, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405003111 · Cited by 617
Population-averaged atlas of the macroscale human structural connectome and its network topology
Yeh, F.-C. F., Panesar, S. S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., Vettel, J. M., & Verstynen, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.027 · Cited by 619
Harms, M. P., Somerville, L. H., Ances, B. M., Andersson, J., Barch, D. M., Bastiani, M., Bookheimer, S. Y., Brown, T., Buckner, R. L., Burgess, G. C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.060 · Cited by 596
Self-citationRiva-Posse, P., Choi, K. S., Holtzheimer, P. E., McIntyre, C. C., Gross, R. E., Chaturvedi, A., Crowell, A. L., Garlow, S. J., Rajendra, J. K., & Mayberg, H. S. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.03.029 · Cited by 460
Riva-Posse, P., Choi, K. S., Holtzheimer, P. E., Crowell, A. L., Garlow, S. J., Rajendra, J. K., McIntyre, C. C., Gross, R. E., & Mayberg, H. S. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.59 · Cited by 441
The Lifespan Human Connectome Project in Aging: An overview
Bookheimer, S. Y., Salat, D. H., Terpstra, M., Ances, B. M., Barch, D. M., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Díaz‐Santos, M., Elam, J. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.009 · Cited by 487
Imaging brain microstructure with diffusion MRI: practicality and applications
Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. G. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3841 · Cited by 456
Somerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.050 · Cited by 373
SANDI: a compartment‐based model for non‐invasive apparent soma and neurite imaging by diffusion MRI
Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D. C., Shemesh, N., & Zhang, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116835 · Cited by 349
The Human Connectome Project and beyond: Initial applications of 300mT/m gradients
McNab, J. A., Edlow, B. L., Witzel, T., Huang, S. Y., Bhat, H., Heberlein, K., Feiweier, T., Liu, K., Keil, B. R., Cohen‐Adad, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.074 · Cited by 352
Self-citationYaman, B., Hosseini, S. A. H., Moeller, S., Ellermann, J., Uǧurbil, K., & Akçakaya, M. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28378 · Cited by 335
Measuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334
The Human Connectome Project: A retrospective
Elam, J. S., Glasser, M. F., Harms, M. P., Sotiropoulos, S. N., Andersson, J., Burgess, G. C., Curtiss, S. W., Oostenveld, R., Larson‐Prior, L. J., Schoffelen, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118543 · Cited by 340
Studying neuroanatomy using MRI
Lerch, J. P., van der Kouwe, A. J. W., Raznahan, A., Paus, T., Johansen‐Berg, H., Miller, K. L., Smith, S. M., Fischl, B. R., & Sotiropoulos, S. N. (2017) · Nature Neuroscience · doi:10.1038/nn.4501 · Cited by 330
Quantitative magnetic resonance imaging of brain anatomy and in vivo histology
Weiskopf, N., Edwards, L., Helms, G., Mohammadi, S., & Kirilina, E. P. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00326-1 · Cited by 321
Dynamic coupling of whole-brain neuronal and neurotransmitter systems
Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Deco, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921475117 · Cited by 284
Toward an electrophysiological “sweet spot” for deep brain stimulation in the subthalamic nucleus
Horn, A., Neumann, W., Degen, K., Schneider, G., & Kühn, A. A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23594 · Cited by 282
Andersson, J., & Sotiropoulos, S. N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.067 · Cited by 249
Towards network-guided neuromodulation for epilepsy
Piper, R. J., Richardson, R. M., Worrell, G. A., Carmichael, D. W., Baldeweg, T., Litt, B., Denison, T., & Tisdall, M. M. (2022) · Brain · doi:10.1093/brain/awac234 · Cited by 248
Self-citationConnectivity Profile Predictive of Effective Deep Brain Stimulation in Obsessive-Compulsive Disorder
Baldermann, J. C., Melzer, C., Zapf, A. C., Kohl, S., Timmermann, L., Tittgemeyer, M., Huys, D., Visser-Vandewalle, V. E. R. M., Kühn, A. A., Horn, A., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.019 · Cited by 252
MGH–USC Human Connectome Project datasets with ultra-high b-value diffusion MRI
Fan, Q., Witzel, T., Nummenmaa, A. R., Dijk, K. R. A. V., Horn, J. D. V., Drews, M. K., Somerville, L. H., Sheridan, M. A., Santillana, R. M., Snyder, J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.075 · Cited by 252
Self-citationHigh resolution whole brain diffusion imaging at 7 T for the Human Connectome Project
Vu, A. T., Auerbach, E. J., Lenglet, C., Moeller, S., Sotiropoulos, S. N., Jbabdi, S., Andersson, J., Yacoub, E. S., & Uǧurbil, K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.004 · Cited by 222
The contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213
Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla
Feinberg, D. A., Beckett, A. J. S., Vu, A. T., Stockmann, J., Huber, L., Ma, S. J., Ahn, S., Setsompop, K., Cao, X., Park, S., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02068-7 · Cited by 201
Self-citationOpportunities of connectomic neuromodulation
Horn, A., & Fox, M. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117180 · Cited by 187
Granberg, T., Fan, Q., Treaba, C. A., Ouellette, R., Herranz, E., Mangeat, G., Louapre, C., Cohen-Adad, J., Klawiter, E. C., Sloane, J. A., et al. (2017) · Brain · doi:10.1093/brain/awx247 · Cited by 184
Self-citationSafadi, Z., Grisot, G., Jbabdi, S., Behrens, T. E. J., Heilbronner, S. R., McLaughlin, N. C. R., Mandeville, J. B., Versace, A., Phillips, M. L., Lehman, J. F., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2335-17.2017 · Cited by 179
Jones, D. K., Alexander, D. C., Bowtell, R. W., Cercignani, M., Dell’Acqua, F., McHugh, D. J., Miller, K. L., Palombo, M., Parker, G. J. M., Rudrapatna, U. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.047 · Cited by 187
Huang, S. Y., Witzel, T., Keil, B. R., Scholz, A., Davids, M., Dietz, P., Rummert, E., Ramb, R., KIRSCH, J. E., Yendiki, A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118530 · Cited by 186
Self-citationMagnetic Resonance Imaging at Ultrahigh Fields
Uǧurbil, K. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2313619 · Cited by 182
Imaging at ultrahigh magnetic fields: History, challenges, and solutions
Uǧurbil, K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.007 · Cited by 175
Horn, A., Wenzel, G. R., Irmen, F., Huebl, J., Li, N., Neumann, W., Krause, P., Böhner, G., Scheel, M., & Kühn, A. A. (2019) · Brain · doi:10.1093/brain/awz239 · Cited by 171
Long-Range Neural Synchrony in Behavior
Harris, A. Z., & Gordon, J. A. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034111 · Cited by 168
Revisiting the global workspace orchestrating the hierarchical organization of the human brain
Deco, G., Vidaurre, D., & Kringelbach, M. L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01003-6 · Cited by 162
Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.09.034 · Cited by 162
Directing cell therapy to anatomic target sites in vivo with magnetic resonance targeting
Muthana, M., Kennerley, A. J., Hughes, R., Fagnano, E., Richardson, J., Paul, M., Murdoch, C., Wright, F., Payne, C., Lythgoe, M. F., et al. (2015) · Nature Communications · doi:10.1038/ncomms9009 · Cited by 157
Virtual Human Models for Electromagnetic Studies and Their Applications
Makarov, S. N., Noetscher, G. M., Yanamadala, J., Piazza, M. W., Louie, S., Prokop, A., Nazarian, A., & Nummenmaa, A. R. (2017) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2722420 · Cited by 157
Probabilistic conversion of neurosurgical DBS electrode coordinates into MNI space
Horn, A., Kühn, A. A., Merkl, A., Shih, L. C., Alterman, R. L., & Fox, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.004 · Cited by 156
Anatomical and functional organization of the human substantia nigra and its connections
Zhang, Y., Larcher, K. M.-H., Mišić, B., & Dagher, A. (2017) · eLife · doi:10.7554/elife.26653 · Cited by 156
Constrained optimization of gradient waveforms for generalized diffusion encoding
Sjölund, J., Szczepankiewicz, F., Nilsson, M., Topgaard, D., Westin, C., & Knutsson, H. E. (2015) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2015.10.012 · Cited by 149
Magnetic resonance imaging of mean cell size in human breast tumors
Xu, J., Jiang, X., Li, H., Arlinghaus, L. R., McKinley, E. T., Devan, S. P., Hardy, B. M., Xie, J., Kang, H., Chakravarthy, A. B., et al. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28056 · Cited by 143
7T: Physics, safety, and potential clinical applications
Kraff, O., & Quick, H. H. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25723 · Cited by 142
Nilsson, M., Lasič, S., Drobnjak, I., Topgaard, D., & Westin, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3711 · Cited by 147
EEG functional connectivity is partially predicted by underlying white matter connectivity
Chu, C. J., Tanaka, N., Diaz, J., Edlow, B. L., Wu, O., Hämäläinen, M., Stufflebeam, S., Cash, S. S., & Kramer, M. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.033 · Cited by 135
Quantification of cell size using temporal diffusion spectroscopy
Jiang, X., Li, H., Xie, J., Zhao, P., Gore, J. C., & Xu, J. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25684 · Cited by 135
Normative vs. patient-specific brain connectivity in deep brain stimulation
Wang, Q., Akram, H., Muthuraman, M., González‐Escamilla, G., Sheth, S. A., Oxenford, S., Yeh, F.-C. F., Groppa, S., Vanegas‐Arroyave, N., Zrinzo, L. U., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117307 · Cited by 134
Connectome imaging for mapping human brain pathways
Shi, Y., & Toga, A. W. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.92 · Cited by 135
Self-citationFoo, T., Tan, E. T., Vermilyea, M. E., Hua, Y., Fiveland, E. W., Piel, J. E., Park, K., Ricci, J., Thompson, P. S., Graziani, D., et al. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28087 · Cited by 131
Makris, N., Yogesh Rathi, A., Mouradian, P., Bonmassar, G., Papadimitriou, G., Ing, W. I., Yeterian, E. H., Kubicki, M. R., Eskandar, E. N., Wald, L. L., et al. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9462-9 · Cited by 129
Self-citationHolographic Reconstruction of Axonal Pathways in the Human Brain
Petersen, M. S., Mlakar, J., Haber, S. N., Parent, M., Smith, Y., Strick, P. L., Griswold, M. A., & McIntyre, C. C. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.030 · Cited by 128
Validation of tractography: Comparison with manganese tracing
Knösche, T. R., Anwander, A., Liptrot, M., & Dyrby, T. B. (2015) · Human Brain Mapping · doi:10.1002/hbm.22902 · Cited by 127
Image quality transfer and applications in diffusion MRI
Alexander, D. C., Zikic, D., Ghosh, A., Tanno, R., Wottschel, V., Zhang, J., Kaden, E., Dyrby, T. B., Sotiropoulos, S. N., Zhang, H. G., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.089 · Cited by 131
Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy
Xu, J., Li, H., Harkins, K. D., Jiang, X., Xie, J., Kang, H., Does, M. D., & Gore, J. C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.006 · Cited by 125
Microstructural imaging of human neocortex in vivo
Edwards, L., Kirilina, E. P., Mohammadi, S., & Weiskopf, N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.055 · Cited by 122
Gradient and shim technologies for ultra high field MRI
Winkler, S. A., Schmitt, F., Landes, H., de Bever, J. T., Wade, T., Alejski, A., & Rutt, B. K. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.033 · Cited by 121
Self-citationDevelopment of a high angular resolution diffusion imaging human brain template
Varentsova, A., Zhang, S., & Arfanakis, K. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.009 · Cited by 119
Connectomic deep brain stimulation for obsessive-compulsive disorder
Baldermann, J. C., Schüller, T., Kohl, S., Voon, V., Li, N., Hollunder, B., Figee, M., Haber, S. N., Sheth, S. A., Mosley, P. E., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.07.010 · Cited by 118
Gunalan, K., Chaturvedi, A., Howell, B. L., Duchin, Y., Lempka, S. F., Patriat, R., Sapiro, G., Harel, N., & McIntyre, C. C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176132 · Cited by 114
Turbulent-like Dynamics in the Human Brain
Deco, G., & Kringelbach, M. L. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108471 · Cited by 113
Vachha, B. A., & Huang, S. Y. (2021) · European Radiology Experimental · doi:10.1186/s41747-021-00216-2 · Cited by 113
The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter
Huang, S. Y., Nummenmaa, A. R., Witzel, T., Duval, T., Cohen‐Adad, J., Wald, L. L., & McNab, J. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.008 · Cited by 112
Self-citationWhat’s new and what’s next in diffusion MRI preprocessing
Tax, C. M. W., Bastiani, M., Veraart, J., Garyfallidis, E., & Irfanoglu, M. O. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118830 · Cited by 121
The effect of Gibbs ringing artifacts on measures derived from diffusion MRI
Perrone, D., Aelterman, J., Pižurica, A., Jeurissen, B., Philips, W. R., & Leemans, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.068 · Cited by 118
Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast
Eichner, C., Cauley, S., Cohen‐Adad, J., Möller, H. E., Turner, R., Setsompop, K., & Wald, L. L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.074 · Cited by 113
Self-citationAvram, A., Sarlls, J. E., Barnett, A. S., Özarslan, E., Thomas, C., Irfanoglu, M. O., Hutchinson, E. B., Pierpaoli, C. M., & Basser, P. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.027 · Cited by 113
Distributed harmonic patterns of structure-function dependence orchestrate human consciousness
Luppi, A. I., Vohryzek, J., Kringelbach, M. L., Mediano, P. A. M., Craig, M. M., Adapa, R., Carhart-Harris, R. L., Roseman, L., Pappas, I., Peattie, A. R. D., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04474-1 · Cited by 114
Tax, C. M. W., Grussu, F., Kaden, E., Ning, L., Rudrapatna, U. S., Evans, J., St‐Jean, S., Leemans, A., Koppers, S., Merhof, D., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.077 · Cited by 116
Post mortem mapping of connectional anatomy for the validation of diffusion MRI
Yendiki, A., Aggarwal, M., Axer, M., Howard, A. F. D., van Walsum, A. V. C., & Haber, S. N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119146 · Cited by 116
Fick, R., Wassermann, D., Caruyer, E., & Rachid Deriche, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.046 · Cited by 114
Rapid brain MRI acquisition techniques at ultra‐high fields
Setsompop, K., Feinberg, D. A., & Polimeni, J. R. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3478 · Cited by 106
Self-citationKringelbach, M. L., Perl, Y. S., Tagliazucchi, E., & Deco, G. (2023) · Science Advances · doi:10.1126/sciadv.ade6049 · Cited by 105
Diffusion-MRI in neurodegenerative disorders
Goveas, J., O’Dwyer, L., Mascalchi, M., Cosottini, M., Diciotti, S., De Santis, S., Passamonti, L., Tessa, C., Toschi, N., & Giannelli, M. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.04.006 · Cited by 104
Morawski, M., Kirilina, E. P., Scherf, N., Jäger, C., Reimann, K., Trampel, R., Gavriilidis, F., Geyer, S., Biedermann, B., Arendt, T., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.060 · Cited by 108
Budinger, T. F., & Bird, M. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.067 · Cited by 102
Lightweight, compact, and high‐performance 3 T MR system for imaging the brain and extremities
Foo, T., Laskaris, E. T., Vermilyea, M. E., Xu, M., Thompson, P., Conte, G., Epps, C. V., Immer, C., Lee, S., Tan, E. T., et al. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27175 · Cited by 100
The Structural Connectome of the Human Central Homeostatic Network
Edlow, B. L., McNab, J. A., Witzel, T., & Kinney, H. C. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0378 · Cited by 99
Self-citationSelf-decoupled radiofrequency coils for magnetic resonance imaging
Yan, X., Gore, J. C., & Grissom, W. A. (2018) · Nature Communications · doi:10.1038/s41467-018-05585-8 · Cited by 98
Gradient waveform design for tensor-valued encoding in diffusion MRI
Szczepankiewicz, F., Westin, C., & Nilsson, M. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.109007 · Cited by 94
Rare long-range cortical connections enhance human information processing
Deco, G., Perl, Y. S., Vuust, P., Tagliazucchi, E., Kennedy, H., & Kringelbach, M. L. (2021) · Current Biology · doi:10.1016/j.cub.2021.07.064 · Cited by 95
Toward a standardized structural–functional group connectome in MNI space
Horn, A., & Blankenburg, F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.048 · Cited by 94
A high‐performance gradient insert for rapid and short‐T 2 imaging at full duty cycle
Weiger, M., Overweg, J., Rösler, M. B., Froidevaux, R., Hennel, F., Wilm, B. J., Penn, A., Sturzenegger, U., Schuth, W., Mathlener, M., et al. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26954 · Cited by 91
ConnectomeDB—Sharing human brain connectivity data
Hodge, M. R., HORTON, W. G., Brown, T. T., Herrick, R., Olsen, T., Hileman, M., McKay, M., Archie, K. A., Cler, E. A., Harms, M. P., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.046 · Cited by 90
Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T
Wen, Q., Kelley, D. A. C., Banerjee, S., Lupo, J. M., Chang, S. M., Xu, D., Hess, C. P., & Nelson, S. J. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.08.017 · Cited by 88
Maffei, C., Lee, C., Planich, M., Ramprasad, M., Ravi, N., Trainor, D., Urban, Z., Kim, M., Jones, R. J., Henin, A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118706 · Cited by 87
Comparative connectivity correlates of dystonic and essential tremor deep brain stimulation
Tsuboi, T., Wong, J. K., Eisinger, R. S., Okromelidze, L., Burns, M. R., Ramirez‐Zamora, A., Almeida, L., Shukla, A. W., Foote, K. D., Okun, M. S., et al. (2021) · Brain · doi:10.1093/brain/awab074 · Cited by 86
The visual white matter: The application of diffusion MRI and fiber tractography to vision science
Rokem, A., Takemura, H., Bock, A. S., Scherf, K. S., Behrmann, M., Wandell, B. A., Fine, I., Bridge, H., & Pestilli, F. (2017) · Journal of Vision · doi:10.1167/17.2.4 · Cited by 92
Targeting of White Matter Tracts with Transcranial Magnetic Stimulation
Nummenmaa, A. R., McNab, J. A., Savadjiev, P., Okada, Y. C., Hämäläinen, M., Wang, R., Wald, L. L., Pascual‐Leone, Á., Wedeen, V. J., & Raij, T. (2013) · Brain stimulation · doi:10.1016/j.brs.2013.10.001 · Cited by 81
Self-citationNetwork curvature as a hallmark of brain structural connectivity
Farooq, H., Chen, Y., Georgiou, T. T., Tannenbaum, A., & Lenglet, C. (2019) · Nature Communications · doi:10.1038/s41467-019-12915-x · Cited by 84
Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain
McKinnon, E. T., Jensen, J. H., Glenn, G. R., & Helpern, J. A. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.10.026 · Cited by 81
Karaman, M., Sui, Y., Wang, H., Magin, R. L., Li, Y., & Zhou, X. J. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26012 · Cited by 80
Brain imaging with improved acceleration and SNR at 7 Tesla obtained with 64‐channel receive array
Uǧurbil, K., Auerbach, E. J., Moeller, S., Grant, A., Wu, X., de Moortele, P. V., Olman, C. A., DelaBarre, L. J., Schillak, S., Radder, J., et al. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27695 · Cited by 82
Ning, L., Bonet-Carné, E., Grussu, F., Sepehrband, F., Kaden, E., Veraart, J., Blumberg, S. B., Khoo, C. S., Palombo, M., Kokkinos, I., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117128 · Cited by 77
Metadata
- Authors
- Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Adad, J., McNab, J. A., Keil, B., Tisdall, M. D., Hoecht, P., Dietz, P., Cauley, S. F., Tountcheva, V., Matschl, V., Lenz, V. H., Heberlein, K., Potthast, A., Thein, H., Van Horn, J., Toga, A., Schmitt, F., Lehne, D., Rosen, B. R., Wedeen, V., & Wald, L. L.
- Affiliations
- Harvard University (US)
- Siemens Healthineers (Germany) (DE)
- University of California, Los Angeles (US)
- Corresponding author
- Wald, L.L.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 220-233
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3725309
- OpenAlex
- W1987263024
- Cited by
- 545
- References
- 69
- Citations by year
- 2026: 28 · 2025: 49 · 2024: 41 · 2023: 44 · 2022: 42 · 2021: 61 · 2020: 53 · 2019: 39 · 2018: 39 · 2017: 40 · 2016: 37 · 2015: 51 · 2014: 15 · 2013: 6
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- National Institutes of Health, NIH Blueprint Initiative for Neuroscience Research (U01MH093765, P41EB015896, K99/R00EB012107)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › NMR spectroscopy and applications
- Keywords
- diffusion MRI, Human Connectome Project, compressed sensing, diffusion spectrum imaging, high angular resolution diffusion imaging, signal-to-noise ratio, crossing fibers, diffusion tractography
- MeSH terms
- Animals, Brain, Humans, Image Enhancement, Models, Anatomic, Models, Neurological, Nerve Net, Diffusion Tensor Imaging, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
