Near-infrared spectroscopy and electroencephalography neurofeedback for binge-eating disorder: an exploratory randomized trial.

BACKGROUND: Binge-eating disorder (BED) co-occurs with neurobehavioral alterations in the processing of disorder-relevant content such as visual food stimuli. Whether neurofeedback (NF) directly targeting them is suited for treatment remains unclear. This study sought to determine feasibility and estimate effects of individualized, functional near-infrared spectroscopy-based real-time NF (rtfNIRS-NF) and high-beta electroencephalography-based NF (EEG-NF), assuming superiority over waitlist (WL). METHODS: Single-center, assessor-blinded feasibility study with randomization to rtfNIRS-NF, EEG-NF, or WL and assessments at baseline (t0), postassessment (t1), and 6-month follow-up (t2). NF comprised 12 60-min food-specific rtfNIRS-NF or EEG-NF sessions over 8 weeks. Primary outcome was the binge-eating frequency at t1 assessed interview-based. Secondary outcomes included feasibility, eating disorder symptoms, mental and physical health, weight management-related behavior, executive functions, and brain activity at t1 and t2. RESULTS: In 72 patients (intent-to-treat), the results showed feasibility of NF regarding recruitment, attrition, adherence, compliance, acceptance, and assessment completion. Binge eating improved at t1 by -8.0 episodes, without superiority of NF v. WL (-0.8 episodes, 95% CI -2.4 to 4.0), but with improved estimates in NF at t2 relative to t1. NF was better than WL for food craving, anxiety symptoms, and body mass index, but overall effects were mostly small. Brain activity changes were near zero. CONCLUSIONS: The results show feasibility of food-specific rtfNIRS-NF and EEG-NF in BED, and no posttreatment differences v. WL, but possible continued improvement of binge eating. Confirmatory and mechanistic evidence is warranted in a double-blind randomized design with long-term follow-up, considering dose-response relationships and modes of delivery.

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.

Significance: Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. Aim: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2 s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. Approach: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination. Results: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. Conclusions: The study demonstrates that an encoding time as short as 2 s can enable immediate, efficient, and convenient fNIRS-BCI communication.

Mechanisms underlying fNIRS-neurofeedback over the prefrontal cortex for participants with binge-eating disorder.

OBJECTIVE: Despite the increasing popularity of neurofeedback (NF), aiming at voluntary modulation of dysfunctional prefrontal cortex (PFC) signals in the treatment of binge-eating disorder (BED) and/or overweight, mechanisms remain poorly understood. METHODS: Based on a randomized-controlled trial offering 12 food-specific real-time functional near-infrared spectroscopy (rtfNIRS)-NF sessions to participants with BED (n = 22), this preregistered study examined (1) online regulation success as predictor for offline regulation success, defined by PFC signals during regulation versus watch, and subjective regulation success, and (2) changes in loss of control (LOC) eating after vs. before and across 12 rtfNIRS-NF-sessions. RESULTS: Higher online regulation success expectedly predicted better subjective, but worse offline regulation success. LOC eating decreased after vs. before, but not over rtfNIRS-NF-sessions, and was not associated with subjective or offline regulation success. CONCLUSIONS: The association between online and subjective regulation success confirmed the presumed mechanism of operant conditioning underlying rtfNIRS-NF-learning. The contrary association between online and offline regulation indicated differential PFC involvement upon subtraction of automatic food-specific responses from regulation signals for offline success. Decreased LOC eating after food-specific rtfNIRS-NF-sessions suggested the potential of NF in BED treatment. SIGNIFICANCE: Results may guide the optimization of future NF studies in larger samples with BED.

No threat: Emotion regulation neurofeedback for police special forces recruits.

Police officers of the Special Forces are confronted with highly demanding situations in terms of stress, high tension and threats to their lives. Their tasks are specifically high-risk operations, such as arrests of armed suspects and anti-terror interventions. Improving the emotion regulation skills of police officers might be a vital investment, supporting them to stay calm and focused. A promising approach is training emotion regulation by using real-time (rt-) fMRI neurofeedback. Specifically, downregulating activity in key areas of the fronto-limbic emotion regulation network in the presence of threatening stimuli. Thirteen recruits of the Dutch police special forces underwent six weekly rt-fMRI sessions, receiving neurofeedback from individualized regions of their emotion regulation network. Their task was to reduce the image size of threatening images, wherein the image size represented their brain activity. A reduction in image size represented successful downregulation. Participants were free to use their preferred regulation strategy. A control group of fifteen recruits received no neurofeedback. Both groups completed behavioural tests (image rating on evoked valence and arousal, questionnaire) before and after the neurofeedback training. We hypothesized that the neurofeedback group would improve in downregulation and would score better than the control group on the behavioural tests after the neurofeedback training. Neurofeedback training resulted in a significant decrease in image size (t(12) = 2.82, p = .015) and a trend towards decreased activation in the target regions (t(10) = 1.82, p = .099) from the first to the last session. Notably, subjects achieved downregulation below the pre-stimulus baseline in the last two sessions. No relevant differences between groups were found in the behavioural tasks. Through the training of rt-fMRI neurofeedback, participants learned to downregulate the activity in individualized areas of the emotion regulation network, by using their own preferred strategies. The lack of behavioural between-group differences may be explained by floor effects. Tasks that are close to real-life situations may be needed to uncover behavioural correlates of this emotion regulation training.

Multimodal assessment of the spatial correspondence between fNIRS and fMRI hemodynamic responses in motor tasks.

Functional near-infrared spectroscopy (fNIRS) provides a cost-efficient and portable alternative to functional magnetic resonance imaging (fMRI) for assessing cortical activity changes based on hemodynamic signals. The spatial and temporal underpinnings of the fMRI blood-oxygen-level-dependent (BOLD) signal and corresponding fNIRS concentration of oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) measurements are still not completely clear. We aim to analyze the spatial correspondence between these hemodynamic signals, in motor-network regions. To this end, we acquired asynchronous fMRI and fNIRS recordings from 9 healthy participants while performing motor imagery and execution. Using this multimodal approach, we investigated the ability to identify motor-related activation clusters in fMRI data using subject-specific fNIRS-based cortical signals as predictors of interest. Group-level activation was found in fMRI data modeled from corresponding fNIRS measurements, with significant peak activation found overlapping the individually-defined primary and premotor motor cortices, for all chromophores. No statistically significant differences were observed in multimodal spatial correspondence between HbO, HbR, and HbT, for both tasks. This suggests the possibility of translating neuronal information from fMRI into an fNIRS motor-coverage setup with high spatial correspondence using both oxy and deoxyhemoglobin data, with the inherent benefits of translating fMRI paradigms to fNIRS in cognitive and clinical neuroscience.

Introduction to the shared near infrared spectroscopy format.

Significance: Functional near-infrared spectroscopy (fNIRS) is a popular neuroimaging technique with proliferating hardware platforms, analysis approaches, and software tools. There has not been a standardized file format for storing fNIRS data, which has hindered the sharing of data as well as the adoption and development of software tools. Aim: We endeavored to design a file format to facilitate the analysis and sharing of fNIRS data that is flexible enough to meet the community's needs and sufficiently defined to be implemented consistently across various hardware and software platforms. Approach: The shared NIRS format (SNIRF) specification was developed in consultation with the academic and commercial fNIRS community and the Society for functional Near Infrared Spectroscopy. Results: The SNIRF specification defines a format for fNIRS data acquired using continuous wave, frequency domain, time domain, and diffuse correlation spectroscopy devices. Conclusions: We present the SNIRF along with validation software and example datasets. Support for reading and writing SNIRF data has been implemented by major hardware and software platforms, and the format has found widespread use in the fNIRS community.

Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels.

Significance: Functional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ [ HbO ] and Δ [ HbR ] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.

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

[This corrects the article DOI: 10.3389/fnins.2020.00594.].

Self-modulation of motor cortex activity after stroke: a randomized controlled trial.

Real-time functional MRI neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders that are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial (ClinicalTrials.gov: NCT03775915) in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n = 12) or Sham (n = 12) neurofeedback. Assessments of brain structure, brain function and measures of upper-limb function were carried out before and 1 week after neurofeedback training. Additionally, measures of upper-limb function were repeated 1 month after neurofeedback training. Primary outcome measures were (i) changes in lateralization of motor cortex activity during movements of the stroke-affected hand throughout neurofeedback training days; and (ii) changes in motor performance of the affected limb on the Jebsen Taylor Test (JTT). Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within (P = 0.019), but not across, training days. There was no group effect on the primary behavioural outcome measure, which was average JTT performance across all subtasks (P = 0.116). Secondary analysis found improvements in the performance of the gross motor subtasks of the JTT in the Real neurofeedback group compared to Sham (P = 0.010). However, there were no improvements on the Action Research Arm Test or the Upper Extremity Fugl-Meyer score (both P > 0.5). Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1 week after neurofeedback training (P = 0.008), indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract were positively correlated with participants neurofeedback performance (P = 0.002). Therefore, here we demonstrate that chronic stroke survivors are able to use functional MRI neurofeedback to self-modulate motor cortex activity in comparison to a Sham control, and that training is associated with improvements in gross hand motor performance and with white matter structural changes.

Semantic fMRI neurofeedback: a multi-subject study at 3 tesla.

Objective.Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive procedure allowing the self-regulation of brain functions via enhanced self-control of fMRI based neural activation. In semantic rt-fMRI-NF, an estimated relation between multivariate fMRI activation patterns and abstract mental states is exploited for a multi-dimensional feedback stimulus via real-time representational similarity analysis (rt-RSA). Here, we assessed the performances of this framework in a multi-subject multi-session study on a 3 T MRI clinical scanner.Approach.Eighteen healthy volunteers underwent two semantic rt-fMRI-NF sessions on two different days. In each session, participants were first requested to engage in specific mental states while local fMRI patterns of brain activity were recorded during stimulated mental imagery of concrete objects (pattern generation). The obtained neural representations were to be replicated and modulated by the participants in subsequent runs of the same session under the guidance of a rt-RSA generated visual feedback (pattern modulation). Performance indicators were derived from the rt-RSA output to assess individual abilities in replicating (and maintaining over time) a target pattern. Simulations were carried out to assess the impact of the geometric distortions implied by the low-dimensional representation of patterns' dissimilarities in the visual feedback.Main results.Sixteen subjects successfully completed both semantic rt-fMRI-NF sessions. Considering some performance indicators, a significant improvement between the first and the second runs, and within run increasing modulation performances were observed, whereas no improvements were found between sessions. Simulations confirmed that in a small percentage of cases visual feedback could be affected by metric distortions due to dimensionality reduction implicit to the rt-RSA approach.Significance.Our results proved the feasibility of the semantic rt-fMRI-NF at 3 T, showing that subjects can successfully modulate and maintain a target mental state when guided by rt-RSA derived feedback. Further development is needed to encourage future clinical applications.

See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions.

Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.

Extremely fast pRF mapping for real-time applications.

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback.

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength.

Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis.

Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.

Neurofeedback Training versus Treatment-as-Usual for Alcohol Dependence: Results of an Early-Phase Randomized Controlled Trial and Neuroimaging Correlates.

INTRODUCTION: Alcohol dependence is one of the most common substance use disorders, and novel treatment options are urgently needed. Neurofeedback training (NFT) based on real-time functional magnetic resonance imaging (rtf-MRI) has emerged as an attractive candidate for add-on treatments in psychiatry, but its use in alcohol dependence has not been formally investigated in a clinical trial. We investigated the use of rtfMRI-based NFT to prevent relapse in alcohol dependence. METHODS: Fifty-two alcohol-dependent patients from the UK who had completed a detoxification program were randomly assigned to a treatment group (receiving rtfMRI NFT in addition to standard care) or the control group (receiving standard care only). At baseline, alcohol consumption was assessed as the primary outcome measure and a variety of psychological, behavioral, and neural parameters as secondary outcome measures to determine feasibility and secondary training effects. Participants in the treatment group underwent 6 NFT sessions over 4 months and were trained to downregulate their brain activation in the salience network in the presence of alcohol stimuli and to upregulate frontal activation in response to pictures related to positive goals. Four, 8, and 12 months after baseline assessment, both groups were followed up with a battery of clinical and psychometric tests. RESULTS: Primary outcome measures showed very low relapse rates for both groups. Analysis of neural secondary outcome measures indicated that the majority of patients modulated the salience system in the desired directions, by decreasing activity in response to alcohol stimuli and increasing activation in response to positive goals. The intervention had a good safety and acceptability profile. CONCLUSION: We demonstrated that rtfMRI-neurofeedback targeting hyperactivity of the salience network in response to alcohol cues is feasible in currently abstinent patients with alcohol dependence.

Evidence of fNIRS-Based Prefrontal Cortex Hypoactivity in Obesity and Binge-Eating Disorder.

Obesity (OB) and associated binge-eating disorder (BED) show increased impulsivity and emotional dysregulation. Albeit well-established in neuropsychiatric research, functional near-infrared spectroscopy (fNIRS) has rarely been used to study OB and BED. Here, we investigated fNIRS-based food-specific brain signalling, its association with impulsivity and emotional dysregulation, and the temporal variability in individuals with OB with and without BED compared to an age- and sex-stratified normal weight (NW) group. Prefrontal cortex (PFC) responses were recorded in individuals with OB (n = 15), OB + BED (n = 13), and NW (n = 12) in a passive viewing and a response inhibition task. Impulsivity and emotional dysregulation were self-reported; anthropometrics were objectively measured. The OB and NW groups were measured twice 7 days apart. Relative to the NW group, the OB and OB + BED groups showed PFC hyporesponsivity across tasks, whereas there were few significant differences between the OB and OB + BED groups. Greater levels of impulsivity were significantly associated with stronger PFC responses, while more emotional dysregulation was significantly associated with lower PFC responses. Temporal differences were found in the left orbitofrontal cortex responses, yet in opposite directions in the OB and NW groups. This study demonstrated diminished fNIRS-based PFC responses across OB phenotypes relative to a NW group. The association between impulsivity, emotional dysregulation, and PFC hypoactivity supports the assumption that BED constitutes a specific OB phenotype.

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

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.