The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice

Simon H Kohl, David M A Mehler, Michael Lührs, Robert T Thibault, Kerstin Konrad, Bettina Sorger, Simon H Kohl, David M A Mehler, Michael Lührs, Robert T Thibault, Kerstin Konrad, Bettina Sorger

Abstract

Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts. Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N = 441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1-2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.

Keywords: brain-computer interfacing; clinical translation; functional near-infrared spectroscopy; neurofeedback; real-time data analysis; self-regulation; systematic review.

Copyright © 2020 Kohl, Mehler, Lührs, Thibault, Konrad and Sorger.

Figures

Figure 1
Figure 1
Search decision flow diagram according to preferred reporting items for systematic reviews and meta-analyses (PRISMA; Moher et al., 2009).
Figure 2
Figure 2
Structure of results and discussion. In the first two sections, we provide a comprehensive overview of how fNIRS-neurofeedback training is implemented, describing and discussing important features of neurofeedback-training protocols (1) and of the real-time signal-processing methods applied (2). In the third section, we critically evaluate the quality of published studies including experimental design and reporting quality as well as statistical power/sensitivity as an indicator of reliability of the reported findings (3). In the fourth section, we assess and discuss the effectiveness of fNIRS-neurofeedback to regulate and induce pre-post changes in brain activity (4). Finally, we assess and discuss its effectiveness in changing behavioral outcomes and we review the clinical potential of fNIRS-neurofeedback (5). The fNIRS illustration was created by Laura Bell.
Figure 3
Figure 3
(A) Number of participants from different target populations and (B) Number of studies targeting a certain brain region. ADHD, attention-deficit/hyperactivity disorder; dlPFC, dorsolateral prefrontal cortex; IFG, inferior frontal gyrus; OFC, orbitofrontal cortex; PFC, prefrontal cortex; SMA, supplementary motor area.
Figure 4
Figure 4
Statistical power curves to detect different effect sizes with 20 participants (median sample size) for different statistical tests. Dashed lines indicate smallest effect sizes detectable at 80% power. Note that the power curve for the 2 × 2 mixed ANOVA was based on liberal statistical assumptions (e.g., high correlation among repeated measures, sphericity, and uncorrected p-value of 0.05).
Figure 5
Figure 5
Quality of studies according to the CRED-nf and JBI checklist.
Figure 6
Figure 6
Neurofeedback regulation success. Overall regulation was classified “Yes” if a significant effect for one or more of the four measures were reported and “No” if no significant effect was reported. If both were reported, the overall regulation was classified “Yes/No.” Note that Kober et al. (2015, 2018) trained the regulation of HbO and HbR in different groups and found differential results for the groups. Therefore, the two studies were counted twice for the four measures. In overall regulation, the two studies were only counted once and were classified as “Yes/No”.

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