Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies

Sorger, Bettina; Scharnowski, Frank; Linden, David; Hampson, Michelle; Young, Kymberly

doi:10.1016/j.neuroimage.2018.11.004

Cited by 184 publications

(196 citation statements)

References 104 publications

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“…Depending on the specific research aim, neurofeedback studies can make use of several different control conditions. Control conditions may include, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control, and can be applied in a within-or between-subject design (see Sorger et al, 2019). Ideally, multiple control conditions are applied in order to disentangle neurofeedback-specific from unspecific processes.…”

Section: Control Conditionsmentioning

confidence: 99%

“…In this way, specific effects related to the control targets and neurofeedback-unspecific processes that likely differ for different neurofeedback targets may average out across all subjects of this control group. For extensive discussions on different control conditions in neurofeedback research, see Lubianiker et al, (2019) and Sorger et al, (2019).…”

Section: Control Conditionsmentioning

confidence: 99%

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

Mehler¹,

Lührs²,

Thibault³

et al. 2019

Preprint

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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.

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Section: Control Conditionsmentioning

confidence: 99%

Section: Control Conditionsmentioning

confidence: 99%

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

Mehler¹,

Lührs²,

Thibault³

et al. 2019

Preprint

Self Cite

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“…However its effective deployment in the clinical practice is being held back by the debated evidence about its efficiency, most likely as a result of poor study design and lack of established guidelines and knowledge about the underlying mechanisms of NF (Thibault et al, 2016;Perronnet et al, 2016). In recent years, increasingly rigorous approaches are becoming the new standard (Ros et al, 2019;Sulzer et al, 2013;Stoeckel et al, 2014;Thibault et al, 2016), and new studies are digging into the mechanisms Sitaram et al, 2016;Emmert et al, 2016;Birbaumer et al, 2013;Kober et al, 2013) as well as the methodological aspects of NF (Sorger et al, 2019;Emmert et al, 2017;Krause et al, 2017;Sorger et al, 2016;Sepulveda et al, 2016). However another reason for the debated efficiency of current approaches might be the inherent limitations of single imaging modalities (Biessmann et al, 2011;Fazli et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

Lécuyer

Mano

et al. 2018

Preprint

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Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain function by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly EEG or fMRI. EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new NF approach that consists of providing a NF based simultaneously on EEG and fMRI. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a spectrum of possibilities for defining bimodal NF targets that could be more ro-bust, flexible and effective than unimodal ones. However facing a greater amount of information, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose that the EEG and fMRI features should be represented in a single bimodal feedback, which we refer to as integrated feedback. We then introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The first group (BI DIM, n=10) was shown a two-dimensional (2D) feedback in which each dimension de-picted the information from one modality. The second group (UNI DIM, n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UNI DIM group than in the BI DIM group, which suggests that the 1D feedback is easier to control than the 2D feedback. However when looking at posthoc activation levels, the difference of fMRI activation levels between the NF runs and the preliminary motor imagery run without NF was more significant in the 2D group. Moreover, subjects from the BI DIM group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobule (BI DIM > UNI DIM, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more spe-cific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in combination in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms making the most of the bimodal information and better suited to clinical applications.

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“…The applications for functional Magnetic Response Imaging (fMRI) in relation to brain function are numerous, profound, and range widely including studies on dementia [28], drug dependency [16,5], brain-computer interface [31,13], ADHD clinical testing [14], psychiatric disorders [12], stress [33], neurological disorders [8], parkinson's disease [27], aging [24], and cancer [26].…”

Section: Introductionmentioning

confidence: 99%

Analytic Model of fMRI BOLD Signals for Separable Metrics of Neural and Metabolic Activity

Schaper¹

2019

Preprint

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The applications of fMRI (functional magnetic resonance imaging) are broad covering diagnostic and clinical extents of brain function, which involves the analysis of BOLD (blood oxygen level-dependent) contrast signals. The BOLD signals are sourced from both neural and metabolic functions, and thus to enable a detailed examination of fMRI studies, methods are sought to separate the neural and metabolic functions, such that the neural component, which is often the metric of interest, can be independently examined, especially in relation to neural connectivity. In this work, a modeling approach is developed that separates the neural and metabolic functions from the overall BOLD signal. The newly developed model is initially developed within a linear framework and demonstrates excellent comparison in data fit at 97.4% to the three Gamma function, which has been widely used to characterize fMRI BOLD experimental data. The neural component of the model formulation is comprised of a proper transfer function of two poles and two zeros, and characterizes the salient features of the BOLD signal, including the initial dip, peak, undershoot, and stabilization period. The linear model is extended to characterize nonlinear fMRI BOLD signal responses through the integration of saturation functions to both the leading and trailing edges of the signal. The nonlinear model representation not only explains the muted response in amplitude and oscillations, but also explains nuanced oscillations during the hold and settling phases of fMRI BOLD responses as exemplified in comparison to published data of sensorimotor responses. Further, the newly developed decomposition is derived within a framework for modeling neurovascular coupling environments, and thus lends credibility to the modeling framework. In developing the decomposition of the neural and metabolic transfer functions, it is a conclusion that the BOLD signal correlates very well with the fast dynamics associated with neural response to external stimuli. * charlesschaper@comcast.net Graphical AbstractThe normalized impulse response of the BOLD signal and the corresponding neural activity according to the newly developed model. There is a correspondence of the critical points for the oscillatory response of the neural function and metabolic reactivity, including the initial dip, peak and subsequent undershoot. Thus, the BOLD signal is a correlated representation of the underlying neural response.

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Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies

Cited by 184 publications

References 104 publications

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

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

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Analytic Model of fMRI BOLD Signals for Separable Metrics of Neural and Metabolic Activity

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