CoChat
Sign Up
Cited by 12Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI

Open access
Spencer, Arthur P. C., Nguyen-Duc, Jasmine, de Riedmatten, Inès, Szczepankiewicz, Filip, Jelescu, Ileana O.·2025·Nature Communications
DOI 10.1038/s41467-025-60357-5PMC PMC12125227

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural activity, facilitating detection of both grey and white matter activity. Further, through minimising vascular contamination, ADC-fMRI has the potential to overcome the limited temporal specificity of the BOLD signal. However, the use of linear diffusion encoding introduces sensitivity to fibre directionality, while averaging over multiple directions comes at great cost to temporal resolution. In this study, we used spherical b-tensor encoding to impart diffusion sensitisation in all directions per shot, providing an ADC-fMRI contrast capable of detecting activity independently of fibre directionality. We provide evidence from two task-based experiments on a clinical scanner that isotropic ADC-fMRI is more temporally specific than BOLD-fMRI, and offers more balanced mapping of grey and white matter activity. We further demonstrate that isotropic ADC-fMRI detects white matter activity independently of fibre direction, while linear ADC-fMRI preferentially detects activity in voxels containing fibres perpendicular to the diffusion encoding direction. Thus, isotropic ADC-fMRI opens avenues for investigation into whole-brain grey and white matter functional connectivity.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-025-60357-5

Cites

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • DfMRI may offer greater spatial and temporal precision than BOLD, including potential sensitivity in white matter, where BOLD fMRI is challenging.

    “DfMRI may, therefore, offer greater spatial and temporal precision than BOLD, including potential sensitivity in white matter, where BOLD fMRI is challenging (de Riedmatten, Spencer, Nguyen-Duc, et al., 2025; Nguyen-Duc et al., 2025; Spencer et al., 2025).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Spencer et al., 2025

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 90 · Cited by 12

Research works printed but not linked: 85

  1. [1]Ogawa, S., Lee, T.-M., Kay, A. R. & Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl Acad. Sci. 87, 9868–9872 (1990). 2124706 PMC55275 · DOI · Paper page
  2. [3]O’Herron, P. et al. Neural correlates of single-vessel haemodynamic responses in vivo. Nature 534, 378–382 (2016). 27281215 PMC4911280 · DOI
  3. [4]Grajauskas, L. A., Frizzell, T., Song, X. & D’Arcy, R. C. White matter fmri activation cannot be treated as a nuisance regressor: Overcoming a historical blind spot. Front. Neurosci. 13, 1024 (2019). 31636527 PMC6787144 · DOI
  4. [5]Gawryluk, J. R., Mazerolle, E. L. & D’Arcy, R. C. Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions. Front. Neurosci. 8, 239 (2014). 25152709 PMC4125856 · DOI
  5. [6]Gore, J. C. et al. Functional MRI and resting state connectivity in white matter-a mini-review. Magn. Reson. imaging 63, 1–11 (2019). 31376477 PMC6861686 · DOI
  6. [7]Li, M., Newton, A. T., Anderson, A. W., Ding, Z. & Gore, J. C. Characterization of the hemodynamic response function in white matter tracts for event-related fMRI. Nat. Commun. 10, 1140 (2019). 30850610 PMC6408456 · DOI
  7. [8]Baillet, S., Mosher, J. C. & Leahy, R. M. Electromagnetic brain mapping. IEEE Signal Process. Mag. 18, 14–30 (2001). · DOI
  8. [9]Filley, C. M. White matter: organization and functional relevance. Neuropsychol. Rev. 20, 158–173 (2010). 20352350 · DOI
  9. [10]Darquié, A., Poline, J.-B., Poupon, C., Saint-Jalmes, H. & Le Bihan, D. Transient decrease in water diffusion observed in human occipital cortex during visual stimulation. Proc. Natl Acad. Sci. 98, 9391–9395 (2001). 11459931 PMC55431 · DOI · Paper page
  10. [12]Yang, Y. et al. Imaging action potential in single mammalian neurons by tracking the accompanying sub-nanometer mechanical motion. ACS Nano 12, 4186–4193 (2018). 29570267 PMC6141446 · DOI
  11. [13]Ling, T. et al. High-speed interferometric imaging reveals dynamics of neuronal deformation during the action potential. Proc. Natl. Acad. Sci. 117, 10278–10285 (2020). 32341158 PMC7229674 · DOI
  12. [14]Chéreau, R., Saraceno, G. E., Angibaud, J., Cattaert, D. & Nägerl, U. V. Superresolution imaging reveals activity-dependent plasticity of axon morphology linked to changes in action potential conduction velocity. Proc. Natl. Acad. Sci. 114, 1401–1406 (2017). 28115721 PMC5307438 · DOI
  13. [15]Sherpa, A. D., Xiao, F., Joseph, N., Aoki, C. & Hrabetova, S. Activation of β -adrenergic receptors in rat visual cortex expands astrocytic processes and reduces extracellular space volume. Synapse 70, 307–316 (2016). 27085090 PMC4909535 · DOI
  14. [16]Kwon, J., Lee, S., Jo, Y. & Choi, M. All-optical observation on activity-dependent nanoscale dynamics of myelinated axons. Neurophotonics 10, 015003–015003 (2023). 36699624 PMC9868287 · DOI
  15. [17]Aso, T. et al. An intrinsic diffusion response function for analyzing diffusion functional MRI time series. Neuroimage 47, 1487–1495 (2009). 19450693 · DOI · Paper page
  16. [18]Aso, T., Urayama, S.-i, Fukuyama, H. & Le Bihan, D. Comparison of diffusion-weighted fMRI and BOLD fMRI responses in a verbal working memory task. Neuroimage 67, 25–32 (2013). 23147237 · DOI
  17. [19]Nunes, D., Gil, R. & Shemesh, N. A rapid-onset diffusion functional MRI signal reflects neuromorphological coupling dynamics. Neuroimage 231, 117862 (2021). 33592243 · DOI · Paper page
  18. [20]Miller, K. L. et al. Evidence for a vascular contribution to diffusion FMRI at high b value. Proc. Natl. Acad. Sci. 104, 20967–20972 (2007). 18093924 PMC2409250 · DOI · Paper page
  19. [21]Nicolas, R., Gros-Dagnac, H., Aubry, F. & Celsis, P. Comparison of BOLD, diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm. Magn. Reson. imaging 39, 123–131 (2017). 28163122 · DOI
  20. [22]De Luca, A., Schlaffke, L., Siero, J. C., Froeling, M. & Leemans, A. On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm. Hum. brain Mapp. 40, 5069–5082 (2019). 31410939 PMC6865683 · DOI
  21. [23]Olszowy, W., Diao, Y. & Jelescu, I. O. Beyond BOLD: Evidence for diffusion fMRI contrast in the human brain distinct from neurovascular response. bioRxiv2021-05 (2021).
  22. [24]de Riedmatten, I., Spencer, A. P., Olszowy, W. & Jelescu, I. O. Apparent Diffusion Coefficient fMRI shines light on white matter resting-state connectivity compared to BOLD. Commun. Biol. 8, 447 (2025). 40091123 PMC11911413 · DOI · Paper page
  23. [26]Le Bihan, D. What can we see with IVIM MRI? Neuroimage 187, 56–67 (2019). 29277647 · DOI · Paper page
  24. [27]Debaker, C., Djemai, B., Ciobanu, L., Tsurugizawa, T. & Le Bihan, D. Diffusion mri reveals in vivo and non-invasively changes in astrocyte function induced by an aquaporin-4 inhibitor. PLoS One 15, e0229702 (2020). 32413082 PMC7228049 · DOI
  25. [28]Iima, M. & Le Bihan, D. Clinical intravoxel incoherent motion and diffusion mr imaging: Past, present, and future. Radiology 278, 13–32 (2016). 26690990 · DOI · Paper page
  26. [29]Spees, W. M., Lin, T.-H. & Song, S.-K. White-matter diffusion fMRI of mouse optic nerve. Neuroimage 65, 209–215 (2013). 23085108 PMC3513522 · DOI
  27. [30]Lin, T.-H. et al. Diffusion fMRI detects white-matter dysfunction in mice with acute optic neuritis. Neurobiol. Dis. 67, 1–8 (2014). 24632420 PMC4035476 · DOI
  28. [31]Abe, Y., Tsurugizawa, T. & Le Bihan, D. Water diffusion closely reveals neural activity status in rat brain loci affected by anesthesia. PLoS Biol. 15, e2001494 (2017). 28406906 PMC5390968 · DOI
  29. [32]Wong, E. C., Cox, R. W. & Song, A. W. Optimized isotropic diffusion weighting. Magn. Reson. Med. 34, 139–143 (1995). 7476070 · DOI
  30. [33]Mori, S. & Van Zijl, P. C. Diffusion weighting by the trace of the diffusion tensor within a single scan. Magn. Reson. Med. 33, 41–52 (1995). 7891534 · DOI
  31. [34]Szczepankiewicz, F., Sjölund, J., Ståhlberg, F., Lätt, J. & Nilsson, M. Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems. PloS one 14, e0214238 (2019). 30921381 PMC6438503 · DOI
  32. [35]Szczepankiewicz, F., Westin, C.-F. & Nilsson, M. Gradient waveform design for tensor-valued encoding in diffusion MRI. J. Neurosci. Methods 348, 109007 (2021). 33242529 PMC8443151 · DOI
  33. [36]Tax, C. M., Szczepankiewicz, F., Nilsson, M. & Jones, D. K. The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. Neuroimage 210, 116534 (2020). 31931157 PMC7429990 · DOI
  34. [37]Schilling, K. G. et al. Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis. Proc. Natl Acad. Sci. 120, e2219666120 (2023). 37824529 PMC10589709 · DOI
  35. [38]Rafael-Patino, J. et al. Robust monte-carlo simulations in diffusion-MRI: effect of the substrate complexity and parameter choice on the reproducibility of results. Front. Neuroinformatics 14, 8 (2020). PMC7076166 32210781 · DOI
  36. [39]Nguyen-Duc, J. et al. CATERPillar: a fast and flexible framework framework for generating synthetic white matter numerical phantoms. In Proceedings of the international society for magnetic resonance in medicine (2024).
  37. [40]Visser, M., Jefferies, E. & Lambon Ralph, M. Semantic processing in the anterior temporal lobes: a meta-analysis of the functional neuroimaging literature. J. Cogn. Neurosci. 22, 1083–1094 (2010). 19583477 · DOI
  38. [41]Witt, S. T., Laird, A. R. & Meyerand, M. E. Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis. Neuroimage 42, 343–356 (2008). 18511305 PMC2592684 · DOI
  39. [42]Huber, L. et al. Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex. Neuroimage 208, 116463 (2020). 31862526 PMC11829252 · DOI
  40. [43]Gordon, E. M. et al. A somato-cognitive action network alternates with effector regions in motor cortex. Nature 617, 351–359 (2023). 37076628 PMC10172144 · DOI
  41. [44]DuPre, E. et al. TE-dependent analysis of multi-echo fMRI with tedana. J. Open Source Softw. 6, 3669 (2021). · DOI
  42. [45]Ding, Z. et al. Detection of synchronous brain activity in white matter tracts at rest and under functional loading. Proc. Natl Acad. Sci. 115, 595–600 (2018). 29282320 PMC5776967 · DOI
  43. [46]Zu, Z. et al. The missing third dimension-Functional correlations of BOLD signals incorporating white matter. Sci. Adv. 10, eadi0616 (2024). 38277462 PMC10816695 · DOI
  44. [47]Hertanu, A., Pavan, T. & Jelescu, I. O. Unveiling microstructural dynamics: Somatosensory-evoked response induces extensive diffusivity and kurtosis changes associated with neural activity in rodents. bioRxiv2024-04 (2024). PMC12319920 40800919
  45. [48]Nunes, D., Ianus, A. & Shemesh, N. Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway. Neuroimage 184, 646–657 (2019). 30267858 PMC6264401 · DOI
  46. [49]De Swiet, T. M. & Mitra, P. P. Possible systematic errors in single-shot measurements of the trace of the diffusion tensor. J. Magn. Reson., Ser. B 111, 15–22 (1996). 8661259 · DOI
  47. [50]Jelescu, I. O., de Skowronski, A., Geffroy, F., Palombo, M. & Novikov, D. S. Neurite exchange imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. NeuroImage 256, 119277 (2022). 35523369 PMC10363376 · DOI · Paper page
  48. [51]Pyatigorskaya, N., Le Bihan, D., Reynaud, O. & Ciobanu, L. Relationship between the diffusion time and the diffusion MRI signal observed at 17.2 tesla in the healthy rat brain cortex. Magn. Reson. Med. 72, 492–500 (2014). 24022863 · DOI
  49. [52]Lee, H.-H., Papaioannou, A., Novikov, D. S. & Fieremans, E. In vivo observation and biophysical interpretation of time-dependent diffusion in human cortical gray matter. Neuroimage 222, 117054 (2020). 32585341 PMC7736473 · DOI
  50. [53]Fieremans, E. et al. In vivo observation and biophysical interpretation of time-dependent diffusion in human white matter. NeuroImage 129, 414–427 (2016). 26804782 PMC4803645 · DOI · Paper page
  51. [54]Mulkern, R. V., Haker, S. J. & Maier, S. E. Complimentary aspects of diffusion imaging and fMRI: II. Elucidating contributions to the fMRI signal with diffusion sensitization. Magn. Reson. imaging 25, 939–952 (2007). 17442520 · DOI
  52. [55]Williams, R. J., McMahon, K. L., Hocking, J. & Reutens, D. C. Comparison of block and event-related experimental designs in diffusion-weighted functional MRI. J. Magn. Reson. Imaging 40, 367–375 (2014). 24923816 · DOI
  53. [56]Williams, R. J., Reutens, D. C. & Hocking, J. Functional localization of the human color center by decreased water displacement using diffusion-weighted fMRI. Brain Behav. 5, e00408 (2015). 26664792 PMC4667755 · DOI
  54. [57]Tsurugizawa, T., Ciobanu, L. & Le Bihan, D. Water diffusion in brain cortex closely tracks underlying neuronal activity. Proc. Natl Acad. Sci. 110, 11636–11641 (2013). 23801756 PMC3710802 · DOI · Paper page
  55. [58]Komaki, Y. et al. Differential effects of aquaporin-4 channel inhibition on BOLD fMRI and diffusion fMRI responses in mouse visual cortex. PLoS One 15, e0228759 (2020). 32437449 PMC7241787 · DOI · Paper page
  56. [59]Pampel, A., Jochimsen, T. H. & Möller, H. E. BOLD background gradient contributions in diffusion-weighted fMRI-comparison of spin-echo and twice-refocused spin-echo sequences. NMR Biomed. 23, 610–618 (2010). 20235336 · DOI
  57. [60]Schummers, J., Yu, H. & Sur, M. Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science 320, 1638–1643 (2008). 18566287 · DOI
  58. [61]Szczepankiewicz, F. & Sjölund, J. Cross-term-compensated gradient waveform design for tensor-valued diffusion MRI. J. Magn. Reson. 328, 106991 (2021). 33984713 · DOI
  59. [62]Gountouna, V.-E. et al. Functional Magnetic Resonance Imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task. Neuroimage 49, 552–560 (2010). 19631757 · DOI
  60. [63]Assaf, Y. Imaging laminar structures in the gray matter with diffusion MRI. Neuroimage 197, 677–688 (2019). 29309898 · DOI · Paper page
  61. [64]Ganepola, T. et al. Using diffusion MRI to discriminate areas of cortical grey matter. NeuroImage 182, 456–468 (2018). 29274501 PMC6189525 · DOI
  62. [65]Aggarwal, M., Nauen, D. W., Troncoso, J. C. & Mori, S. Probing region-specific microstructure of human cortical areas using high angular and spatial resolution diffusion MRI. Neuroimage 105, 198–207 (2015). 25449747 PMC4262592 · DOI
  63. [66]Li, J. et al. Exploring the functional connectome in white matter. Hum. Brain Mapp. 40, 4331–4344 (2019). 31276262 PMC6865787 · DOI
  64. [67]Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281 (2010). 19819338 · DOI
  65. [68]Griswold, M. A. et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. J. Int. Soc. Magn. Reson. Med. 47, 1202–1210 (2002). 12111967 · DOI
  66. [69]Setsompop, K. et al. Improving diffusion MRI using simultaneous multi-slice echo planar imaging. Neuroimage 63, 569–580 (2012). 22732564 PMC3429710 · DOI
  67. [71]Kundu, P. et al. Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. Neuroimage 154, 59–80 (2017). 28363836 · DOI
  68. [72]Peirce, J. et al. Psychopy2: Experiments in behavior made easy. Behav. Res. methods 51, 195–203 (2019). 30734206 PMC6420413 · DOI
  69. [73]Moeller, S. et al. NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. Neuroimage 226, 117539 (2021). 33186723 PMC7881933 · DOI
  70. [74]Kellner, E., Dhital, B., Kiselev, V. G. & Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76, 1574–1581 (2016). 26745823 · DOI · Paper page
  71. [76]Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. Fsl. Neuroimage 62, 782–790 (2012). 21979382 · DOI · Paper page
  72. [77]Andersson, J. L., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870–888 (2003). 14568458 · DOI · Paper page
  73. [78]Tustison, N. J. et al. The ANTsX ecosystem for quantitative biological and medical imaging. Sci. Rep. 11, 9068 (2021). 33907199 PMC8079440 · DOI
  74. [79]Hoopes, A., Mora, J. S., Dalca, A. V., Fischl, B. & Hoffmann, M. Synthstrip: skull-stripping for any brain image. NeuroImage 260, 119474 (2022). 35842095 PMC9465771 · DOI
  75. [80]Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. Neuroimage 142, 394–406 (2016). 27523449 PMC5159209 · DOI · Paper page
  76. [81]Veraart, J., Fieremans, E. & Novikov, D. S. Diffusion MRI noise mapping using random matrix theory. Magn. Reson. Med. 76, 1582–1593 (2016). 26599599 PMC4879661 · DOI
  77. [82]Cordero-Grande, L., Christiaens, D., Hutter, J., Price, A. N. & Hajnal, J. V. Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage 200, 391–404 (2019). 31226495 PMC6711461 · DOI
  78. [83]Dhollander, T., Raffelt, D. & Connelly, A. Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. In ISMRM workshop on breaking the barriers of diffusion MRI (Lisbon, Portugal, 2016).
  79. [84]Jeurissen, B., Tournier, J.-D., Dhollander, T., Connelly, A. & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014). 25109526 · DOI
  80. [85]Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010). 20027588 · DOI
  81. [86]Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. imaging 20, 45–57 (2001). 11293691 · DOI
  82. [87]Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012). 22248573 PMC3685476 · DOI · Paper page
  83. [88]Woolrich, M. W., Ripley, B. D., Brady, M. & Smith, S. M. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14, 1370–1386 (2001). 11707093 · DOI
  84. [89]Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M. & Smith, S. M. Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 21, 1732–1747 (2004). 15050594 · DOI
  85. [90]Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016). 27437579 PMC4990127 · DOI · Paper page

Authors
Spencer, A. P. C., Nguyen-Duc, J., de Riedmatten, I., Szczepankiewicz, F., & Jelescu, I. O.
Affiliations
  • Centre Hospitalier Universitaire Vaudois (CH)
  • Lund University (SE)
Corresponding author
Spencer, Arthur P. C.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2025-05-30
Volume
16
Issue
1
Pages
5036-5036
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-025-60357-5
PMC
PMC12125227
OpenAlex
W4410891810
Cited by
12
References
90
Citations by year
2026: 5 · 2025: 7
FWCI
6.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc-nd)
  • IRIS (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Lund University Publications (Lund University) (submittedVersion)
Funders
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (194260)
  • Cancerfonden (220592JIA)
  • Centre d'Imagerie BioMédicale
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
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
apparent diffusion coefficient, BOLD signal, temporal specificity, functional connectivity, diffusion encoding
MeSH terms
Adult, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Oxygen, Diffusion Magnetic Resonance Imaging, Young Adult, White Matter, Gray Matter

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