@article{RN251,
   author = {Heunis, S. and Lamerichs, R. and Zinger, S. and Caballero-Gaudes, C. and Jansen, J. F. A. and Aldenkamp, B. and Breeuwer, M.},
   title = {Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review},
   journal = {Hum Brain Mapp},
   note = {Heunis, Stephan
Lamerichs, Rolf
Zinger, Svitlana
Caballero-Gaudes, Cesar
Jansen, Jacobus F A
Aldenkamp, Bert
Breeuwer, Marcel
eng
LSHM16053-SGF/LSH-TKI
Philips Research
Review
2020/04/26 06:00
Hum Brain Mapp. 2020 Apr 25. doi: 10.1002/hbm.25010.},
   abstract = {Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.},
   keywords = {denoising
fMRI
neurofeedback
quality
real-time
reproducibility},
   ISSN = {1097-0193 (Electronic)
1065-9471 (Linking)},
   DOI = {10.1002/hbm.25010},
   url = {http://www.ncbi.nlm.nih.gov/pubmed/32333624
https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25010},
   year = {2020},
   type = {Journal Article}
}

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A.","Aldenkamp, B.","Breeuwer, M."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Heunis"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Lamerichs"],"firstnames":["R."],"suffixes":[]},{"propositions":[],"lastnames":["Zinger"],"firstnames":["S."],"suffixes":[]},{"propositions":[],"lastnames":["Caballero-Gaudes"],"firstnames":["C."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Aldenkamp"],"firstnames":["B."],"suffixes":[]},{"propositions":[],"lastnames":["Breeuwer"],"firstnames":["M."],"suffixes":[]}],"title":"Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review","journal":"Hum Brain Mapp","note":"Heunis, Stephan Lamerichs, Rolf Zinger, Svitlana Caballero-Gaudes, Cesar Jansen, Jacobus F A Aldenkamp, Bert Breeuwer, Marcel eng LSHM16053-SGF/LSH-TKI Philips Research Review 2020/04/26 06:00 Hum Brain Mapp. 2020 Apr 25. doi: 10.1002/hbm.25010.","abstract":"Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.","keywords":"denoising fMRI neurofeedback quality real-time reproducibility","issn":"1097-0193 (Electronic) 1065-9471 (Linking)","doi":"10.1002/hbm.25010","url":"http://www.ncbi.nlm.nih.gov/pubmed/32333624 https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25010","year":"2020","bibtex":"@article{RN251,\n author = {Heunis, S. and Lamerichs, R. and Zinger, S. and Caballero-Gaudes, C. and Jansen, J. F. A. and Aldenkamp, B. and Breeuwer, M.},\n title = {Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review},\n journal = {Hum Brain Mapp},\n note = {Heunis, Stephan\nLamerichs, Rolf\nZinger, Svitlana\nCaballero-Gaudes, Cesar\nJansen, Jacobus F A\nAldenkamp, Bert\nBreeuwer, Marcel\neng\nLSHM16053-SGF/LSH-TKI\nPhilips Research\nReview\n2020/04/26 06:00\nHum Brain Mapp. 2020 Apr 25. doi: 10.1002/hbm.25010.},\n abstract = {Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. 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