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Cited by 4Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Evaluating the dependence of ADC-fMRI on haemodynamics in breath-hold and resting-state conditions

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de Riedmatten, Inès, Spencer, Arthur P.C., Nguyen-Duc, Jasmine, Pérot, Jean-Baptiste, Szczepankiewicz, Filip, Esteban, Oscar, Jelescu, Ileana O.·2025·Imaging Neuroscience
DOI 10.1162/imag.a.1020PMC PMC12630321
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Abstract

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of mapping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by blood-oxygen level dependent (BOLD) effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromorphological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two interleaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm−2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and a pattern of delayed haemodynamic response in white matter. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between expired CO2 and ADC timeseries, as well as low inter- and intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to microstructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds.

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  • 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 de Riedmatten, Spencer, Nguyen-Duc, et al., 2025

    The Discussion reports an earlier diffusion-response onset than the BOLD response and describes BOLD’s delayed haemodynamic response in white matter. It also says dfMRI may balance sensitivity to microstructural changes with good temporal resolution. But the paper does not establish greater spatial or temporal precision for dfMRI than BOLD; it explicitly says ADC-fMRI currently does not match BOLD’s temporal resolution. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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  1. Abe, Y., Takata, N., Sakai, Y., Hamada, H. T., Hiraoka, Y., Aida, T., Tanaka, K., Bihan, D. L., Doya, K., & Tanaka, K. F. (2020). Diffusion functional MRI reveals global brain network functional abnormalities driven by targeted local activity in a neuropsychiatric disease mouse model. NeuroImage, 223, 117318. 10.1016/j.neuroimage.2020.117318 32882386 · DOI
  2. Abe, Y., Tsurugizawa, T., & Le Bihan, D. (2017). Water diffusion closely reveals neural activity status in rat brain loci affected by anesthesia. PLoS Biology, 15(4), e2001494. 10.1371/journal.pbio.2001494 28406906 PMC5390968 · DOI
  3. Addeh, A., Williams, R. J., Golestani, A., Pike, G. B., & MacDonald, M. E. (2025). Physiological confounds in BOLD-fMRI and their correction. NMR in Biomedicine, 38(7), e70076. 10.1002/nbm.70076 40491186 PMC12149699 · DOI
  4. Andersson, J. L., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. NeuroImage, 20(2), 870– 888. 10.1016/s1053-8119(03)00336-7 14568458 · DOI · Paper page
  5. Aso, T., Urayama, S.-I., Fukuyama, H., & Le Bihan, D. (2013). Comparison of diffusion-weighted fMRI and BOLD fMRI responses in a verbal working memory task. NeuroImage, 67, 25– 32. 10.1016/j.neuroimage.2012.11.005 23147237 · DOI
  6. Aso, T., Urayama, S.-I., Poupon, C., Sawamoto, N., Fukuyama, H., & Le Bihan, D. (2009). An intrinsic diffusion response function for analyzing diffusion functional MRI time series. NeuroImage, 47(4), 1487– 1495. 10.1016/j.neuroimage.2009.05.027 19450693 · DOI · Paper page
  7. Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage, 31(4), 1536– 1548. 10.1016/j.neuroimage.2006.02.048 16632379 · DOI
  8. Birn, R. M., Murphy, K., & Bandettini, P. A. (2008). The effect of respiration variations on independent component analysis results of resting state functional connectivity. Human Brain Mapping, 29(7), 740– 750. 10.1002/hbm.20577 18438886 PMC2715870 · DOI · Paper page
  9. Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008). The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage, 40(2), 644– 654. 10.1016/j.neuroimage.2007.11.059 18234517 PMC2533266 · DOI
  10. Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537– 541. 10.1002/mrm.1910340409 8524021 · DOI · Paper page
  11. Bright, M. G., Whittaker, J. R., Driver, I. D., & Murphy, K. (2020). Vascular physiology drives functional brain networks. NeuroImage, 217, 116907. 10.1016/j.neuroimage.2020.116907 32387624 PMC7339138 · DOI
  12. Brookes, M. J., Hale, J. R., Zumer, J. M., Stevenson, C. M., Francis, S. T., Barnes, G. R., Owen, J. P., Morris, P. G., & Nagarajan, S. S. (2011). Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. NeuroImage, 56(3), 1082– 1104. 10.1016/j.neuroimage.2011.02.054 21352925 PMC3224862 · DOI · Paper page
  13. Champagne, A. A., Bhogal, A. A., Coverdale, N. S., Mark, C. I., & Cook, D. J. (2019). A novel perspective to calibrate temporal delays in cerebrovascular reactivity using hypercapnic and hyperoxic respiratory challenges. NeuroImage, 187, 154– 165. 10.1016/j.neuroimage.2017.11.044 29217405 · DOI
  14. Chang, C., Cunningham, J. P., & Glover, G. H. (2009). Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage, 44(3), 857– 869. 10.1016/j.neuroimage.2008.09.029 18951982 PMC2677820 · DOI
  15. Chang, C., & Glover, G. H. (2009). Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI. NeuroImage, 47(4), 1381– 1393. 10.1016/j.neuroimage.2009.04.048 19393322 PMC2721281 · DOI
  16. Chang, C., Thomason, M. E., & Glover, G. H. (2008). Mapping and correction of vascular hemodynamic latency in the BOLD signal. NeuroImage, 43(1), 90– 102. 10.1016/j.neuroimage.2008.06.030 18656545 PMC2587338 · DOI
  17. Chen, J. E., Lewis, L. D., Chang, C., Tian, Q., Fultz, N. E., Ohringer, N. A., Rosen, B. R., & Polimeni, J. R. (2020). Resting-state “physiological networks”. NeuroImage, 213, 116707. 10.1016/j.neuroimage.2020.116707 32145437 PMC7165049 · DOI
  18. Chéreau, R., Saraceno, G. E., Angibaud, J., Cattaert, D., & Nägerl, U. V. (2017). Superresolution imaging reveals activity-dependent plasticity of axon morphology linked to changes in action potential conduction velocity. Proceedings of the National Academy of Sciences of the United States of America, 114(6), 1401– 1406. 10.1073/pnas.1607541114 28115721 PMC5307438 · DOI
  19. Darquié, A., Poline, J.-B., Poupon, C., Saint-Jalmes, H., & Le Bihan, D. (2001). Transient decrease in water diffusion observed in human occipital cortex during visual stimulation. Proceedings of the National Academy of Sciences of the United States of America, 98(16), 9391– 9395. 10.1073/pnas.151125698 11459931 PMC55431 · DOI · Paper page
  20. De Luca, A., Schlaffke, L., Siero, J. C. W., Froeling, M., & Leemans, A. (2019). On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm. Human Brain Mapping, 40(17), 5069– 5082. 10.1002/hbm.24758 31410939 PMC6865683 · DOI
  21. Debaker, C., Djemai, B., Ciobanu, L., Tsurugizawa, T., & Le Bihan, D. (2020). Diffusion MRI reveals in vivo and non-invasively changes in astrocyte function induced by an aquaporin-4 inhibitor. PLoS One, 15(5), e0229702. 10.1371/journal.pone.0229702 32413082 PMC7228049 · DOI
  22. de Riedmatten, I., Spencer, A. P., Olszowy, W., & Jelescu, I. O. (2025). Apparent diffusion coefficient fMRI shines light on white matter resting-state connectivity compared to BOLD. Communications biology, 8(1), 447. 10.1038/s42003-025-07889-0 40091123 PMC11911413 · DOI · Paper page
  23. Donahue, M. J., Lu, H., Jones, C. K., Edden, R. A., Pekar, J. J., & van Zijl, P. C. (2006). Theoretical and experimental investigation of the VASO contrast mechanism. Magnetic Resonance in Medicine, 56(6), 1261– 1273. 10.1002/mrm.21072 17075857 · DOI
  24. Driver, I. D., Whittaker, J. R., Bright, M. G., Muthukumaraswamy, S. D., & Murphy, K. (2016). Arterial CO2 fluctuations modulate neuronal rhythmicity: Implications for MEG and fMRI studies of resting-state networks. Journal of Neuroscience, 36(33), 8541– 8550. 10.1523/jneurosci.4263-15.2016 27535903 PMC4987431 · DOI
  25. DuPre, E., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S., Staden, I, Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M., Whitaker, K., & Handwerk, D. A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669. 10.21105/joss.03669 · DOI
  26. Friston, K. J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M., & Turner, R. (1998). Event-related fMRI: Characterizing differential responses. NeuroImage, 7(1), 30– 40. 10.1006/nimg.1997.0306 9500830 · DOI
  27. Gong, J., Stickland, R. C., & Bright, M. G. (2023). Hemodynamic timing in resting-state and breathing-task BOLD fMRI. NeuroImage, 274, 120120. 10.1016/j.neuroimage.2023.120120 37072074 PMC10208394 · DOI
  28. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253– 258. 10.1073/pnas.0135058100 12506194 PMC140943 · DOI · Paper page
  29. Greitz, D., Wirestam, R., Franck, A., Nordell, B., Thomsen, C., & Ståhlberg, F. (1992). Pulsatile brain movement and associated hydrodynamics studied by magnetic resonance phase imaging. Neuroradiology, 34(5), 370– 380. 10.1007/BF00596493 1407513 · DOI
  30. Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., & Haase, A. (2002). Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 47(6), 1202– 1210. 10.1002/mrm.10171 12111967 · DOI
  31. Handwerker, D. A., Ollinger, J. M., & D’Esposito, M. (2004). Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage, 21(4), 1639– 1651. 10.1016/j.neuroimage.2003.11.029 15050587 · DOI
  32. Hoopes, A., Mora, J. S., Dalca, A. V., Fischl, B., & Hoffmann, M. (2022). SynthStrip: Skull-stripping for any brain image. NeuroImage, 260, 119474. 10.1016/j.neuroimage.2022.119474 35842095 PMC9465771 · DOI
  33. Iima, M., & Le Bihan, D. (2016). Clinical intravoxel incoherent motion and diffusion MR imaging: Past, present, and future. Radiology, 278(1), 13– 32. 10.1148/radiol.2015150244 26690990 · DOI · Paper page
  34. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. NeuroImage, 62(2), 782– 790. 10.1016/j.neuroimage.2011.09.015 21979382 · DOI · Paper page
  35. Jin, T., & Kim, S.-G. (2010). Change of the cerebrospinal fluid volume during brain activation investigated by T1 ρ-weighted fMRI. NeuroImage, 51(4), 1378– 1383. 10.1016/j.neuroimage.2010.03.047 20338251 PMC2873771 · DOI
  36. Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine, 76(5), 1574– 1581. 10.1002/mrm.26054 26745823 · DOI · Paper page
  37. Kluger, D. S., & Gross, J. (2021). Respiration modulates oscillatory neural network activity at rest. PLoS Biology, 19(11), e3001457. 10.1371/journal.pbio.3001457 34762645 PMC8610250 · DOI · Paper page
  38. Komaki, Y., Debacker, C., Djemai, B., Ciobanu, L., Tsurugizawa, T., & Bihan, D. L. (2020). Differential effects of aquaporin-4 channel inhibition on BOLD fMRI and diffusion fMRI responses in mouse visual cortex. PLoS One, 15(5), e0228759. 10.1371/journal.pone.0228759 32437449 PMC7241787 · DOI · Paper page
  39. Kundu, P., Voon, V., Balchandani, P., Lombardo, M. V., Poser, B. A., & Bandettini, P. A. (2017). Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage, 154, 59– 80. 10.1016/j.neuroimage.2017.03.033 28363836 · DOI
  40. Kwon, J., Lee, S., Jo, Y., & Choi, M. (2023). All-optical observation on activity-dependent nanoscale dynamics of myelinated axons. Neurophotonics, 10(1), 015003. 10.1117/1.NPh.10.1.015003 36699624 PMC9868287 · DOI
  41. Le Bihan, D. (2012). Diffusion, confusion and functional MRI. NeuroImage, 62(2), 1131– 1136. 10.1016/j.neuroimage.2011.09.058 21985905 · DOI
  42. Le Bihan, D. (2019). What can we see with IVIM MRI? NeuroImage, 187, 56– 67. 10.1016/j.neuroimage.2017.12.062 29277647 · DOI · Paper page
  43. Li, M., Newton, A. T., Anderson, A. W., Ding, Z., & Gore, J. C. (2019). Characterization of the hemodynamic response function in white matter tracts for event-related fMRI. Nature Communications, 10(1), 1140. 10.1038/s41467-019-09076-2 PMC6408456 30850610 · DOI
  44. Ling, T., Boyle, K. C., Zuckerman, V., Flores, T., Ramakrishnan, C., Deisseroth, K., & Palanker, D. (2020). High-speed interferometric imaging reveals dynamics of neuronal deformation during the action potential. Proceedings of the National Academy of Sciences of the United States of America, 117(19), 10278– 10285. 10.1073/pnas.1920039117 32341158 PMC7229674 · DOI
  45. Liu, P., Uh, J., Devous, M. D., Adinoff, B., & Lu, H. (2012). Comparison of relative cerebral blood flow maps using pseudo-continuous arterial spin labeling and single photon emission computed tomography. NMR in Biomedicine, 25(5), 779– 786. 10.1002/nbm.1792 22139764 PMC3298573 · DOI
  46. Manjón, J. V., Coupé, P., Martı-Bonmatı, L., Collins, D. L., & Robles, M. (2010). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192– 203. 10.1002/jmri.22003 20027588 · DOI
  47. Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P.-F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage, 49(2), 1271– 1281. 10.1016/j.neuroimage.2009.10.002 19819338 · DOI
  48. Miller, K. L., Bulte, D. P., Devlin, H., Robson, M. D., Wise, R. G., Woolrich, M. W., Jezzard, P., & Behrens, T. E. J. (2007). Evidence for a vascular contribution to diffusion FMRI at high b value. Proceedings of the National Academy of Sciences of the United States of America, 104(52), 20967– 20972. 10.1073/pnas.0707257105 18093924 PMC2409250 · DOI · Paper page
  49. Moeller, S., Pisharady, P. K., Ramanna, S., Lenglet, C., Wu, X., Dowdle, L., Yacoub, E., Uğurbil, K., & Akçakaya, M. (2021). NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing. NeuroImage, 226, 117539. 10.1016/j.neuroimage.2020.117539 33186723 PMC7881933 · DOI
  50. Murphy, K., Harris, A. D., & Wise, R. G. (2011). Robustly measuring vascular reactivity differences with breath-hold: Normalising stimulus-evoked and resting state BOLD fMRI data. NeuroImage, 54(1), 369– 379. 10.1016/j.neuroimage.2010.07.059 20682354 · DOI
  51. Nicolas, R., Gros-Dagnac, H., Aubry, F., & Celsis, P. (2017). Comparison of BOLD, diffusion-weighted fMRI and ADC-fMRI for stimulation of the primary visual system with a block paradigm. Magnetic Resonance Imaging, 39, 123– 131. 10.1016/j.mri.2017.01.022 28163122 · DOI
  52. Nunes, D., Gil, R., & Shemesh, N. (2021). A rapid-onset diffusion functional MRI signal reflects neuromorphological coupling dynamics. NeuroImage, 231, 117862. 10.1016/j.neuroimage.2021.117862 33592243 · DOI · Paper page
  53. O’Herron, P., Chhatbar, P. Y., Levy, M., Shen, Z., Schramm, A. E., Lu, Z., & Kara, P. (2016). Neural correlates of single-vessel haemodynamic responses in vivo. Nature, 534(7607), 378– 382. 10.1038/nature17965 27281215 PMC4911280 · DOI
  54. Olszowy, W., Diao, Y., & Jelescu, I. O. (2021, May). Beyond BOLD: Evidence for diffusion fMRI contrast in the human brain distinct from neurovascular response. bioRxiv. 10.1101/2021.05.16.444253 · DOI
  55. Özbay, P. S., Chang, C., Picchioni, D., Mandelkow, H., Moehlman, T. M., Chappel-Farley, M. G., van Gelderen, P., de Zwart, J. A., & Duyn, J. H. (2018). Contribution of systemic vascular effects to fMRI activity in white matter. NeuroImage, 176, 541– 549. 10.1016/j.neuroimage.2018.04.045 29704614 PMC7328303 · DOI
  56. Pampel, A., Jochimsen, T. H., & Möller, H. E. (2010). BOLD background gradient contributions in diffusion-weighted fMRI–comparison of spin-echo and twice-refocused spin-echo sequences. NMR in Biomedicine, 23(6), 610– 618. 10.1002/nbm.1502 20235336 · DOI
  57. Peirce, J., Gray, J. R., Simpson, S., MacAskill, M., Höchenberger, R., Sogo, H., Kastman, E., & Lindeløv, J. K. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51, 195– 203. 10.3758/s13428-018-01193-y 30734206 PMC6420413 · DOI
  58. Piechnik, S. K., Evans, J., Bary, L. H., Wise, R. G., & Jezzard, P. (2009). Functional changes in CSF volume estimated using measurement of water T2 relaxation. Magnetic Resonance in Medicine, 61(3), 579– 586. 10.1002/mrm.21897 19132756 · DOI
  59. Pinto, J., Bright, M. G., Bulte, D. P., & Figueiredo, P. (2021). Cerebrovascular reactivity mapping without gas challenges: A methodological guide. Frontiers in Physiology, 11, 608475. 10.3389/fphys.2020.608475 33536935 PMC7848198 · DOI
  60. Poser, B. A., & Norris, D. G. (2007). Fast spin echo sequences for BOLD functional MRI. Magnetic Resonance Materials in Physics, Biology and Medicine, 20(1), 11– 17. 10.1007/s10334-006-0063-x PMC2798036 17245581 · DOI
  61. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142– 2154. 10.1016/j.neuroimage.2011.10.018 22019881 PMC3254728 · DOI · Paper page
  62. Quaegebeur, A., Lange, C., & Carmeliet, P. (2011). The neurovascular link in health and disease: Molecular mechanisms and therapeutic implications. Neuron, 71(3), 406– 424. 10.1016/j.neuron.2011.07.013 21835339 · DOI · Paper page
  63. Rydhög, A. S., Szczepankiewicz, F., Wirestam, R., Ahlgren, A., Westin, C.-F., Knutsson, L., & Pasternak, O. (2017). Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain. NeuroImage, 156, 423– 434. 10.1016/j.neuroimage.2017.04.023 28412443 PMC5548601 · DOI
  64. Sherpa, A. D., Xiao, F., Joseph, N., Aoki, C., & Hrabetova, S. (2016). Activation of β-adrenergic receptors in rat visual cortex expands astrocytic processes and reduces extracellular space volume. Synapse (New York, N.Y.), 70(8), 307– 316. 10.1002/syn.21908 27085090 PMC4909535 · DOI
  65. Spees, W. M., Lin, T.-H., & Song, S.-K. (2013). White-matter diffusion fMRI of mouse optic nerve. NeuroImage, 65, 209– 215. 10.1016/j.neuroimage.2012.10.021 23085108 PMC3513522 · DOI
  66. Spencer, A. P., Nguyen-Duc, J., de Riedmatten, I., Szczepankiewicz, F., & Jelescu, I. O. (2025). Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI. Nature Communications, 16(1), 1– 12. 10.1038/s41467-025-60357-5 PMC12125227 40447634 · DOI · Paper page
  67. Szczepankiewicz, F., & Sjölund, J. (2021). Cross-term-compensated gradient waveform design for tensor-valued diffusion MRI. Journal of Magnetic Resonance, 328, 106991. 10.1016/j.jmr.2021.106991 33984713 · DOI
  68. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Doyne Farmer, J. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica D: Nonlinear Phenomena, 58(1), 77– 94. 10.1016/0167-2789(92)90102-S · DOI
  69. Thomas, B. P., Liu, P., Park, D. C., van Osch, M. J., & Lu, H. (2014). Cerebrovascular reactivity in the brain white matter: Magnitude, temporal characteristics, and age effects. Journal of Cerebral Blood Flow & Metabolism, 34(2), 242– 247. 10.1038/jcbfm.2013.194 24192640 PMC3915204 · DOI
  70. Tong, Y., Hocke, L. M., Fan, X., Janes, A. C., & Frederick, B. d. (2015). Can apparent resting state connectivity arise from systemic fluctuations? Frontiers in Human Neuroscience, 9, 285. 10.3389/fnhum.2015.00285 26029095 PMC4432665 · DOI
  71. Tong, Y., Lindsey, K. P., Hocke, L. M., Vitaliano, G., Mintzopoulos, D., & Frederick, B. d. (2017). Perfusion information extracted from resting state functional magnetic resonance imaging. Journal of Cerebral Blood Flow & Metabolism, 37(2), 564– 576. 10.1177/0271678X16631755 26873885 PMC5381451 · DOI
  72. Tsurugizawa, T., Ciobanu, L., & Le Bihan, D. (2013). Water diffusion in brain cortex closely tracks underlying neuronal activity. Proceedings of the National Academy of Sciences of the United States of America, 110(28), 11636– 11641. 10.1073/pnas.1303178110 23801756 PMC3710802 · DOI · Paper page
  73. Tustison, N. J., Cook, P. A., Holbrook, A. J., Johnson, H. J., Muschelli, J., Devenyi, G. A., Duda, J. T., Das, S. R., Cullen, N. C., Gillen, D. L., Yassa, M. A., Stone, J. R., Gee, J. C., & Avants, B. B. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Scientific Reports, 11(1), 9068. 10.1038/s41598-021-87564-6 33907199 PMC8079440 · DOI
  74. Williams, R. J., McMahon, K. L., Hocking, J., & Reutens, D. C. (2014). Comparison of block and event-related experimental designs in diffusion-weighted functional MRI. Journal of Magnetic Resonance Imaging, 40(2), 367– 375. 10.1002/jmri.24353 24923816 · DOI
  75. Williams, R. J., Reutens, D. C., & Hocking, J. (2015). Functional localization of the human color center by decreased water displacement using diffusion-weighted fMRI. Brain and Behavior, 5(11), e00408. 10.1002/brb3.408 26664792 PMC4667755 · DOI
  76. Yuan, H., Zotev, V., Phillips, R., & Bodurka, J. (2013). Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. NeuroImage, 79, 81– 93. 10.1016/j.neuroimage.2013.04.068 23631982 · DOI
  77. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45– 57. 10.1109/42.906424 11293691 · DOI
  78. Zvolanek, K. M., Moia, S., Dean, J. N., Stickland, R. C., Caballero-Gaudes, C., & Bright, M. G. (2023). Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI. NeuroImage, 272, 120038. 10.1016/j.neuroimage.2023.120038 36958618 PMC10266270 · DOI

Cited by

Sorted by citations

Authors
de Riedmatten, I., Spencer, A. P. C., Nguyen-Duc, J., Pérot, J.-B., Szczepankiewicz, F., Esteban, O., & Jelescu, I. O.
Affiliations
  • Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
  • Centre Hospitalier Universitaire Vaudois (CH)
  • Lund University (SE)
Corresponding author
Spencer, Arthur P.C.
ORCID
Venue
Imaging Neuroscience
Publisher
MIT Press
Published
2025
Volume
3
ISSN
2837-6056 (ISSN-L)
DOI
10.1162/imag.a.1020
PMC
PMC12630321
OpenAlex
W4416738434
Cited by
4
References
7
Citations by year
2026: 3 · 2025: 1
FWCI
2.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Imaging Neuroscience (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS (submittedVersion, cc-by)
  • IRIS (submittedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Funders
  • Swiss Secretariat for Research and Innovation (‘FIREPATH’ MB22.00032)
  • SNSF (194260)
  • Swedish Cancer Society (22 0592 JIA)
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, hemodynamic response, respiration, end-tidal carbon dioxide, breath-holding, resting state, white matter, functional neuroimaging, reproducibility

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