Preventing relapse in alcohol disorder with EEG-neurofeedback as a neuromodulation technique: A review and new insights regarding its application
Introduction
Alcohol Use Disorder (AUD) is a major public health concern, with excessive consumption resulting in 3.3 million deaths worldwide each year (6% of all deaths). It is responsible for 5.1% of the disability-adjusted life years (World Health Organization, 2018). Reinforcing prevention and care hence still appear to be highly relevant goals. Although a multitude of therapeutic approaches are available for alcohol-dependent patients, the relapse rate nonetheless remains astonishingly high (approximately 80% at one year post-withdrawal) (Schellekens, de Jong, Buitelaar, & Verkes, 2015). This highlights the limitations of the current treatments, which are mainly based on psychotherapy and pharmacology (Bari et al., 2018). From a neurocognitive point of view, it is an indisputable fact that addictive substances hijack brain systems that subserve neuronal patterns associated with compulsive behavior of substance use (Sokhadze, Cannon, & Trudeau, 2008). According to the Dual Process Network Theory (Stacy & Wiers, 2010), dependent patients are faced with a conflict between strengthened impulsive habits of consumption and weakened executive processes underlying abstinence (Wiers, Gladwin, Hofmann, Salemink, & Ridderinkhof, 2013). An abnormal bottom-up system generates a “craving” response (i.e., an intense desire to drink; Robinson & Berridge, 2008) and an automatic approach tendency, while an ineffective top-down process gives rise to reduced executive control with impaired inhibition of the dominant response (e.g., Wiers et al., 2013). In present-day society, there is a paradoxical link between the volume allocated to advertising of the substance itself and the message allocated to the prevention of health risks. This inconsistency emphasizes the limited impact of health prevention campaigns, and advertisements may render detoxified patients vulnerable to eventual relapse by fostering attentional biases and by straining inhibiting abilities (e.g., Frings et al., 2018). Therefore, preventing relapse or minimizing its extent is a challenging goal for long-term successful management of addictive behaviors.
The typical care for alcoholic patients consists of a dual treatment comprising medication and psychotherapy. Pharmacological entities can target the GABAergic system (using drugs such as baclofen, acamprosate, or topiramate) or they can act as opioid antagonists (e.g., naltrexone or nalmefene). However, these treatments have a limited impact on drinking control, providing significant but still only low to medium efficacy (for a meta-analysis see Palpacuer et al., 2018). Moreover, numerous psychotherapeutic treatments are provided together with pharmacological entities. Although some of these evidence-based therapies result in significant effects, none of them have proven to be a watershed in AUD recovery to date (Ray et al., 2019). There is, therefore, a need for the development of new add-on tools as care supports. In this view, researchers have considered neural networks as a treatment variable of interest to reduce specific symptoms and/or to optimize performance (Enriquez-Geppert, Huster, & Herrmann, 2013). Transcranial electrical stimulation (Philip, Sorensen, McCalley, & Hanlon, 2019), transcranial magnetic stimulation (Philip et al., 2019), and cognitive training procedure (Azevedo & Mammis, 2017) are currently used for this purpose, promoting a “multimodal” approach suggesting that the typical psychological/pharmacological approach needs to be complemented with other treatments such as neuromodulation (e.g., Luigjes, Segrave, de Joode, Figee, & Denys, 2019) and cognitive revalidation (Bates, Buckman, & Nguyen, 2013). The main objective of the present paper is to focus on the merits of neurofeedback (NF), which is a tool that has experienced renewed interest in both clinical and research areas (Marzbani, Marateb, & Mansourian, 2016). This article outlines the arguments in favor of the application of neurofeedback as an add-on tool in the treatment of alcoholic detoxified patients by enhancing the cognitive abilities needed to maintain long-term abstinence.
The widespread interfaces for the application of NF mostly involve functional Magnetic Resonance Imaging-neurofeedback (fMRI-NF) and electroencephalogram-neurofeedback (EEG-NF) (Dickerson, 2018). As EEG has high temporal resolution (on a millisecond scale) and since NF relies on real-time processes, EEG-NF provides a definite advantage for optimal learning (Zotev, Phillips, Yuan, Misaki, & Bodurka, 2014). For all mental states, there is a corresponding cerebral activity arising from a distributed brain network. This activity can be recorded using an EEG during a sensory or a cognitive task. EEG recordings can be analyzed based on parameters such as the amplitude (the magnitude of the oscillation in microvolts) and the rhythm (the oscillation frequency in Hertz) (Mumtaz, Vuong, Malik, & Rashid, 2018), and we usually analyze either Event-Related-Potentials (ERPs) or Event-Related-Oscillations (EROs). On the one hand, EROs reflect the integrative activity and the communication between populations of neurons associated with information processing of the task stimuli (Kamarajan & Porjesz, 2015). On the other hand, ERPs are voltage deflections arising from these superimposed EROs (Başar & Dumermuth, 1982). ERPs represent the average of the electrical activity emanating from synchronized parallel processing systems, thus generating a compound waveform resulting from different oscillatory responses (Karakas, 2000). As ERPs do not reflect the entire activity of the EEG signal, the focus has now returned to analysis of the genuine EEG signal, namely the oscillatory activity (for an overview see Bastiaansen, Mazaheri, & Jensen, 2011). Here, neurofeedback takes on its full meaning. Indeed, the use of EEG-NF involves the identification of target oscillations in order to reinforce or inhibit specific forms of EEG activity associated with cognitive impairments (Gunkelman & Johnstone, 2005). Thus, EEG-NF protocols rely on electrical activity recorded from the scalp, and they mainly focus on: alpha (8–12 Hz), beta (13–30 Hz), delta (0–4 Hz), theta (4–8 Hz), and gamma (30–50 Hz) frequencies, or combinations of these such as the alpha/theta ratio, the beta/theta ratio, etc. (for a review see Marzbani et al., 2016). By offering access to this wide range of targets, the EEG-NF procedure broadens the scope of the investigations through a multitude of experimental protocols that can be considered.
At the methodological level, NF is a form of biofeedback in which a measure of brain activity is provided as feedback in real-time to a participant (Hammond, 2011). More specifically, the brain oscillatory activity is simultaneously recorded and converted into a visual or an auditory signal that is fed back in real-time to the participant. Thus, the participant learns to visualize, interact with, and manage their own brain activity through an interface that digitalizes and converts the recorded signal into customized and understandable information (Gunkelman & Johnstone, 2005) (see Fig. 1).
Neurofeedback requires the participant to actively participate in their own care by finding personal mental strategies, by learning about their impact on brain activity, and by actively implementing them in a repetitive way. This procedure relies on two fundamental concepts: operant conditioning and brain plasticity (Enriquez-Geppert et al., 2013). The participant is asked to modify this feedback information via mental strategies such as imagining particular events taking place (e.g., thinking about the negative consequences of alcohol consumption), and the expected changes are positively reinforced (Cox et al., 2016). Participants thereby control their responses, see their progress in real-time, and achieve optimal performance in order to control their symptoms or an unwelcome behavior (Cox et al., 2016). In this manner, the neurofeedback is aimed at providing patients opportunities to modify altered brain activity and to recover optimal functioning (Micoulaud-Franchi, Quiles, & Vion-Dury, 2013).
Section snippets
Method: literature search strategy and selection of studies for inclusion
PubMed was used to identify available publications, without a specified date of coverage. The keywords “EEG”, “training”, and “alcohol” were used in order to identify relevant publications. Out of all of these publications (N = 76), we chose to focus our review on EEG-based neurofeedback studies and one main substance: alcohol (n = 4). For the sake of completeness and considering the same inclusion criteria, we also used the keywords “neurofeedback” and “alcohol” in a second selection. Out of
Results
The identification of relevant frequency patterns associated with specific AUD-related impairments has remained a major difficulty to date (Sokhadze et al., 2008). Yet, the aim of researchers is always to identify the most appropriate NF protocol in order to provide add-on tools to current treatment care (Arns et al., 2017) (see Table 1). For many years, the most commonly used protocol has been that of Peniston and Kulkosky (modulation of alpha-theta frequencies) (Peniston & Kulkosky, 1989),
Discussion and future perspectives
The underlying objective of these pioneering studies is to assess whether NF training can have a therapeutic effect on the relapse risk of addicted individuals via secondary improvements such as a reduction of anxiety (Sokhadze et al., 2008). There has, however, been a paucity of suitable placebo-controlled studies to date, as well as objective and rigorous evaluations of the scientific outcomes, thus explaining the current lack of robust evidence (Thibault, Lifshitz, & Raz, 2017). Indeed, in
Conclusion
To summarize, the separation of the relapse mechanism into two subcomponents (lack of inhibition and attentional bias) draws our attention to patient particularities: some benefit more from decreasing/suppressing attentional bias, others more from increasing inhibitory control, and others instead make the most of both. The implementation of EEG-NF protocols exclusively related to either of these subcomponents should then lead to a more targeted intervention, thereby allowing specific needs to
Limitations
As applied to neurofeedback, each interface presents its own advantages and drawbacks (Orndorff-Plunkett et al., 2017, Thibault et al., 2016). It should be mentioned that, despite its limited temporal resolution, fMRI offers optimal anatomical resolution (in the range of 1–2 mm) and it provides a large amount of information (Grodin & Ray, 2019). Functional results have indicated the effectiveness of fMRI-NF training by targeting relevant brain regions, and a number of studies have led to the
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
Salvatore Campanella is a Senior Research Associate (FRS-FNRS, Belgium).
Financial support
The authors were funded by the Belgian Fund for Scientific Research (F.N.R.S., Belgium) and the Brugmann Foundation (CHU Brugmann, Brussels, Belgium), although these funds did not exert any editorial direction or censorship on any part of this article.
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