Neurofeedback: time needed for a promising non-pharmacological therapeutic method

doi:10.1016/S2215-0366(16)30189-4

The Lancet Psychiatry

Volume 3, Issue 9, September 2016, Page e16

https://doi.org/10.1016/S2215-0366(16)30189-4 Get rights and content

References (5)

RT Thibault et al.
When can neurofeedback join the clinical armamentarium?
Lancet Psychiatry
(2016)
M Arns et al.
Evaluation of neurofeedback in ADHD: the long and winding road
Biol Psychol
(2014)

There are more references available in the full text version of this article.

Cited by (15)

The learning effects and curves during high beta down-training neurofeedback for patients with major depressive disorder
2020, Journal of Affective Disorders
Citation Excerpt :
Therefore, Thibault and Raz (2016a, 2016b) and Thibault et al. (2017) commented that under inadequate experimental design and limited effects were found, placebo factors should be accountable for the improvements after NFB training. Conversely, others proposed that the problematic methodology may also lead to underestimated effects and different outcome measures should be thoroughly examined (Micoulaud-Franchi and Fovet, 2016; Pigott, 2017; Pigott et al., 2017; 2018; Fovet et al., 2017; Witte et al., 2018). To diminish the controversy, multiple NFB researchers reached a consensus by dividing the mechanisms of NFB into five categories, including NFB-specific, NFB-nonspecific, general-nonspecific, repetition-related, and natural factors (Ros et al., 2019).
: Electroencephalography (EEG) has revealed increased beta activity in patients with comorbid major depressive disorder (MDD) and anxiety symptoms. Negative emotions and high beta activity could be decreased by a high beta down-training neurofeedback (NFB) protocol. The present study utilized three objective parameters — trainability, independence, and interpretability — to validate the effects of high beta down-training sessions.
: EEG data were collected from 23 patients with comorbid MDD and anxiety symptoms during high beta down-training sessions. Participants received five weeks of training, two sessions per week, to down-train high beta amplitude (20–32 Hz) at EEG sites P3 and P4. Three efficacy parameters were examined by comparing pre-training and post-training EEG.
: The trainability index revealed the learning curves of reduced high beta activity at P3 and P4, confirming training effects across and within sessions. The independence index revealed only beta band activity decreased. The interpretability index revealed the decreased high beta activity was positively correlated with decreased severity of depression, especially for cognitive depression.
: With only ten sessions in this study, it is unknown whether the NFB training caused extended and stable learning effects. Additionally, combining high beta down-training protocol with enhancing another target band could better ensure the desired changes in brain activity. Finally, the effect of medication on EEG cannot be excluded in present study.
: The trainability, independence and interpretability of the high beta down-training NFB protocol were confirmed, supporting the protocol's use in future research and clinical applications.
Neurofeedback: A challenge for integrative clinical neurophysiological studies
2020, Neurophysiologie Clinique
EEG neurofeedback research: A fertile ground for psychiatry?
2019, Encephale
Citation Excerpt :
However, the clinical efficacy of this approach remains a controversial and delicate issue even for well-investigated applications, such as the therapeutic use of EEG NFB in ADHD [6,7]. Indeed, the effectiveness of neurofeedback is largely debated [8–11]. In this paper, we propose that several factors related to the concept of biomarker may be responsible for the conflicting results in the EEG NFB literature:
The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human–computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human–computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.
Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects
2019, Neurophysiologie Clinique
Citation Excerpt :
Therefore, an important and timely field of research consists of determining better, i.e. more reliable and specific, EEG targets related to hyperkinetic activity [22], but also to the inattention dimension in ADHD. This will enable more relevant and efficient training to be provided to patients [59], which should ensure that the EEG target is modified during the neurofeedback procedure. In this critical review, we first described and discussed the relationship between neuroplasticity, motor skills and SMR BCI/neurofeedback training in line with the MI literature.
Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedback studies and to summarize the background from neurophysiological and neuroplasticity studies that led to SMR being considered as reliable and valid EEG targets to improve motor skills through BCI/neurofeedback procedures. The second objective of this review is to introduce the main findings regarding SMR BCI/neurofeedback in healthy subjects. Third, the main findings regarding BCI/neurofeedback efficiency in patients with hypokinetic activities (in particular, motor deficit following stroke) as well as in patients with hyperkinetic activities (in particular, Attention Deficit Hyperactivity Disorder, ADHD) will be introduced. Due to a range of limitations, a clear association between SMR BCI/neurofeedback training and enhanced motor skills has yet to be established. However, SMR BCI/neurofeedback appears promising, and highlights many important challenges for clinical neurophysiology with regards to therapeutic approaches using BCI/neurofeedback.
Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review
2018, Neuroscience
Citation Excerpt :
Interestingly, the framework we propose (see Fig. 1) suggests that task-unspecific factors could also have a significant impact on the modulation of target neurophysiological patterns in NF treatments. Thus, beyond the debated placebo effect of NF (Thibault and Raz, 2016; Micoulaud Franchi and Fovet, 2016), a human-factor-centered approach of NF leads us to consider the reciprocal influence of neural networks activated by the target neurophysiological patterns and those activated by the task-unspecific factors. As highlighted in this paper, more and more effort is devoted to the understanding of between-patient variability and to the control of between-study variability.
In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain–Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.’s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another.
By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials.
Neurofeedback: One of today's techniques in psychiatry?
2017, Encephale
Citation Excerpt :
Thanks to the principle on which it is based and to the fertile dynamic nature of ongoing research in a range of clinical, therapeutic and fundamental topics, neurofeedback can be considered a technology of today [6,7]. However, despite great interest in neurofeedback research [8–10], significant controversy exists, particularly in psychiatry and neurology [7,11]. With regard to the efficacy of neurofeedback in brain disorders, opinions within the scientific community appear to be rather sharply divided [7,9,12] comprising an optimistic group who consider neurofeedback to be effective and a skeptical group who do neither assign scientific or therapeutic value to neurofeedback training.
Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry.
Literature on neurofeedback and mental disorders but also on brain computer interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the Neurofeedback evaluation & training (NExT) section of the French Association of biological psychiatry and neuropsychopharmacology (AFPBN).
Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics) and techniques (EEG, fMRI, signal processing).
The field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain computer interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice.
Le neurofeedback consiste à mesurer, chez un sujet, une activité cérébrale et à traiter le signal au moyen d’une interface technique afin d’en extraire un paramètre d’intérêt qui sera présenté en temps réel au participant sous la forme d’une information visuelle ou auditive. L’objectif est d’apprendre au sujet à modifier ce paramètre et donc à moduler son activité cérébrale et cognitive. Cependant, l’utilisation du neurofeedback en pratique clinique pour la prise en charge des troubles psychiatriques reste controversée.
Cet article présente une synthèse de la 1^re journée nationale sur le neurofeedback organisé par la section Neurofeedback Evaluation & Training (NExT) de l’Association française de psychiatrie biologique et de neuropharmacologie (AFPBN). Un état des lieux de l’utilisation du neurofeedback en électroencéphalographie (EEG) et en imagerie par résonance magnétique fonctionnelle (IRMf) est proposé. Pour intégrer l’arsenal thérapeutique, cette technique doit en effet répondre aux exigences de l’evidence based medicine.
Les études montrent une efficacité probable du neurofeedback en EEG pour le trouble du déficit de l’attention/hyperactivité (TDAH) chez les enfants. Pour les autres troubles psychiatriques, le nombre d’études est encore trop limité pour se positionner. En ce qui concerne le neurofeedback en IRMf, le niveau de preuve reste, pour l’heure, trop faible pour justifier une utilisation clinique. Les modalités d’emploi du neurofeedback, notamment en ce qui concerne les indications médicales, les protocoles d’utilisation (activité(s) cérébrale(s) ciblée(s), caractéristiques d’apprentissage) et les outils de mesure employés (EEG, IRMf, mode de traitement du signal) restent donc à clarifier.
Le vaste champ de recherche du neurofeedback implique à la fois des psychiatres, des neurophysiologistes et des chercheurs du domaine des interfaces cerveaux-ordinateurs. Les futurs travaux devront s’attacher à déterminer les critères permettant d’optimiser les séances de neurofeedback afin de mieux comprendre ses effets, le tout dans l’optique d’une utilisation en pratique clinique dans certaines indications. L’étude des processus d’apprentissage constitue un élément clé autour duquel les futures recherches devront se focaliser.

View all citing articles on Scopus

View full text

CorrespondenceNeurofeedback: time needed for a promising non-pharmacological therapeutic method

Lancet Psychiatry

Biol Psychol

Correspondence
Neurofeedback: time needed for a promising non-pharmacological therapeutic method