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Real-time and Recursive Estimators for Functional MRI Quality Assessment

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Abstract

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

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Data Availability

The software repositories and test datasets are openly available at https://github.com/OpenNFT. All datasets are available from the corresponding author on reasonable request.

References

  • Afyouni, S., & Nichols, T. E. (2018). Insight and inference for DVARS. NeuroImage, 172, 291–312.

    Article  PubMed  Google Scholar 

  • Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L. R., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E., Vidaurre, D., Webster, M., McCarthy, P., Rorden, C., Daducci, A., Alexander, D. C., Zhang, H., Dragonu, I., Matthews, P. M., … Smith, S. M. (2018). Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage, 166, 400–424.

    Article  PubMed  Google Scholar 

  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38, 95–113.

    Article  PubMed  Google Scholar 

  • Astrakas, L. G., Kallistis, N. S., & Kalef-Ezra, J. A. (2016). Technical Note: Independent component analysis for quality assurance in functional MRI. Medical Physics, 43, 983–992.

    Article  PubMed  Google Scholar 

  • Bagarinao, E., Matsuo, K., Nakai, T., & Sato, S. (2003). Estimation of general linear model coefficients for real-time application. NeuroImage, 19, 422–429.

    Article  CAS  PubMed  Google Scholar 

  • Bagarinao, E., Nakai, T., & Tanaka, Y. (2006). Real-time functional MRI: Development and emerging applications. Magnetic Resonance in Medical Sciences, 5, 157–165.

    Article  PubMed  Google Scholar 

  • Basilio, R., Garrido, G. J., Sato, J. R., Hoefle, S., Melo, B. R., Pamplona, F. A., Zahn, R., & Moll, J. (2015). FRIEND Engine Framework: A real time neurofeedback client-server system for neuroimaging studies. Frontiers in Behavioral Neuroscience, 9, 3.

    Article  PubMed  PubMed Central  Google Scholar 

  • Benigno, G.B., Menon, R.S., Serrai, H. (2021). Schrodinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact. NeuroImage 226, 117525.

  • Bolton, T.A.W., Kebets, V., Glerean, E., Zöller, D., Li, J., Yeo, B.T.T., Caballero-Gaudes, C., Van De Ville, D. (2020). Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI. NeuroImage, 209, 116433.

  • Cox, R. W., & Jesmanowicz, A. (1999). Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine, 42, 1014–1018.

    Article  CAS  PubMed  Google Scholar 

  • DeDora, D.J., Nedic, S., Katti, P., Arnab, S., Wald, L.L., Takahashi, A., Van Dijk, K.R.A., Strey, H.H., Mujica-Parodi, L.R. (2016). Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks. Front Neuroscience 10

  • Diedrichsen, J., & Shadmehr, R. (2005). Detecting and adjusting for artifacts in fMRI time series data. NeuroImage, 27, 624–634.

    Article  PubMed  Google Scholar 

  • Dosenbach, N. U. F., Koller, J. M., Earl, E. A., Miranda-Dominguez, O., Klein, R. L., Van, A. N., Snyder, A. Z., Nagel, B. J., Nigg, J. T., Nguyen, A. L., Wesevich, V., Greene, D. J., & Fair, D. A. (2017). Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage, 161, 80–93.

    Article  PubMed  Google Scholar 

  • Esteban, O., Birman, D., Schaer, M., Koyejo, O.O., Poldrack, R.A., Gorgolewski, K.J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One 12, e0184661.

  • Fair, D.A., Miranda-Dominguez, O., Snyder, A.Z., Perrone, A., Earl, E.A., Van, A.N., Koller, J.M., Feczko, E., Tisdall, M.D., van der Kouwe, A., Klein, R.L., Mirro, A.E., Hampton, J.M., Adeyemo, B., Laumann, T.O., Gratton, C., Greene, D.J., Schlaggar, B.L., Hagler, D.J., Jr., Watts, R., Garavan, H., Barch, D.M., Nigg, J.T., Petersen, S.E., Dale, A.M., Feldstein-Ewing, S.W., Nagel, B.J., Dosenbach, N.U.F. (2020). Correction of respiratory artifacts in MRI head motion estimates. NeuroImage 208, 116400.

  • Friedman, L., & Glover, G. H. (2006). Report on a multicenter fMRI quality assurance protocol. Journal of Magnetic Resonance Imaging, 23, 827–839.

    Article  PubMed  Google Scholar 

  • Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19, 1273–1302.

    Article  CAS  PubMed  Google Scholar 

  • Geissler, A., Gartus, A., Foki, T., Tahamtan, A. R., Beisteiner, R., & Barth, M. (2007). Contrast-to-noise ratio (CNR) as a quality parameter in fMRI. Journal of Magnetic Resonance Imaging, 25, 1263–1270.

    Article  PubMed  Google Scholar 

  • Glover, G. H., Li, T. Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44, 162–167.

    Article  CAS  PubMed  Google Scholar 

  • Goto, M., Abe, O., Miyati, T., Yamasue, H., Gomi, T., & Takeda, T. (2016). Head Motion and Correction Methods in Resting-state Functional MRI. Magnetic Resonance in Medical Sciences, 15, 178–186.

    Article  PubMed  Google Scholar 

  • Greve, D. N., Mueller, B. A., Liu, T., Turner, J. A., Voyvodic, J., Yetter, E., Diaz, M., McCarthy, G., Wallace, S., Roach, B. J., Ford, J. M., Mathalon, D. H., Calhoun, V. D., Wible, C. G., Brown, G. G., Potkin, S. G., & Glover, G. (2011). A novel method for quantifying scanner instability in fMRI. Magnetic Resonance in Medicine, 65, 1053–1061.

    Article  PubMed  Google Scholar 

  • Heunis, S., Lamerichs, R., Zinger, S., Caballero-Gaudes, C., Jansen, J. F. A., Aldenkamp, B., & Breeuwer, M. (2020). Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Human Brain Mapping, 41, 3439–3467.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. Neuroimage, 62, 782–790.

    Article  PubMed  Google Scholar 

  • Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T. U., Sebold, M., Manjaly, Z. M., Pruessmann, K. P., & Stephan, K. E. (2017). The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. Journal of Neuroscience Methods, 276, 56–72.

    Article  PubMed  Google Scholar 

  • Kopel, R., Sladky, R., Laub, P., Koush, Y., Robineau, F., Hutton, C., Weiskopf, N., Vuilleumier, P., Van De Ville, D., & Scharnowski, F. (2019). No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI. NeuroImage, 191, 421–429.

    Article  CAS  PubMed  Google Scholar 

  • Koush, Y., Ashburner, J., Prilepin, E., Sladky, R., Zeidman, P., Bibikov, S., Scharnowski, F., Nikonorov, A., & De Ville, D. V. (2017a). OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. NeuroImage, 156, 489–503.

    Article  PubMed  Google Scholar 

  • Koush, Y., Ashburner, J., Prilepin, E., Sladky, R., Zeidman, P., Bibikov, S., Scharnowski, F., Nikonorov, A., & Van De Ville, D. (2017b). Real-time fMRI data for testing OpenNFT functionality. Data in Brief, 14, 344–347.

    Article  PubMed  PubMed Central  Google Scholar 

  • Koush, Y., Meskaldji, D.-E., Pichon, S., Rey, G., Rieger, S. W., Linden, D. E., Van De Ville, D., Vuilleumier, P., & Scharnowski, F. (2017c). Learning control over emotion networks through connectivity-based neurofeedback. Cerebral Cortex, 27, 1193–1202.

    PubMed  Google Scholar 

  • Koush, Y., Rosa, M.J., Robineau, F., Heinen, K., S, W.R., Weiskopf, N., Vuilleumier, P., Van De Ville, D., Scharnowski, F. (2013). Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI. NeuroImage 81, 422-430.

  • Koush, Y., Zvyagintsev, M., Dyck, M., Mathiak, K. A., & Mathiak, K. (2012). Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI. NeuroImage, 59, 478–489.

    Article  PubMed  Google Scholar 

  • Krylova, M., Skouras, S., Razi, A., Nicholson, A. A., Karner, A., Steyrl, D., Boukrina, O., Rees, G., Scharnowski, F., & Koush, Y. (2021). Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks. Science and Reports, 11, 23363.

    Article  CAS  Google Scholar 

  • Lancaster, J. L., Woldorff, M. G., Parsons, L. M., Liotti, M., Freitas, E. S., Rainey, L., Kochunov, P. V., Nickerson, D., Mikiten, S. A., & Fox, P. T. (2000). Automated Talairach Atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lorenz, R., Monti, R.P., Hampshire, A., Koush, Y., Anagnostopoulos, C., Faisal, A.A., Sharp, D., Montana, G., Leech, R., Violante, I.R. (2016). Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization. 2016 6th International Workshop on Pattern Recognition in Neuroimaging (Prni), 49–52.

  • Lu, W., Dong, K., Cui, D., Jiao, Q., & Qiu, J. (2019). Quality assurance of human functional magnetic resonance imaging: A literature review. Quantitative Imaging in Medicine and Surgery, 9, 1147–1162.

    Article  PubMed  PubMed Central  Google Scholar 

  • MacInnes, J. J., Adcock, R. A., Stocco, A., Prat, C. S., Rao, R. P. N., & Dickerson, K. C. (2020). Pyneal: Open Source Real-Time fMRI Software. Frontiers in Neuroscience, 14, 900.

    Article  PubMed  PubMed Central  Google Scholar 

  • Maziero, D., Rondinoni, C., Marins, T., Stenger, V.A., Ernst, T. (2020). Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion. NeuroImage 212.

  • Misaki, M., Barzigar, N., Zotev, V., Phillips, R., Cheng, S., & Bodurka, J. (2015). Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis. Journal of Neuroscience Methods, 256, 117–121.

    Article  PubMed  Google Scholar 

  • Murphy, K., Bodurka, J., & Bandettini, P. A. (2007). How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. NeuroImage, 34, 565–574.

    Article  PubMed  Google Scholar 

  • Nakai, T., Bagarinao, E., Matsuo, K., Ohgami, Y., & Kato, C. (2006). Dynamic monitoring of brain activation under visual stimulation using fMRI–the advantage of real-time fMRI with sliding window GLM analysis. Journal of Neuroscience Methods, 157, 158–167.

    Article  PubMed  Google Scholar 

  • Parkes, L., Fulcher, B., Yucel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage, 171, 415–436.

    Article  PubMed  Google Scholar 

  • Patel, A. X., Kundu, P., Rubinov, M., Jones, P. S., Vertes, P. E., Ersche, K. D., Suckling, J., & Bullmore, E. T. (2014). A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage, 95, 287–304.

    Article  PubMed  Google Scholar 

  • Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Comparing dynamic causal models. NeuroImage, 22, 1157–1172.

    Article  CAS  PubMed  Google Scholar 

  • Posse, S., Ackley, E., Mutihac, R., Rick, J., Shane, M., Murray-Krezan, C., Zaitsev, M., & Speck, O. (2012). Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging. NeuroImage, 61, 115–130.

    Article  PubMed  Google Scholar 

  • 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, 2142–2154.

    Article  PubMed  Google Scholar 

  • Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341.

    Article  PubMed  Google Scholar 

  • Ros, T., Enriquez-Geppert, S., Zotev, V., Young, K.D., Wood, G., Whitfield-Gabrieli, S., Wan, F., Vuilleumier, P., Vialatte, F., Van De Ville, D., Todder, D., Surmeli, T., Sulzer, J.S., Strehl, U., Sterman, M.B., Steiner, N.J., Sorger, B., Soekadar, S.R., Sitaram, R., Sherlin, L.H., Schonenberg, M., Scharnowski, F., Schabus, M., Rubia, K., Rosa, A., Reiner, M., Pineda, J.A., Paret, C., Ossadtchi, A., Nicholson, A.A., Nan, W., Minguez, J., Micoulaud-Franchi, J.A., Mehler, D.M.A., Luhrs, M., Lubar, J., Lotte, F., Linden, D.E.J., Lewis-Peacock, J.A., Lebedev, M.A., Lanius, R.A., Kubler, A., Kranczioch, C., Koush, Y., Konicar, L., Kohl, S.H., Kober, S.E., Klados, M.A., Jeunet, C., Janssen, T.W.P., Huster, R.J., Hoedlmoser, K., Hirshberg, L.M., Heunis, S., Hendler, T., Hampson, M., Guggisberg, A.G., Guggenberger, R., Gruzelier, J.H., Gobel, R.W., Gninenko, N., Gharabaghi, A., Frewen, P., Fovet, T., Fernandez, T., Escolano, C., Ehlis, A.C., Drechsler, R., Christopher deCharms, R., Debener, S., De Ridder, D., Davelaar, E.J., Congedo, M., Cavazza, M., Breteler, M.H.M., Brandeis, D., Bodurka, J., Birbaumer, N., Bazanova, O.M., Barth, B., Bamidis, P.D., Auer, T., Arns, M., Thibault, R.T. (2020). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain, 143, 1674-1685.

  • Sato, J.R., Basilio, R., Paiva, F.F., Garrido, G.J., Bramati, I.E., Bado, P., Tovar-Moll, F., Zahn, R., Moll, J. (2013). Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox. PLoS One 8, e81658.

  • Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H., Gur, R. C., Gur, R. E., & Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256.

    Article  PubMed  Google Scholar 

  • Scheinost, D., Papademetris, X., & Constable, R. T. (2014). The impact of image smoothness on intrinsic functional connectivity and head motion confounds. NeuroImage, 95, 13–21.

    Article  PubMed  Google Scholar 

  • Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns. Cerebral Cortex, 22, 158–165.

    Article  CAS  PubMed  Google Scholar 

  • Simmons, A., Moore, E., & Williams, S. C. R. (1999). Quality Control for Functional Magnetic Resonance Imaging Using Automated Data Analysis and Shewhart Charting. Magnetic Resonance in Medicine, 41, 1274–1278.

    Article  CAS  PubMed  Google Scholar 

  • Stöcker, T., Schneider, F., Klein, M., Habel, U., Kellermann, T., Zilles, K., & Shah, N. J. (2005). Automated quality assurance routines for fMRI data applied to a multicenter study. Human Brain Mapping, 25, 237–246.

    Article  PubMed  PubMed Central  Google Scholar 

  • Triantafyllou, C., Polimeni, J. R., & Wald, L. L. (2011). Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils. NeuroImage, 55, 597–606.

    Article  PubMed  Google Scholar 

  • van der Zwaag, W., Marques, J. P., Kober, T., Glover, G., Gruetter, R., & Krueger, G. (2012). Temporal SNR characteristics in segmented 3D-EPI at 7T. Magnetic Resonance in Medicine, 67, 344–352.

    Article  PubMed  Google Scholar 

  • Van Dijk, K. R., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59, 431–438.

    Article  PubMed  Google Scholar 

  • Weiskopf, N., Sitaram, R., Josephs, O., Veit, R., Scharnowski, F., Goebel, R., Birbaumer, N., Deichmann, R., & Mathiak, K. (2007). Real-time functional magnetic resonance imaging: Methods and applications. Magnetic Resonance Imaging, 25, 989–1003.

    Article  PubMed  Google Scholar 

  • Welford, B. P. (1962). Note on a Method for Calculating Corrected Sums of Squares and Products. Technometrics, 4, 419–420.

    Article  Google Scholar 

  • Welvaert, M., Rosseel, Y. (2013). On the definition of signal-to-noise ratio and contrast-to-noise ratio for FMRI data. PLoS One 8, e77089.

  • Wu, D. H., Lewin, J. S., & Duerk, J. L. (1997). Inadequacy of motion correction algorithms in functional MRI: Role of susceptibility-induced artifacts. Journal of Magnetic Resonance Imaging, 7, 365–370.

    Article  CAS  PubMed  Google Scholar 

  • Zilverstand, A., Sorger, B., Slaats-Willemse, D., Kan, C.C., Goebel, R., Buitelaar, J.K. (2017). fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study. PLoS One 12, e0170795.

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Acknowledgements

ND, EP, AN were supported by RFBR 20-31-90113A and 19-29-01235MK grants. TA was supported by the Biotechnology and Biological Sciences Research Council, London (BB/S008314/1).

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Authors and Affiliations

  1. Aligned Research Group, Los Gatos, USA

    Nikita Davydov & Evgeny Prilepin

  2. Samara National Research University, Samara, Russia

    Nikita Davydov & Artem Nikonorov

  3. Image Processing Systems Institute, Russian Academy of Science, Samara, Russia

    Nikita Davydov & Artem Nikonorov

  4. Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland

    Lucas Peek

  5. School of Psychology, University of Surrey, Guildford, UK

    Tibor Auer

  6. Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Nicolas Gninenko & Dimitri Van De Ville

  7. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland

    Nicolas Gninenko & Dimitri Van De Ville

  8. Department of Radiology and Medical Imaging, Yale University, New Haven, USA

    Yury Koush

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  1. Nikita Davydov
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  2. Lucas Peek
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  3. Tibor Auer
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  4. Evgeny Prilepin
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  5. Nicolas Gninenko
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  6. Dimitri Van De Ville
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  8. Yury Koush
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Contributions

YK – conceived and coordinated the study; all authors contributed to data analyses and/or software tools; ND, YK – wrote the manuscript; all authors critically edited the manuscript.

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Correspondence to Yury Koush.

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Highlights

•  Recursive methods for real-time fMRI quality assessment (rtQA)

•  Implementation of the rtQA as an extension in the open-source OpenNFT software

•  Interactive visualization of rtQA parameters for time-series and whole-brain data

•  OpenNFT supports task, rest, and neurofeedback experimental paradigms

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Davydov, N., Peek, L., Auer, T. et al. Real-time and Recursive Estimators for Functional MRI Quality Assessment. Neuroinform 20, 897–917 (2022). https://doi.org/10.1007/s12021-022-09582-7

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