Quality and denoising in real‐time functional magnetic resonance imaging neurofeedback: A methods review

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.

Neurofeedback training in major depressive disorder: a systematic review of clinical efficacy, study quality and reporting practices

“…Similarly, for fMRI-neurofeedback, control for confounding factors such as online correction of head motion, breathing, and cardiovascular artefacts are often insufficiently reported. This finding is in line with earlier findings for fMRI neurofeedback studies more broadly [48,55].…”

“…These values can be based on signal changes with respect to baseline in individual brain areas, correlations between time series of different brain areas (connectivity based feedback) [41,42], or the output of more complex algorithm that classify different brain states based on brain activity pattern variations [27]. However, all these processing methods vary according to the neurofeedback paradigm and are subject to ongoing research around methods development [43][44][45][46][47][48].…”

“…Although several studies report the exclusion of subjects due to excessive artifacts, only few studies intended to perform online quality control and denoising. This is not a particular problem in studies applying neurofeedback in MDD populations, but a current issue in the field [48]. Thus, we recommend that more rigorous approaches should be conducted during experiments and the reporting of results, for instance with regards to EOG and EMG noises in EEG-based protocols [39], or respiration and pulse waves in fMRI-based experiments [40].…”

“…In addition to the proper experimental design, an appropriate report of methods and results is crucial to advance the neurofeedback field and propagate reliable results. For example, an extensive methodological review showed that a substantial portion of neurofeedback studies do not apply or report adequate denoising methods in fMRI-based protocols [48] (complete data base available here: https://rtfmri-methods.herokuapp.com/). the CRED-nf checklist was created in a collaborative effort between several dozen laboratories to support this matter [81], including an easy-to-use app for quick validation (rtfin.org/CREDnf).…”

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

“…The problem of not reporting important information about signal-processing methods is also clearly visible in fMRI-neurofeedback studies and was addressed in a recent review by Heunis et al (2020) recommending more rigorous reporting and development of methodological reporting standards. These recommendations can also be applied to fNIRS-neurofeedback research and similar default measures such as signal-or contrast-to-noise ratio calculations for evaluating fNIRS-signal quality could be established to make results more comparable across studies and to improve reproducibility (Heunis et al, 2020). Notably, fMRI-neurofeedback can already rely on a further developed field, and efforts with regard to standardization have already been made (Nichols et al, 2016).…”