Abstract
The characterization of the direction of information flow between cortical areas via layer functional connectivity (FC) is of interest for many neuroscientific applications. Most laminar fMRI acquisitions are in the thermal-noise dominated regime due to spatial resolution requirements. However, laminar FC estimates using gradient-echo BOLD might be particularly biased by physiological sources of noise which are amplified by the draining vein effect. In this work, we aimed at assessing whether thermal and physiological denoising can be helpful in reducing biases in laminar functional connectivity studies. We tested NORDIC, RETROICOR, and aCompCor on a dataset acquired at 7T with a resolution of 0.8 × 0.8 × 1.5 mm3 and evaluated the following metrics on a laminar basis after each denoising step: temporal standard deviation (tSD), coefficient of variation (CoV), fluctuation amplitude (FA) and seed-based functional connectivity strength of the primary motor cortex (M1) with premotor (PM) and somatosensory (S1) regions. We found that NORDIC had the largest impact on the metrics considered, mostly in deeper laminae. The application of physiological denoising, especially aCompCor, following NORDIC had the largest effect on upper laminae, in line with the fact that they are more affected by physiological noise than the deeper laminae. The application of NORDIC and aCompCor resulted in laminar functional connectivity results pointing to a stronger connectivity of upper M1 laminae to S1 than lower M1 laminae to S1, and to a reduction in the upper-layer bias in the connectivity to PM. Therefore, both thermal and physiological denoising represent important steps to increase sensitivity and reduce the vascular bias of laminar fMRI data.
Data availability
Data and code will be made available from the corresponding author upon reasonable request.
References
Uludag, K. & Havlicek, M. Determining laminar neuronal activity from BOLD fMRI using a generative model. Prog. Neurobiol. 207, 102055 (2021).
Dumoulin, S. O. Layers of neuroscience. Neuron 96, 1205–1206 (2017).
Kok, P., Bains, L. J., Van Mourik, T., Norris, D. G. & de Lange, F. P. Selective activation of the deep layers of the human primary visual cortex by top-down feedback. Curr. Biol. 26, 371–376 (2016).
Self, M. W., van Kerkoerle, T., Goebel, R. & Roelfsema, P. R. Benchmarking laminar fMRI: Neuronal spiking and synaptic activity during top-down and bottom-up processing in the different layers of cortex. Neuroimage 197, 806–817 (2019).
Deshpande, G., Wang, Y. & Robinson, J. Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain. Brain Informatics 9, 2 (2022).
Yang, J., Huber, L., Yu, Y. & Bandettini, P. A. Linking cortical circuit models to human cognition with laminar fMRI. Neurosci. Biobehav. Rev. 128, 467–478 (2021).
Bastos, A. M. et al. Canonical microcircuits for predictive coding. Neuron 76, 695–711 (2012).
Barbas, H. General cortical and special prefrontal connections: principles from structure to function. Annu. Rev. Neurosci. 38, 269–289 (2015).
Knudsen, L. et al. Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression. Neuroimage 271, 120011 (2023).
Stanley, O. W., Kuurstra, A. B., Klassen, L. M., Menon, R. S. & Gati, J. S. Effects of phase regression on high-resolution functional MRI of the primary visual cortex. Neuroimage 227, 117631 (2021).
Roefs, E. C. et al. The Contribution of the Vascular Architecture and Cerebrovascular Reactivity to the BOLD signal Formation across Cortical Depth. Imaging Neuroscience (2024).
Guidi, M. et al. Cortical laminar resting-state signal fluctuations scale with the hypercapnic blood oxygenation level-dependent response. Hum. Brain Mapp. 41, 2014–2027 (2020).
Kashyap, S., Ivanov, D., Havlicek, M., Poser, B. A. & Uludağ, K. Impact of acquisition and analysis strategies on cortical depth-dependent fMRI. Neuroimage 168, 332–344 (2018).
Koopmans, P. J., Barth, M. & Norris, D. G. Layer-specific BOLD activation in human V1. Hum. Brain Mapp. 31, 1297–1304 (2010).
Chai, Y. et al. Improving laminar fMRI specificity by reducing macrovascular bias revealed by respiration effects. Imaging Neuroscience (2024).
Chen, G., Wang, F., Gore, J. C. & Roe, A. W. Layer-specific BOLD activation in awake monkey V1 revealed by ultra-high spatial resolution functional magnetic resonance imaging. Neuroimage 64, 147–155 (2013).
Markuerkiaga, I., Marques, J. P., Gallagher, T. E. & Norris, D. G. Estimation of laminar BOLD activation profiles using deconvolution with a physiological point spread function. J. Neurosci. Methods 353, 109095 (2021).
Olman, C. A. et al. Layer-specific fMRI reflects different neuronal computations at different depths in human V1. PLoS ONE 7, e32536 (2012).
Kay, K., Jamison, K. W., Zhang, R.-Y. & Uğurbil, K. A temporal decomposition method for identifying venous effects in task-based fMRI. Nat. Methods 17, 1033–1039 (2020).
Priovoulos, N., de Oliveira, I. A. F., Poser, B. A., Norris, D. G. & van Der Zwaag, W. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Hum. Brain Mapp. 44, 2509–2522 (2023).
Schulz, J., Fazal, Z., Metere, R., Marques, J. P. & Norris, D. G. Arterial blood contrast (ABC) enabled by magnetization transfer (MT): a novel MRI technique for enhancing the measurement of brain activation changes. Biorxiv, (2020).
Huber, L. et al. Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications. NeuroImage (2016).
Finn, E. S., Huber, L., Jangraw, D. C., Molfese, P. J. & Bandettini, P. A. Layer-dependent activity in human prefrontal cortex during working memory. Nat. Neurosci. 22, 1687–1695 (2019).
Schellekens, W. et al. The many layers of BOLD. The effect of hypercapnic and hyperoxic stimuli on macro-and micro-vascular compartments quantified by CVR, M, and CBV across cortical depth. Journal of Cerebral Blood Flow Metabol., 43, 419–432 (2023).
Goense, J. B. & Logothetis, N. K. Laminar specificity in monkey V1 using high-resolution SE-fMRI. Magn. Reson. Imaging 24, 381–392 (2006).
Siero, J. C. et al. BOLD specificity and dynamics evaluated in humans at 7 T: comparing gradient-echo and spin-echo hemodynamic responses. PLoS ONE 8, e54560 (2013).
Zhao, F., Wang, P., Hendrich, K., Ugurbil, K. & Kim, S.-G. Cortical layer-dependent BOLD and CBV responses measured by spin-echo and gradient-echo fMRI: insights into hemodynamic regulation. Neuroimage 30, 1149–1160 (2006).
Huber, L., Uludağ, K. & Möller, H. E. Non-BOLD contrast for laminar fMRI in humans: CBF, CBV, and CMRO2. Neuroimage 197, 742–760 (2019).
Shao, X. et al. Laminar perfusion imaging with zoomed arterial spin labeling at 7 Tesla. Neuroimage 245, 118724 (2021).
Kashyap, S. et al. Sub-millimetre resolution laminar fMRI using Arterial Spin Labelling in humans at 7 T. PLoS ONE 16, e0250504 (2021).
Bohraus, Y., Merkle, H., Logothetis, N. K. & Goense, J. Laminar differences in functional oxygen metabolism in monkey visual cortex measured with calibrated fMRI. Cell Reports 42 (2023).
Guidi, M., Huber, L., Lampe, L., Gauthier, C. J. & Möller, H. E. Lamina-dependent calibrated BOLD response in human primary motor cortex. Neuroimage 141, 250–261 (2016).
Shao, X. et al. Laminar multi-contrast fMRI at 7T allows differentiation of neuronal excitation and inhibition underlying positive and negative BOLD responses. Imaging Neuroscience (2024).
Chai, Y., Li, L., Huber, L., Poser, B. A. & Bandettini, P. A. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. Neuroimage 207, 116358 (2020).
Liu, T. T. Noise contributions to the fMRI signal: An overview. Neuroimage 143, 141–151 (2016).
Caballero-Gaudes, C. & Reynolds, R. C. Methods for cleaning the BOLD fMRI signal. NeuroImage (2016).
Bodurka, J., Ye, F., Petridou, N., Murphy, K. & Bandettini, P. A. Mapping the MRI voxel volume in which thermal noise matches physiological noise—implications for fMRI. Neuroimage 34, 542–549 (2007).
Triantafyllou, C. et al. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage 26, 243–250 (2005).
Vizioli, L. et al. Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging. Nat. Commun. 12, 5181 (2021).
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).
Dowdle, L. T. et al. Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies. Neuroimage 270, 119949 (2023).
Faes, L. K. et al. Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC. bioRxiv (2024).
Akbari, A., Gati, J. S., Zeman, P., Liem, B. & Menon, R. S. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD. bioRxiv, (2023).
Schmid, F., Barrett, M. J., Jenny, P. & Weber, B. Vascular density and distribution in neocortex. Neuroimage 197, 792–805 (2019).
Baez-Yanez, M. G. et al. A fully synthetic three-dimensional human cerebrovascular model based on histological characteristics to investigate the hemodynamic fingerprint of the layer BOLD fMRI signal formation. bioRxiv, (2024).
Polimeni, J. R., Bianciardi, M., Keil, B. & Wald, L. L. In Proc.Intl. Soc. Mag. Reson. Med.
Hu, X., Le, T. H., Parrish, T. & Erhard, P. Retrospective estimation and correction of physiological fluctuation in functional MRI. Magn. Reson. Med. 34, 201–212 (1995).
Glover, G. H., Li, T. Q. & Ress, D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167 (2000).
Wise, R. G., Ide, K., Poulin, M. J. & Tracey, I. Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. Neuroimage 21, 1652–1664 (2004).
Pruim, R. H. et al. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage 112, 267–277 (2015).
Beall, E. B. & Lowe, M. J. Isolating physiologic noise sources with independently determined spatial measures. Neuroimage 37, 1286–1300 (2007).
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).
Mascali, D. et al. Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks. Hum. Brain Mapp. 42, 1805–1828 (2021).
Van Schuerbeek, P., De Wandel, L. & Baeken, C. The optimized combination of aCompCor and ICA-AROMA to reduce motion and physiologic noise in task fMRI data. Biomed. Phys. Eng. Express 8, 057001 (2022).
De Blasi, B. et al. Noise removal in resting-state and task fMRI: functional connectivity and activation maps. J. Neural Eng. 17, 046040 (2020).
Pais-Roldán, P., Yun, S. D. & Shah, N. J. Pre-processing of sub-millimeter GE-BOLD fMRI data for laminar applications. Front. neuroimag. 1, 869454 (2022).
Krentz, M. et al. A comparison of fMRI data-derived and physiological data-derived methods for physiological noise correction. Biorxiv, (2023).
Association, W. M. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310, 2191–2194 (2013).
Poser, B. A., Koopmans, P. J., Witzel, T., Wald, L. L. & Barth, M. Three dimensional echo-planar imaging at 7 Tesla. Neuroimage 51, 261–266 (2010).
Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T 1-mapping at high field. Neuroimage 49, 1271–1281 (2010).
Bianciardi, M. et al. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magn. Reson. Imaging 27, 1019–1029 (2009).
Birn, R. M. Quality control procedures and metrics for resting-state functional MRI. Front. Neuroimag. 2, 1072927 (2023).
Gaser, C. et al. CAT–A computational anatomy toolbox for the analysis of structural MRI data. Biorxiv, (2022).
Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).
Huber, L. R. et al. LayNii: A software suite for layer-fMRI. Neuroimage 237, 118091 (2021).
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
Kasper, L. et al. The PhysIO toolbox for modeling physiological noise in fMRI data. J. Neurosci. Methods 276, 56–72 (2017).
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
Huber, L. et al. High-resolution CBV-fMRI allows mapping of laminar activity and connectivity of cortical input and output in human M1. Neuron 96, 1253–1263 (2017).
Zou, Q.-H. et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 172, 137–141 (2008).
Guidi, M., Giulietti, G., Moeller, H. E., Norris, D. G. & Giove, F. Depth-dependent effects of thermal and physiological noise reduction in BOLD fMRI.
Faes, L. K. et al. Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC. Imaging Neurosci. 2, 1–18 (2024).
Knudsen, L. et al. NORDIC denoising on VASO data. Front. Neurosci. https://doi.org/10.3389/fnins.2024.1499762 (2025).
toward understanding the noise. Petridou, N., Schäfer, A., Gowland, P. & Bowtell, R. Phase vs. magnitude information in functional magnetic resonance imaging time series. Magn. Reson. Imaging 27, 1046–1057 (2009).
Barry, R. L., Strother, S. C. & Gore, J. C. Complex and magnitude-only preprocessing of 2D and 3D BOLD fMRI data at 7 T. Magn. Reson. Med. 67, 867–871 (2012).
Tijssen, R. H., Jenkinson, M., Brooks, J. C., Jezzard, P. & Miller, K. L. Optimizing RetroICor and RetroKCor corrections for multi-shot 3D FMRI acquisitions. Neuroimage 84, 394–405 (2014).
Lutti, A., Thomas, D. L., Hutton, C. & Weiskopf, N. High-resolution functional MRI at 3 T: 3D/2D echo-planar imaging with optimized physiological noise correction. Magn. Reson. Med. 69, 1657–1664 (2013).
Kotlarz, P. et al. Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI. biorxiv (2023).
Pfaffenrot, V. et al. Characterizing BOLD activation patterns in the human hippocampus with laminar fMRI. Biorxiv, (2024).
bailes, Sm., Gomez, D. E., Setzer, B. & Lewis, L. D. Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing. Elife 12, e86453 (2023).
Knudsen, L. et al. The laminar pattern of proprioceptive activation in human primary motor cortex. Cerebral Cortex 35, bhaf076 (2025).
Knudsen, L. et al. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv, (2023).
Thomas, C. G. & Menon, R. S. Amplitude response and stimulus presentation frequency response of human primary visual cortex using BOLD EPI at 4 T. Magn. Reson. Med. 40, 203–209 (1998).
Bandettini, P. A. & Cox, R. W. Event-related fMRI contrast when using constant interstimulus interval: theory and experiment. Magnetic Resonance Med. 43, 540–548 (2000).
Taylor, A. J., Kim, J. H. & Ress, D. Characterization of the hemodynamic response function across the majority of human cerebral cortex. Neuroimage 173, 322–331 (2018).
Miezin, F. M., Maccotta, L., Ollinger, J., Petersen, S. & Buckner, R. Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. Neuroimage 11, 735–759 (2000).
Handwerker, D. A., Gonzalez-Castillo, J., D’esposito, M. & Bandettini, P. A. The continuing challenge of understanding and modeling hemodynamic variation in fMRI. Neuroimage 62, 1017–1023 (2012).
Boubela, R. N. et al. Beyond noise: using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Front. Hum. Neurosci. 7, 168 (2013).
Lewis, L. D., Setsompop, K., Rosen, B. R. & Polimeni, J. R. Fast fMRI can detect oscillatory neural activity in humans. Proc. Natl. Acad. Sci. 113, E6679–E6685 (2016).
Siero, J. C., Petridou, N., Hoogduin, H., Luijten, P. R. & Ramsey, N. F. Cortical depth-dependent temporal dynamics of the BOLD response in the human brain. J. Cereb. Blood Flow Metab. 31, 1999–2008 (2011).
Drew, P. J. Vascular and neural basis of the BOLD signal. Curr. Opin. Neurobiol. 58, 61–69 (2019).
Viessmann, O., Jezzard, P. & Möller, H. E. in 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine.
Chen, J. E., Jahanian, H. & Glover, G. H. Nuisance regression of high-frequency functional magnetic resonance imaging data: denoising can be noisy. Brain Connect. 7, 13–24 (2017).
Acknowledgements
We would like to thank Domenica Klank for radiographic assistance and Lauren Bains for MR protocol optimization. Work supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022) CUP B83C22004960002 – SINVASC, by the European Union—Next Generation EU—NRRP M6C2—Investment 2.1 Enhancement and strengthening of biomedical research in the NHS under the grant PNRR-MAD-2022-12376889 CUP I63C22000960007, and by the European Union—Next Generation EU – and Ministero della Salute PNRR PNC-E3-2022-23683266 CUP J83C23000110007 PNC-HLS-DA, INNOVA. LH was supported by the NIH grant 5P41EB030006-05.
Author information
Authors and Affiliations
Contributions
Conceptualization: MG, GG, IM, HEM, DGN, FG; Data curation: MG, GG, IM; Formal analysis: MG, GG, FG; Funding acquisition: HEM, DGN, FG; Investigation: MG, GG, IM; Methodology: MG, DS, LK, LH, HEM, DGN, FG; Project administration: HEM, DGN, FG; Resources: LH, HEM, DGN, FG; Software: BAP, HEM; Supervision: BAP, LH, HEM, DGN, FG; Visualization: MG, GG, DS, LK; Writing – original draft: MG, GG; Writing – review & editing: MG, GG, DS, IM, LK, BAP, LH, HEM, DGN, FG.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Guidi, M., Giulietti, G., Sharoh, D. et al. Impact of thermal and physiological denoising on laminar functional connectivity. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37599-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-37599-4
