Skip to main content

A debate on working memory and cognitive control: can we learn about the treatment of substance use disorders from the neural correlates of anorexia nervosa?

Abstract

Background

Anorexia Nervosa (AN) is a debilitating, sometimes fatal eating disorder (ED) whereby restraint of appetite and emotion is concomitant with an inflexible, attention-to-detail perfectionist cognitive style and obsessive-compulsive behaviour. Intriguingly, people with AN are less likely to engage in substance use, whereas those who suffer from an ED with a bingeing component are more vulnerable to substance use disorder (SUD).

Discussion

This insight into a beneficial consequence of appetite control in those with AN, which is shrouded by the many other unhealthy, excessive and deficit symptoms, may provide some clues as to how the brain could be trained to exert better, sustained control over appetitive and impulsive processes. Structural and functional brain imaging studies implicate the executive control network (ECN) and the salience network (SN) in the neuropathology of AN and SUD. Additionally, excessive employment of working memory (WM), alongside more prominent cognitive deficits may be utilised to cope with the experience of negative emotions and may account for aberrant brain function.

Summary

WM enables mental rehearsal of cognitive strategies while regulating, restricting or avoiding neural responses associated with the SN. Therefore, high versus low WM capacity may be one of the factors that unites common cognitive and behavioural symptoms in those suffering from AN and SUD respectively. Furthermore, emerging evidence suggests that by evoking neural plasticity in the ECN and SN with WM training, improvements in neurocognitive function and cognitive control can be achieved. Thus, considering the neurocognitive processes of excessive appetite control and how it links to WM in AN may aid the application of adjunctive treatment for SUD.

Peer Review reports

Background

This debate article is written to pose the question: can a neurobiological hypothesis for excessive appetite control, derived from neurobiological data of anorexia nervosa (AN) help to direct adjunctive treatment for the control of substance use disorder (SUD)? Specifically, the role of working memory (WM) in relation to other cognitive strategies in the employment of appetite control will be considered. In order to comment on and attempt to answer this question, the following will be discussed: a) some recent research into the neuropsychological and neurobiological profile of AN; b) the neuropsychological and neurobiological profile of addiction with a focus on SUD; c) evidence regarding the neurobiology of WM; d) how WM training has been implemented to evoke neuroplasticity, clinical improvements and transferability to self-regulation skills in schizophrenia, attention deficit hyperactivity disorder (ADHD) and SUD; e) the limitations and criticisms of WM training; f) how WM training might be used as an adjunct to treatment for those with SUD, and potentially also for those with eating disorders.

Given that this article examines where WM fits in to the complex aetiology of AN and SUD, a brief description of WM and two contemporary theories, namely Global Workspace Theory (GWT) and Bayesian Probabilistic Inference (BPI) will follow, that provide suggestions as to how WM might be utilised to achieve appetite control. This will be done before briefly summarising the neuropsychological and neurobiological profiles of AN and SUD in relation to WM.

The theoretical framework for WM was first described over 40 years ago in a book chapter by Baddeley and Hitch [1] a term synonymous, to some extent, with the limited capacity short-term memory store (see Fig. 1). However, whereas short-term memory is described as a storage process, WM rather refers to the structure and function of neural mechanisms that enable temporary storage, organisation and manipulation of memory while dynamically attending to and processing other information. Specifically, WM can be defined as “…the holding mechanism in the mind for a small amount of information that is kept in a temporarily heightened state of availability. As such, it should contain what we think of as the conscious mind, but also captures the broader role of on-going processing and temporary memory functions outside of conscious awareness.” [2]. Thus, if we take the viewpoint that WM functions to toggle between cognitive-emotion interactions in the brain, providing as a by-product a sense of conscious self-regulation, we may form a hinge model of the mind from which excessive, cognitive control and excessive engagement in appetitive processes swings. However, this viewpoint does not presuppose that WM is synonymous with consciousness or cognitive control (which introduces the infinite regress problem of homunculus management), but rather that the subjective experience of varying degrees of cognitive control, or self-regulation, automatically arises from the neural capacity to flexibly toggle between not mutually exclusive cognitive and affective states.

Fig. 1
figure1

The original working memory model by Baddeley [1]. Reproduced via open access Wikimedia: https://commons.wikimedia.org/wiki/File:Working-memory-en.svg

A contemporary neurobiological theory of WM is formulated by the Global Workspace Theory (GWT, [3]), see Fig. 2. The theory posits that focal ambiguity in the brain (e.g. how attention is drawn to one of many immediate or delayed salient, rewarding possibilities) is solved by conscious precepts emerging out of unconscious, ‘backstage’ events that form a coalition within cortico-limbic tracts. The GWT helps to address the issue that the brain does not possess a controlling homunculus, but that various neural systems compete for limited conscious access at any given moment, and that the transient presence of WM is experienced consciously (and perhaps volitionally), fuelled by dynamic unconscious systems. The notion that decision-making is derived from backstage non-conscious processes as opposed to consciously and with free-will, has been shown previously by various researchers [4, 5], particularly in relation to overt action selection [6, 7]. Moreover, the underlying neural mechanisms of unconscious tendencies to act are particularly pertinent to the question posed by this article, in terms of the cognitive control over consuming food or an illicit substance. For example, if a decision to act has been determined by prior unconscious neural processes it begs two questions. How do people suffering with AN override the primary appetitive drive to consume food with sometimes-fatal rigidity? And how do individuals with SUD learn to follow complex cognitive strategies to engage in drug taking despite awareness of harmful consequences? Perhaps the answer to these questions lies in how traits and cognitive biases of people with AN and SUD contribute to styles of decision-making under conditions of uncertainty – for example, delaying a decision to act (or eat) [8] versus ‘jumping-to-conclusions’ [9] respectively.

Fig. 2
figure2

Bernie Baars’ Global Workspace Model incorporating working memory. Reproduced with permission via email communication from Professor Bernard Baars

The presence of cognitive biases relevant to the disease state (e.g. food for eating disorders, drug paraphernalia for substance dependence) suggests that conscious deliberation is perhaps not central to WM and decision-making in those with AN and SUD, but may be a by-product of the SN impinging on higher order cognitive processes. In this vein, the compulsive and complex aetiology that underlies appetite restriction in those with AN and continued substance use in those with SUD may be driven by prior sensory experience of, for example, negative emotional states. Such negative emotional states (e.g. anxiety, emotional abuse) may unconsciously bias WM processes or become resistant to updating in the presence of uncertainty (e.g. new and/or inconsistent stimulation) in the environment.

The influence of uncertainty on the dynamic updating of WM based on prior experience is described in the theory of Bayesian Probablisitic Inference (BPI [10]) and variations in WM capacity are implicated to modulate BPI [11]. See Fig. 3. BPI provides a framework for modelling how a person integrates information from multiple cues (e.g. interoceptive, exteroceptive) and from prior knowledge about self, world and others to update perceptual inferences about probabilities in the environment [12]. For example, it could be that an increased WM capacity (e.g. inherited or learned) enables a person to hold in mind a greater number of predictions in the presence of perceived uncertainties for a longer period, which increases the probability that complex future outcomes can be predicted and prior knowledge more accurately updated. Furthermore, deliberating on a greater number of predictions and observations about uncertainty in the environment may increase the subjective states of cognitive control, self-regulation, etc. [12], and an increased WM capacity may enhance the acute subjectivity of these states.

Fig. 3
figure3

Greg Gandenberger’s Bayesian Probabilistic Inference Model to explain how Bayesianism provides guidance for belief or action, particularly under conditions of uncertainty. Reproduced with permission via email from Gregory Gandenberger(http://gandenberger.org/category/philosophy-of-science/bayesianism/). 1) Likelihoodism is based on a belief system that an outcome will occur. 2) Frequentism is based on prior experience and the probability that an event will occur; 3) Bayesianism is based on updating belief system via frequency of exposure, and informs action tendencies in the presence of uncertainty

a) WM and the neurobiological profile of anorexia nervosa (AN)

Against this background, one could hypothesise that those with AN have increased WM capacity, due to, for example, genetic susceptibility or learning. WM capacity is likely reinforced in terms of BPI [13] by deliberating on strategies to restrict food intake amid a high availability of food (despite a level of uncertainty as to whether food restriction will be possible). It is worth noting that people with AN are shown to be intolerant of uncertainty in comparison to healthy controls and appear to gather more cues from the environment than non-eating disordered people before making a decision [14] which may also contribute to deficits in social and emotional processing [15]. Similarly, people suffering with AN learn to eat the minimum amount to stay alive and while doing so likely reinforce rigidly held unconscious prior beliefs dictating that appetite control and a thin body is synonymous with negative emotion regulation (e.g. ‘nothing tastes as good as skinny feels’ [16]) and reward [17]. In contrast to AN, those who binge eat appear to be at the opposing extreme of an impulse-control spectrum [18], characterised by a lack of self-control, a heightened reward response to food and reduced WM capacity [19, 20]. Thus, rigidly adhering to priors in order to make decisions during conditions of perceived uncertainty (e.g. whether the motivation to eat can be restricted, and whether unpredictable emotions in self and others can be effectively controlled) may contribute to excessive WM capacity and related clinical observations such as perfectionism, lack of central coherence (global thinking), inability to set-shift (adapting to flexible strategies) and social-emotional processing deficits [21].

It is hypothesised in this article that excessive, and thus dysfunctional WM is employed in those with AN. However, it is important to emphasise that WM deficits are a small part of a larger, more complex set of symptoms in those with AN. For example, the cognitive-interpersonal maintenance model of AN [21] posits that a strong attention to detail and an inability to flexibly toggle between rules (weak set shifting) are inherited vulnerabilities but that other social and familial factors exacerbate these vulnerabilities. Furthermore, people with AN have social emotion deficits including sensitivity to criticism, and deficits in emotion regulation. Thus, while WM might be integral to excessive appetite control, there are certainly other factors to consider in the complex aetiology of AN.

Other issues hampering the elucidation of the impact of WM on the symptomatology of AN include a) the assessment of WM, b) how WM is associated with ED symptoms, c) the involvement of specific brain regions and d) WM improvements after therapy (see Table 1 and below for citations). The assessment of WM in AN has to date included the N-back task (remembering letters 1,2 or 3 positions prior to a target for example); the Working Memory Index of the Wechsler Adult Intelligence Scale (WAIS); the Wechsler Memory Scale (WMS) – specifically the Digit Span backwards task; a counting span task; a task that involves remembering the position of an arrow previously presented and a spatial WM task that involves remembering the position of a lit window in a series of houses or the position of hidden blue tokens. Additionally, some studies found that distractions (e.g. negatively rated body images, subliminal and supraliminal images of food) worsen WM task performance in those with AN. In terms of ED symptoms, restricting AN has been associated with superior WM performance, coinciding with low weight status and depression. Higher maternal IQ and education level may be linked to better WM performance in children who are born to mothers with ED symptoms. Anxiety may worsen WM capacity, and a longer duration of ED illness might be linked with excessive WM capacity. However, 4 out of 11 studies also found no correlation between ED symptoms and WM ability. Only 3 brain imaging studies to date have examined neural responses during WM tasks in those with AN, revealing increased bilateral dorsolateral prefrontal cortex (DLPFC), premotor cortex, left middle temporal gyrus and right precuneus activation. Additionally, those who have recovered from AN may have greater amygdala and fusiform activation, and greater suppression of the medial prefrontal cortex during a WM task when viewing images of bodies that have been rated negatively. In sum, most studies show superior [20, 2226], some deficit [27, 28] and others show no difference [2931] in WM performance in AN compared to binge eaters or otherwise healthy people.

Table 1 Chronological list of studies examining working memory in people with AN

The heterogeneity of findings across studies of WM in AN could also be due, in part, to the transdiagnostic nature of symptoms both between (e.g. traits) and within (e.g. states) individuals with ED [18]. Nevertheless, based on current research, there are three main points as to why the neurobiology of WM capacity is an emerging platform upon which to examine the mechanism of appetite and impulse control. First, WM is regarded as a cognitive mechanism that is shown to exert control over distracting arousing stimulation [32]. In line with this, WM and not other cognitions, such as response-inhibition, have been shown to interact with non-consciously processed appetitive images of food in those with AN [23]. Second, WM function is associated with fronto-parietal cortex activation [3335], brain regions that have been shown be most susceptible to neurobiological changes in those with AN [32, 3640]. And third, neuropsychological impairments in those with AN (e.g. executive dysfunction, deficits in somatic and emotion processing, rigid thinking, perseveration) appear to be characteristic of increased function in the DLPFC, anterior cingulate cortex (ACC), striatum and parietal cortex, regions of the executive control network (ECN) and salience network (SN), which provide clues as to the neural mechanisms of appetite control [37, 39, 40] and some further pointers towards the involvement of WM.

Neurobiological similarities and differences between AN, bulimia nervosa (BN) and addiction are also found [46, 70]. For example, a behavioural economic approach to how rewarding substances (e.g. food, drugs) may annex normal learning systems in the brain is at odds with the traditional model of distinct disease processes and treatments [71]. However, in the light of this article it is pertinent to consider similarities and differences in neural systems across disorders such as AN and SUD, given that variations in cognitive control of appetite can help to uncover the neural relationship between conscious tertiary control cognitions and primary process appetitive systems [19] that may be associated with WM function. For example, Kaye and colleagues suggest that extra-ordinary activation of the ECN (which is associated with WM), while diminishing optimal cognitive functioning, aids the suppression of appetite and consummatory behaviour associated with the SN and acts as a protective factor to prevent those with AN from developing SUD [46]. From another perspective, negative emotions that are difficult to consciously attend to might be dealt with by excessive activation of the ECN in those with AN.

In line with this view, Kaye and colleagues suggest that those with BN and SUD learn to self-medicate in response to the experience of negative emotion by consuming, in an uncontrolled and impulsive/compulsive manner, large quantities of rewarding substances and experiences (e.g. food, drugs, alcohol, sex). Bingeing on or excessive regulation of rewarding sensations has the effect of temporarily reducing negative emotion in the absence of optimal self-regulation in conjunction with dysregulation of the serotonergic system [72, 73] particularly involving the frontotemporal cortex [74]. Conversely to AN and healthy people, those who binge eat, like those with SUD have reduced activation of prefrontal cortex networks, combined with an increased activation in the limbic system [19, 64, 75]. Thus, it could be, in line with an impulse-control spectrum neural model [18], and a behavioural economic model [52] that those with AN learn to excessively recruit the ECN via WM function, which diminishes optimal cognitive performance and the reward value of food, whereas those with BN and SUD have weakened WM and learn to consume rewarding substances as a means to saturate the conscious experience of negative emotion (See Fig. 4).

Fig. 4
figure4

Neurobiological impulse-control model of temperamental dominance in ED [18]OCPD = Obsessive-Compulsive Personality Disorder; DLPFC = dorsolateral prefrontal cortex; OFC = orbitofrontal cortex; MPFC = medial prefrontal cortex; ACC = anterior cingulate cortex; COMT = catechol-o-methyltransferase; BDNF = Brain Derived Neurotrophic Factor; 5HT2A = 5-Hydroxy-Tryptophan-2A

Another model of AN has recently emerged that is akin to regarding habitual appetite restraint as a reward reinforcement, or an addiction, whereby excessive recruitment of ECN is considered not to dampen reward processes, but rather to hijack the mesolimbic reward system and increase the saliency of disease-related cues [17]. This model fits well with recent empirical evidence of increased activation of top-down control and bottom-up reward processes, and heightened cognitive bias in response to salient food stimuli in those with AN [67, 68]. The view that excessive appetite restraint is an addiction in itself also fits well with the model being proposed in this article. Specifically, it is suggested at the foundation of the model that an optimal level of cognitive control of appetite enables effective cognitive-emotion processing and self-regulation. However, extreme or limited activation of the ECN can present as addictive symptomatology, that is to say, lack of cognitive control over compulsions (see Fig. 5). This model is in line with the Yerkes-Dodson Law of arousal and performance, in that both extreme low and high levels of arousal – similarly, high and low levels of appetite control - can lead to sub-optimal cognitive control and performance [76].

Fig. 5
figure5

A model of cognitive control of appetite. a The original Yerkes-Dodson Law [76], showing that optimal performance occurs when medium arousal is present, but that both low and high arousal can be detrimental to performance. Reproduced via open access Wikimedia: https://commons.wikimedia.org/wiki/File:Yerkes-Dodson_wet.png; b) Updated model to depict cognitive control of appetite, applicable to restricting anorexia nervosa and addictive behaviours such as SUD and binge eating. Low appetitive processes (e.g. due to satiation with substance/food or hijacked by negative emotion such as anxiety or anger) coincide with low cognitive control. High appetitive processes (e.g. reward sensitivity, real or perceived) coincide with low cognitive control because either reward responses impinge on executive functioning or executive functioning is overloaded. Optimal cognitive control (e.g. self-regulation and effective cognitive-emotion neural interactions) is suggested to occur when appetitive drive is within the medium range

b) WM and the neurobiological profile of addiction

Addiction encompasses a variety of behavioural disorders such as SUD (illicit substances, alcohol, nicotine), pathological gambling, Internet use disorder and sexual dysfunction and reflects altered reward processing [77], pain and negative emotion susceptibility [78, 79]; hijacked decision-making and lack of self-control [71]. Parallels have been drawn between addiction and eating disorders with a binge-eating component in terms of deficits in self-regulatory control over consummatory behaviours [19], and the influence of dopamine signalling during impulsive decision-making [80]. Addictive behaviours appear to stem from an inability to cope with intrusive negative cognitions and emotions (e.g. painful past experiences, lack of immediate reward/tension release, fear of loss), which are either consciously and deliberately suppressed via engagement in rewarding experiences, or unconsciously repressed via the avoidance of a negative experience [81]. In a recent meta-analysis, chronic substance abuse was associated with deficits in verbal WM [82] and sustained abstinence from drug taking is associated with improvements in WM that are concomitant with prefrontal cortex alteration [83]. However, as with eating disorders, it is not clear whether WM deficits are due to developmental vulnerability or to current illness effects.

Deficits in WM in those with SUD before treatment are linked to sub-optimal self-regulation and higher rates of risky decision-making [8486] and are in line with a dual system model incorporating activation of deliberate top-down cognitive control versus automatic bottom-up reward and arousal [87]. Specifically, behavioural, cognitive and affective deficits include apathy, disinhibition and executive dysfunction, lack of response inhibition, mental inflexibility, poor WM and suboptimal processing of affective stimuli [88]. Furthermore, attention bias to addiction-salient stimuli appears to be associated with automatic cognitive processes and deficits in WM [89]. Abstinence from an addictive substance is also associated with poor WM performance that can be improved with cognitive training intervention [90, 91]. Thus, addiction research suggests that optimal response selection, WM and attention, which perhaps collectively enable cognitive control and emotion regulation, are most susceptible during illness, due to a failure of response inhibition that is observed as impulsive and compulsive acts in those with SUD [92].

c) Evidence regarding the neurobiology of WM and links to appetite/impulse control in AN and SUD

WM is a candidate mechanism for exploring the neural bases of cognitive control of appetite and impulsivity given that WM deficits are concomitant with aberrant fronto-parietal network structure and function in both AN and SUD. For an illustration of susceptible brain regions see Fig. 6 and Additional file 1. Additionally, the GWT and BPI help to provide some potential insights into how WM processes contribute to excessive versus deficit cognitive control in those with AN and SUD respectively. Addictive behaviour in itself could be a reflection of how uncertainties are perceived to be incongruent with existing prior beliefs or expectations, to the extent where prediction errors, and therefore a heightened subjectivity of a lack of cognitive control, are increased when a rewarding goal is consistently sought. In line with this suggestion, at the beginning of their illness, people with SUD tend to engage in goal-directed drug seeking behaviour that reinforces reward expectations about consuming the drug [103], which has parallels with excessive recruitment of ECN and WM in those with AN [46]. However, as SUD progresses, goal-directed behaviour transfers from delayed to habitual behaviour, driven by immediate rewards, particularly for those who have a neurobiological vulnerability for impulsive behaviour [104]. This shift from goal-directed to habitual behaviour in those with chronic SUD could reflect a reasoning style that is shaped by a reduction in prediction error and a consistent congruence with priors based on a high degree of reinforcement in the presence of environmental uncertainty (e.g. whether the drug can be sought and ingested). It must also be remembered that ingestion of the drug damages corticostriatal pathways (See Fig. 6) to the extent that suboptimal WM capacity likely ensues in those with SUD. This could indicate, conversely to those with AN where existing priors are rigidly fixed, that as SUD progresses the uncertainties in the environment flood, rather than reinforce and update prior expectations.

Fig. 6
figure6

Brain regions most implicated in addiction. a Prefrontal cortex volume is shown to be reduced in those with SUD, produced via unpublished data with permission from Dr Samantha Brooks; b) A cartoon of the basal ganglia, associated with arousal, motivation, primary process affective states. Together, the prefrontal cortex and areas of the basal ganglia form the cortico-striatal pathway, which is implicated in the neuropathology of SUD and AN

In terms of the neurobiology of WM, GWT and BPI, regions of the lateral prefrontal cortex, ACC, parietal cortex, striatum and hippocampus are linked to deliberative processing, reduced delay discounting and the experience of self-control for the suppression of incongruent impulses [32, 59, 87]. Non-consciously processed salient stimuli activate the amygdala, bilateral ACC, bilateral insular cortex, hippocampus and primary visual cortex [105], and are limbic regions associated with affective states that influence decision-making processes. These regions are implicated in the pathophysiology of AN and SUD as described above, and could be an indication that both disorders share common neural mediators for the dysregulation of negative emotion, especially when derived from childhood or adolescence [106]. The prefrontal cortex is vital to aid planning ahead with multiple steps held in WM, as described by temporal attention framing, which can allow rewards not to be selected, or to be deferred [13, 87, 107109]. Additionally, BPI highlights the role of reinforcement learning in the brain and implicates the corticostriatal pathways in reward prediction [110], involving differential activation in this pathway for immediate versus delayed rewards [107]. Furthermore, when prediction error is present and priors are updated based on new information, there appears to be greater ACC and striatum activation [106], and increased ACC activation is sometimes observed in both AN [70] and SUD [111].

Discussion

Against the background of the neurobiology of AN, SUD and neural mechanisms of WM, a brief discussion will follow as to how WM training may be implemented to promote neuroplasticity and the experience of cognitive control in other psychiatric conditions. Furthermore, some of the existing constraints to WM training will be highlighted, ending by discussing some future directions as to how WM training might be used as an adjunct to standard evidence-based treatment for both SUD and AN, particularly those who have been resistant to treatment.

d) How WM training has been successfully implemented to evoke neuroplasticity, clinical improvements and transferability to other skills in schizophrenia, attention deficit hyperactivity disorder (ADHD) and SUD

Emerging evidence, particularly in those with schizophrenia, attention deficit hyperactivity disorder (ADHD) and SUD, shows that targeted intensive cognitive training can induce normalcy and greater efficiency within neural system operations, while potentially fostering inherent plasticity processes [112]. In those with schizophrenia, repetitive cognitive training has successfully targeted cognitive deficits in perception, WM, attention, and social cognition, or combinations of these functions [113]. Further, among other clinical factors, ‘brain reserve’ measures may predict better outcome following cognitive training [113]. Brain imaging studies of those with schizophrenia undergoing cognitive training reveal structural and functional changes in key prefrontal brain regions associated with WM, executive functioning and cognitive inhibition [112, 114, 115]. It is pertinent to consider WM improvements in schizophrenia in the light of WM training for SUD, given that protracted SUD can provoke the onset of psychosis, suggesting that similar neural systems are at state [116].

Recent studies of those with ADHD, which can also present as comorbidity with SUD [117], have demonstrated clinically significant effects of WM training, particularly in lowering inattention and impulsivity [118120]. Additionally, learning difficulties in those with ADHD are improved with WM training and sustained for up to 8 months [121]. The current leader in WM training for ADHD is CogMed™, a computer-based WM package that claims to increase WM capacity over 5 weeks of progressively difficult levels, 5 times per week at home, work or school, using a variety of WM tasks tailored to school children or adults (http://www.cogmed.com/cogmed-training-method). The CogMed™ package includes games that utilise already existing WM tasks such as digit span, spatial awareness, verbal fluency, rule-changes and attention to detail. CogMed™ has been shown to improve WM function and non-trained response inhibition and reasoning skills in other cognitive domains [122], and is linked to parent-reported lower inattention and impulsivity rates in children [123], in children born with low birth weight [124] and in children with low language ability [125]. Overall, CogMed™ has generated research over the last decade that appears to foster benefits in visuo-spatial and verbal WM that transfer to better attention at 6 months [122, 126]. Further, improvements on impulsivity and attention measures following WM training are related to fronto-parietal and striatal functional alterations in children with ADHD [123, 124]. However, there is also some criticism of CogMed™ in that WM training may only provide near transfer effects, and it is not known whether these effects are durable to other cognitive skills that do not mimic the tasks included in the CogMed™ package [127130].

In those with SUD, computerized WM training has been shown to reduce impulsivity and delay discounting (lowering the preference for immediate over delayed rewards) among stimulant users [131]. However, unlike other fields where WM training is being effectively utilised, WM training research for SUD is in its infancy, and it is currently unclear whether training in those with SUD should be reserved for those with demonstrable cognitive deficits [132], and which cognitive targets (WM or others) result in the largest clinical effect size. Nevertheless, a contemporary behavioural economic theory underlying the use of WM training for those with SUD is that rewarding substances annex learning mechanisms in the brain, resulting in a dysregulation of value systems and distortion in decision-making that can be strengthened with training [71]. More specifically, it is suggested that WM training can help to strengthen a dual-system of deliberative over automatic neural responses to reward [87]. In line with a dual-system neural model of SUD, the mesolimbic reward system, including the nucleus accumbens and ventromedial prefrontal cortex support automatic responses to rewarding stimuli, whereas the lateral prefrontal and parietal cortices (regions associated with WM) are more closely linked to deliberation and the exertion of self-control in the suppression of impulses [59, 87].

e) Constraints/limitations to the WM model of cognitive training for SUD

The most common hurdle for WM training is to demonstrate that improvements to cognitive performance are long-term, transferable to other non-trained cognitive skills and better than other clinical interventions. Some researchers using CogMed™ have not demonstrated long-term, transferable effects that are significantly better than other clinical methods [133], whereas others have [122, 126]. Similarly, WM training in those with SUD appears to reduce impulsivity, but it is not clear how long this effect lasts [120]. Another issue is whether it is more beneficial to train using multiple executive function domains and WM tasks or to focus on neural functioning using less, and simpler tasks to evoke plasticity within a less widespread, measurable domain [134]. Furthermore, it must be noted that using adjunctive WM training might be best implemented in those who have been resistant to standard evidence-based treatment.

Using a WM model to explain cognitive control of appetite and impulsivity is also fraught with pitfalls. WM is only one domain of executive functioning, and the ability to regulate complex cognitive-affective interactions most likely involves many other neural functions. Furthermore, while some of the research into the influence of non-conscious processes on cognitive-affective interactions has been discussed above, it is still hotly debated as to how to measure non-conscious processes that are highly inter- and intra-individually variable. Moreover, the perspective of BPI on WM as a mechanism for the subjective experience of cognitive control in AN and SUD remains difficult to interpret, although it seems to raise important questions as to the problem of appetite and impulse control. For example, for a person suffering with AN, do priors become rigidly fixed and impenetrable to prediction error and normal learning processes? For a person suffering with SUD, do prior expectations become overwhelmingly shaped by uncertainty, or does the experience of drug taking also rigidly shape prior expectations about the world? Another issue is the difficulty of honing in on the neural correlates of appetite and impulse control, and of automatic versus deliberative dual systems, due to the heterogeneity of methods, the transdiagnostic nature of illness and the lack of clear knowledge concerning how neural systems dynamically interact over the course of illness.

f) Future directions: How WM training can be used as an adjunct to treatment for those with SUD, and potentially also for those with eating disorders who are resistant to standard evidence-based treatment

To aid the progression of cognitive training research, several principles for achieving robust and specific integration of neural representations have been proposed [135]. Firstly, a desired skill must be practiced via repetitive training on a progressive and frequent schedule (e.g. between 4-8 weeks for half an hour per day). Additionally, ‘scaffolding’ should occur whereby accurate performance on first level easier trials should become gradually more difficult, while maintaining a trial-by-trial reward. Further, attention and reward systems must be consistently engaged with a high proportion of the learning trials attended to, performed correctly and rewarded. Finally, cognitive training approaches should support generalization of improved function to real world environments, such as learning-driven improvements in cognitive and socio-affective operations to untrained stimuli, tasks, and, most importantly, to cognitively demanding real-world situations. Despite these recommendations however, research into WM training is in its infancy, and is therefore perhaps best reserved for those patients who continue to be resistant to standard evidence-based treatment.

In line with recommendations for optimal WM training, researchers in Cape Town, South Africa have developed a Smartphone App called ‘Curb Your Addiction (C-Ya)’ that has evoked improvements in impulse control in patients with methamphetamine dependence (Brooks et al., under review). During this preliminary investigation brain-imaging data has revealed structural and functional differences in the fronto-striatal circuitry following 4 weeks of WM training. The research in Cape Town is being progressed by examining whether out-patients and other psychiatric cohorts have similar beneficial neurobiological alterations following WM training. In terms of AN or other EDs, there have been no studies to date to examine whether WM training improves flexibility and/or relaxes cognitive control over eating. It may at first appear counter-intuitive to consider that WM training can improve cognitive performance in those with AN, given that they already have a rigid control over appetite, and that it may be more suitable to strengthen control for those who engage in binge eating. However, if one considers that extra-ordinary appetite restriction could in itself be an addictive process that is associated with a range of cognitive deficits, WM training may improve the ability to toggle flexibly between cognitive and affective states. Nevertheless, more research is needed in both ED and SUD to determine whether WM training, as an adjunct to boost standard evidence-based treatment, improves brain structure and function in domains associated with the cognitive control of appetite and impulsivity.

Conclusions

There is a phenomenological quality to mind, consciously experienced when WM is exerted to assume control, albeit temporarily, over non-consciously derived impulses and appetites. Simply put, a person suffering from anorexia nervosa is sometimes conscious of compulsively over-exerting self-control despite the risk of debilitating and/or fatal consequences. Similarly, a person with SUD is sporadically aware that negative emotions are only temporarily substituted by the compulsive consumption of a rewarding substance. It is thus intriguing to consider whether WM training can extend the temporary conscious experience of cognitive control via a process of neural plasticity to improve the clinical prognosis for those with AN and SUD. Has this commentary article answered the question as to whether the neuroscience of extra-ordinary cognitive control in those with anorexia nervosa can be used to determine the usefulness of WM training as an adjunct to the treatment of SUDs? Yes and no. Yes, it has provided a précis as to how WM might be utilised in those with anorexia nervosa to suppress appetite, and how it may be annexed in those with SUD. Yes, it has provided suggestions based on evidence as to how WM training can evoke neural plasticity. No, it has not shown that the temporary and limited conscious WM capacity can be expanded, or that potentially beneficial effects of WM training remain in clinical populations. No, it has not fully clarified how WM is used to regulate appetite, although it has discussed some of the theories as to how WM might function in the brain. Thus, it would be beneficial for further research to be conducted in future to consider the role of WM in the conscious experience of appetite and impulse control.

Abbreviations

ACC:

anterior cingulate cortex

ADHD:

attention deficit hyperactivity disorder

AN:

anorexia nervosa

ASD:

autistic spectrum disorder

BN:

bulimia nervosa

BPI:

bayesian probablilistic inference

C-Ya:

Curb Your Addiction

DLPFC:

dorsolateral prefrontal cortex

ECN:

executive control network

ED:

eating disorder

fMRI:

functional magnetic resonance imaging

GWT:

Global Workspace Theory

HC:

healthy control

OCD:

obsessive-compulsive disorder

OCPD:

Obsessive-Compulsive Personality Disorder

OFC:

orbitofrontal cortex

SN:

salience network

SUD:

substance use disorder

WM:

working memory

References

  1. 1.

    Baddeley, Alan D, Graham J. Hitch. Working memory. Psychol Learn Motiv. 1974;8: 47-89.

  2. 2.

    Logie RH, Cowan N. Perspectives on working memory: introduction to the special issue. Mem Cognit. 2015;43(3):315–24.

    PubMed  Article  Google Scholar 

  3. 3.

    Baars BJ, Franklin S, Ramsoy TZ. Global workspace dynamics: cortical “binding and propagation” enables conscious contents. Front Psychol. 2013;4:200.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Libet B. Unconscious cerebral initiative and the role of conscious will in voluntary action. Behav Brain Sci. 1985;8:529–66.

    Article  Google Scholar 

  5. 5.

    Bechara A, Damásio AR, Damásio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50(1–3):7–15.

    PubMed  CAS  Article  Google Scholar 

  6. 6.

    Soon, Chun Siong; Brass, Marcel; Heinze, Hans-Jochen; Haynes, John-Dylan. Unconscious determinants of free decisions in the human brain. Nat Neurosci. 2008; 11 (5): 543–5.

  7. 7.

    Haggard P. Decision time for free will. Neuron. 2011;69(3):404–6.

    PubMed  CAS  Article  Google Scholar 

  8. 8.

    Decker JH, Figner B, Steinglass JE. On weight and waiting: delay discounting in anorexia nervosa pretreatment and posttreatment. Biol Psychiatry. 2015;78(9):606–14.

    PubMed  Article  Google Scholar 

  9. 9.

    Ermakova AO, Ramachandra P, Corlett PR, Fletcher PC, Murray GK. Effects of methamphetamine administration on information gathering during probabilistic reasoning in healthy humans. PLoS One. 2014; 25;9(7):e102683.

  10. 10.

    Nilsson NJ. Probabilistic logic. Artif Intell. 1986;28(1):71–87.

    Article  Google Scholar 

  11. 11.

    Ahmed N, de Visser E, Shaw T, Mohamed-Ameen A, Campbell M, Parasuraman R. Statistical modelling of networked human-automation performance using working memory capacity. Ergonomics. 2014;57(3):295–318.

    PubMed  Article  Google Scholar 

  12. 12.

    Seth AK, Suzuki K, Critchley HD. An Interoceptive predictive coding model of conscious presence. Front Psychol. 2011;2:395.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Doya K. Reinforcement learning: Computational theory and biological mechanisms. HFSP J. 2007;1(1):30–40.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Sternheim L, Startup H, Schmidt U. An experimental exploration of behavioral and cognitive-emotional aspects of intolerance of uncertainty in eating disorder patients. J Anxiety Disord. 2011;25(6):806–12.

    PubMed  Article  Google Scholar 

  15. 15.

    Abbate-Daga G, Quaranta M, Marzola E, Amianto F, Fassino S. The relationship between alexithymia and intolerance of uncertainty in anorexia nervosa. Psychopathology. 2015;48(3):202–8.

    PubMed  Article  Google Scholar 

  16. 16.

    Kaye WH, Wierenga CE, Bailer UF, Simmons AN, Bischoff-Grethe A. Nothing tastes as good as skinny feels: the neurobiology of anorexia nervosa. Trends Neurosci. 2013;36(2):110–20.

    PubMed  CAS  Article  Google Scholar 

  17. 17.

    O'Hara CB, Campbell IC, Schmidt U. A reward-centred model of anorexia nervosa: a focussed narrative review of the neurological and psychophysiological literature. Neurosci Biobehav Rev. 2015;52:131–52.

    PubMed  Article  Google Scholar 

  18. 18.

    Brooks SJ, Rask-Andersen M, Benedict C, Schiöth HB. A debate on current eating disorder diagnoses in light of neurobiological findings: is it time for a spectrum model? BMC Psychiatry. 2012;6(12):76.

    Article  Google Scholar 

  19. 19.

    Ziauddeen H, Alonso-Alonso M, Hill JO, Kelley M, Khan NA. Obesity and the neurocognitive basis of food reward and the control of intake. Adv Nutr. 2015;6(4):474–86.

    PubMed  Article  Google Scholar 

  20. 20.

    Israel M, Klein M, Pruessner J, Thaler L, Spilka M, Efanov S, et al. N-back task performance and corresponding brain-activation patterns in women with restrictive and bulimic eating-disorder variants: preliminary findings. Psychiatry Res. 2015;232(1):84–91.

    PubMed  Article  Google Scholar 

  21. 21.

    Treasure J, Schmidt U. The cognitive-interpersonal maintenance model of anorexia nervosa revisited: a summary of the evidence for cognitive, socio-emotional and interpersonal predisposing and perpetuating factors. J Eat Disord. 2013;1:13.

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Dickson H, Brooks S, Uher R, Tchanturia K, Treasure J, Campbell IC. The inability to ignore: distractibility in women with restricting anorexia nervosa. Psychol Med. 2008;38(12):1741–8.

    PubMed  CAS  Article  Google Scholar 

  23. 23.

    Hatch A, Madden S, Kohn MR, Clarke S, Touyz S, Gordon E, et al. In first presentation adolescent anorexia nervosa, do cognitive markers of underweight status change with weight gain following a refeeding intervention? Int J Eat Disord. 2010;43(4):295–306.

    PubMed  Google Scholar 

  24. 24.

    Brooks SJ, O'Daly OG, Uher R, Schiöth HB, Treasure J, Campbell IC. Subliminal food images compromise superior working memory performance in women with restricting anorexia nervosa. Conscious Cogn. 2012;21(2):751–63.

    PubMed  Article  Google Scholar 

  25. 25.

    Pruis T, Keel P, Janowsky J. Recovery from anorexia nervosa includes neural compensation for negative body image. Int J Eat Disord. 2012;45(8):919–31.

    PubMed  Article  Google Scholar 

  26. 26.

    Kothari R, Solmi F, Treasure J, Micali N. The neuropsychological profile of children at high risk of developing an eating disorder. Psychol Med. 2013;43(7):1543–54.

    PubMed  CAS  Article  Google Scholar 

  27. 27.

    Seed JA, McCue PM, Wesnes KA, Dahabra S, Young AH. Basal activity of the HPA axis and cognitive function in anorexia nervosa. Int J Neuropsychopharmacol. 2002;5(1):17–25.

    PubMed  CAS  Article  Google Scholar 

  28. 28.

    Weider S, Indredavik MS, Lydersen S, Hestad K. Neuropsychological function in patients with anorexia nervosa or bulimia nervosa. Int J Eat Disord. 2015;48(4):397–405.

    PubMed  Article  Google Scholar 

  29. 29.

    Fowler L, Blackwell A, Jaffa A, Palmer R, Robbins TW, Sahakian BJ, et al. Profile of neurocognitive impairments associated with female in-patients with anorexia nervosa. Psychol Med. 2006;36(4):517–27.

    PubMed  CAS  Article  Google Scholar 

  30. 30.

    Nikendei C, Funiok C, Pfüller U, Zastrow A, Aschenbrenner S, Weisbrod M, et al. Memory performance in acute and weight-restored anorexia nervosa patients. Psychol Med. 2011;41(4):829–38.

    PubMed  CAS  Article  Google Scholar 

  31. 31.

    Lao-Kaim NP, Giampietro VP, Williams SC, Simmons A, Tchanturia K. Functional MRI investigation of verbal working memory in adults with anorexia nervosa. Eur Psychiatry. 2014;29(4):211–8.

    PubMed  CAS  Article  Google Scholar 

  32. 32.

    Okon-Singer H, Hendler T, Pessoa L, Shackman AJ. The neurobiology of emotion-cognition interactions: fundamental questions and strategies for future research. Front Hum Neurosci. 2015;17(9):58.

    Google Scholar 

  33. 33.

    Ma L, Steinberg JL, Hasan KM, Narayana PA, Kramer LA, Moeller FG. Stochastic dynamic causal modeling of working memory connections in cocaine dependence. Hum Brain Mapp. 2014;35(3):760–78.

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Ma L, Steinberg JL, Cunningham KA, Lane SD, Bjork JM, Neelakantan H, et al. Inhibitory behavioral control: a stochastic dynamic causal modeling study comparing cocaine dependent subjects and controls. Neuroimage Clin. 2015;7:837–47.

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Stevens MC, Gaynor A, Bessette KL, Pearlson GD. A preliminary study of the effects of working memory training on brain function. Brain Imaging Behav. 2015; [Epub ahead of print].

  36. 36.

    Castro-Fornieles J, Caldú X, Andrés-Perpiñá S, Lázaro L, Bargalló N, Falcón C, et al. A cross-sectional and follow-up functional MRI study with a working memory task in adolescent anorexia nervosa. Neuropsychologia. 2010;48(14):4111–6.

    PubMed  Article  Google Scholar 

  37. 37.

    Decker JH, Figner B, Steinglass JE. On weight and waiting: delay discounting in anorexia nervosa pretreatment and posttreatment. Biol Psychiatry. 2014;78(9):606-14.

  38. 38.

    Titova OE, Hjorth OC, Schiöth HB, Brooks SJ. Anorexia nervosa is linked to reduced brain structure in reward and somatosensory regions: a meta-analysis of VBM studies. BMC Psychiatry. 2013;9(13):110.

    Article  Google Scholar 

  39. 39.

    Brooks SJ, Solstrand Dahlberg L, Swenne I, Aronsson M, Zarei S, Lundberg L, et al. Obsessive-compulsivity and working memory are associated with differential prefrontal cortex and insula activation in adolescents with a recent diagnosis of an eating disorder. Psychiatry Res. 2014; 30;224(3):246-53.

  40. 40.

    Lao-Kaim NP, Fonville L, Giampietro VP, Williams SC, Simmons A, Tchanturia K. Aberrant function of learning and cognitive control networks underlie inefficient cognitive flexibility in anorexia nervosa: a cross-sectional fMRI study. PLoS One. 2015;10(5):e0124027.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Rabinovici GD, Stephens ML, Possin KL. Executive dysfunction. Continuum (Minneap Minn). Behav Neurol Neuropsychiatry. 2015;21(3):646–59.

    Google Scholar 

  42. 42.

    Stip E, Lungu OV. Salience network and olanzapine in schizophrenia: implications for treatment in anorexia nervosa. Can J Psychiatry. 2015;60(3 Suppl 2):S35–9.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    McFadden KL, Tregellas JR, Shott ME, Frank GK. Reduced salience and default mode network activity in women with anorexia nervosa. J Psychiatry Neurosci. 2014;39(3):178–88.

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Kullmann S, Giel KE, Teufel M, Thiel A, Zipfel S, Preissl H. Aberrant network integrity of the inferior frontal cortex in women with anorexia nervosa. Neuroimage Clin. 2014;4:615–22.

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Frank GK. Altered brain reward circuits in eating disorders: chicken or egg? Curr Psychiatry Rep. 2013;15(10):396.

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Kaye WH, Wierenga CE, Bailer UF, Simmons AN, Wagner A, Bischoff-Grethe A. Does a shared neurobiology for foods and drugs of abuse contribute to extremes of food ingestion in anorexia and bulimia nervosa? Biol Psychiatry. 2013;73(9):836–42.

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Phillipou A, Rossell SL, Castle DJ. The neurobiology of anorexia nervosa: a systematic review. Aust N Z J Psychiatry. 2014;48(2):128–52.

    PubMed  Article  Google Scholar 

  48. 48.

    Hendren RL, De Backer I, Pandina GJ. Review of neuroimaging studies of child and adolescent psychiatric disorders from the past 10 years. J Am Acad Child Adolesc Psychiatry. 2000;39(7):815–28.

    PubMed  CAS  Article  Google Scholar 

  49. 49.

    Mühlau M, Gaser C, Ilg R, Conrad B, Leibl C, Cebulla MH, et al. Gray matter decrease of the anterior cingulate cortex in anorexia nervosa. Am J Psychiatry. 2007;164(12):1850–7.

    PubMed  Article  Google Scholar 

  50. 50.

    McCormick LM, Keel PK, Brumm MC, Bowers W, Swayze V, Andersen A, et al. Implications of starvation-induced change in right dorsal anterior cingulate volume in anorexia nervosa. Int J Eat Disord. 2008;41(7):602–10.

    PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Amianto F, Caroppo P, D'Agata F, Spalatro A, Lavagnino L, Caglio M, et al. Brain volumetric abnormalities in patients with anorexia and bulimia nervosa: a voxel-based morphometry study. Psychiatry Res. 2013;213(3):210–6.

    PubMed  Article  Google Scholar 

  52. 52.

    Bär KJ, de la Cruz F, Berger S, Schultz CC, Wagner G. Structural and functional differences in the cingulate cortex relate to disease severity in anorexia nervosa. J Psychiatry Neurosci. 2015;40(4):269–79.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Connan F, Murphy F, Connor SE, Rich P, Murphy T, Bara-Carill N, et al. Hippocampal volume and cognitive function in anorexia nervosa. Psychiatry Res. 2006;146(2):117–25.

    PubMed  Article  Google Scholar 

  54. 54.

    Boghi A, Sterpone S, Sales S, D'Agata F, Bradac GB, Zullo G, et al. In vivo evidence of global and focal brain alterations in anorexia nervosa. Psychiatry Res. 2011;192(3):154–9.

    PubMed  Article  Google Scholar 

  55. 55.

    Gaudio S, Nocchi F, Franchin T, Genovese E, Cannatà V, Longo D, et al. Gray matter decrease distribution in the early stages of Anorexia Nervosa restrictive type in adolescents. Psychiatry Res. 2011;191(1):24–30.

    PubMed  Article  Google Scholar 

  56. 56.

    Friederich HC, Walther S, Bendszus M, Biller A, Thomann P, Zeigermann S, et al. Grey matter abnormalities within cortico-limbic-striatal circuits in acute and weight-restored anorexia nervosa patients. Neuroimage. 2012;59(2):1106–13.

    PubMed  Article  Google Scholar 

  57. 57.

    Frank GK, Shott ME, Hagman JO, Mittal VA. Alterations in brain structures related to taste reward circuitry in ill and recovered anorexia nervosa and in bulimia nervosa. Am J Psychiatry. 2013;170(10):1152–60.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Brooks SJ, Barker GJ, O'Daly OG, Brammer M, Williams SC, Benedict C, et al. Restraint of appetite and reduced regional brain volumes in anorexia nervosa: a voxel-based morphometric study. BMC Psychiatry. 2011;11:179.

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Wesley MJ, Bickel WK. Remember the future II: meta-analyses and functional overlap of working memory and delay discounting. Biol Psychiatry. 2014;75(6):435–48.

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Ellison Z, Foong J, Howard R, Bullmore E, Williams S, Treasure J. Functional anatomy of calorie fear in anorexia nervosa. Lancet. 1998;352(9135):1192.

    PubMed  CAS  Article  Google Scholar 

  61. 61.

    Joos AA, Saum B, van Elst LT, Perlov E, Glauche V, Hartmann A, et al. Amygdala hyperreactivity in restrictive anorexia nervosa. Psychiatry Res. 2011;191(3):189–95.

    PubMed  Article  Google Scholar 

  62. 62.

    Kim KR, Ku J, Lee JH, Lee H, Jung YC. Functional and effective connectivity of anterior insula in anorexia nervosa and bulimia nervosa. Neurosci Lett. 2012;521(2):152–7.

    PubMed  CAS  Article  Google Scholar 

  63. 63.

    Uher R, Murphy T, Brammer MJ, Dalgleish T, Phillips ML, Ng VW, et al. Medial prefrontal cortex activity associated with symptom provocation in eating disorders. Am J Psychiatry. 2004;161(7):1238–46.

    PubMed  Article  Google Scholar 

  64. 64.

    Brooks SJ, O'Daly OG, Uher R, Friederich HC, Giampietro V, Brammer M, et al. Differential neural responses to food images in women with bulimia versus anorexia nervosa. PLoS One. 2011;6(7):e22259.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  65. 65.

    Brooks SJ, O'Daly O, Uher R, Friederich HC, Giampietro V, Brammer M, et al. Thinking about eating food activates visual cortex with reduced bilateral cerebellar activation in females with anorexia nervosa: an fMRI study. PLoS One. 2012. 2012;7(3):e34000.

  66. 66.

    Suda M, Brooks SJ, Giampietro V, Uher R, Mataix-Cols D, Brammer MJ, et al. Provocation of symmetry/ordering symptoms in Anorexia nervosa: a functional neuroimaging study. PLoS One. 2014;9(5):e97998.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Sanders N, Smeets PA, van Elburg AA, Danner UN, van Meer F, Hoek HW, et al. Altered food-cue processing in chronically ill and recovered women with anorexia nervosa. Front Behav Neurosci. 2015;9:46.

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Brooks S, Prince A, Stahl D, Campbell IC, Treasure J. A systematic review and meta-analysis of cognitive bias to food stimuli in people with disordered eating behaviour. Clin Psychol Rev. 2011;31(1):37–51.

    PubMed  Article  Google Scholar 

  69. 69.

    Boulougouris V, Tsaltas E. Serotonergic and dopaminergic modulation of attentional processes. Prog Brain Res. 2008;172:517–42.

    PubMed  CAS  Article  Google Scholar 

  70. 70.

    Val-Laillet D, Aarts E, Weber B, Ferrari M, Quaresima V, Stoeckel LE, et al. Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity. Neuroimage Clin. 2015;8:1–31.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  71. 71.

    Bickel WK, Quisenberry AJ, Moody L, Wilson AG. Therapeutic opportunities for self-control repair in addiction and related disorders: change and the limits of change in trans-disease processes. Clin Psychol Sci. 2015;3(1):140–53.

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Audenaert K, Van Laere K, Dumont F, Vervaet M, Goethals I, Slegers G, et al. Decreased 5-HT2a receptor binding in patients with anorexia nervosa. J Nucl Med. 2003;44(2):163–9.

    PubMed  CAS  Google Scholar 

  73. 73.

    Bailer UF, Frank GK, Henry SE, Price JC, Meltzer CC, Mathis CA, et al. Exaggerated 5-HT1A but normal 5-HT2A receptor activity in individuals ill with anorexia nervosa. Biol Psychiatry. 2007;61(9):1090–9.

    PubMed  CAS  Article  Google Scholar 

  74. 74.

    Galusca B, Costes N, Zito NG, Peyron R, Bossu C, Lang F, et al. Organic background of restrictive-type anorexia nervosa suggested by increased serotonin 1A receptor binding in right frontotemporal cortex of both lean and recovered patients: [18 F]MPPF PET scan study. Biol Psychiatry. 2007;64(11):1009–13.

    Article  CAS  Google Scholar 

  75. 75.

    Brooks SJ, Cedernaes J, Schiöth HB. Increased prefrontal and parahippocampal activation with reduced dorsolateral prefrontal and insular cortex activation to food images in obesity: a meta-analysis of fMRI studies. PLoS One. 2013;8(4):e60393.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  76. 76.

    Yerkes RM, Dodson JD. The relation of strength of stimulus to rapidity of habit-formation. J Comp Neurol Psychol. 1908;18:459–82.

    Article  Google Scholar 

  77. 77.

    Kravitz AV, Tomasi D, LeBlanc KH, Baler R, Volkow ND, Bonci A, et al. Cortico-striatal circuits: Novel therapeutic targets for substance use disorders. Brain Res. 2015;1628(PtA):186-98

  78. 78.

    Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010;35(1):217–38.

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    LeBlanc DM, McGinn MA, Itoga CA, Edwards S. The affective dimension of pain as a risk factor for drug and alcohol addiction. Alcohol. 2015;49(8):803-9.

  80. 80.

    Volkow ND, Baler RD. NOW vs LATER brain circuits: implications for obesity and addiction. Trends Neurosci. 2015;38(6):345–52.

    PubMed  CAS  Article  Google Scholar 

  81. 81.

    Moss AC, Erskine JA, Albery IP, Allen JR, Georgiou GJ. To suppress, or not to suppress? That is repression: controlling intrusive thoughts in addictive behaviour. Addict Behav. 2015;44:65–70.

    PubMed  Article  Google Scholar 

  82. 82.

    Baldacchino A, Balfour DJ, Passetti F, Humphris G, Matthews K. Neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neurosci Biobehav Rev. 2012;36(9):2056–68.

    PubMed  CAS  Article  Google Scholar 

  83. 83.

    Schulte MH, Cousijn J, den Uyl TE, Goudriaan AE, van den Brink W, Veltman DJ, et al. Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment. Clin Psychol Rev. 2014;34(7):531–50.

    PubMed  Article  Google Scholar 

  84. 84.

    Duarte NA, Woods SP, Rooney A, Atkinson JH, Grant I, Translational Methamphetamine AIDS Research Center Group. Working memory deficits affect risky decision-making in methamphetamine users with attention-deficit/hyperactivity disorder. J Psychiatr Res. 2012;46(4):492–9.

    PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Brevers D, Bechara A, Cleeremans A, Kornreich C, Verbanck P, Noël X. Impaired decision-making under risk in individuals with alcohol dependence. Alcohol Clin Exp Res. 2014;38(7):1924–31.

    PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Yan WS, Li YH2, Xiao L3, Zhu N2, Bechara A3, Sui N4. Working memory and affective decision-making in addiction: a neurocognitive comparison between heroin addicts, pathological gamblers and healthy controls. Drug Alcohol Depend. 2014;134:194–200.

    PubMed  Article  Google Scholar 

  87. 87.

    McClure SM, Bickel WK. A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training. Ann N Y Acad Sci. 2014;1327:62–78.

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Verdejo-García A, Bechara A, Recknor EC, Pérez-García M. Executive dysfunction in substance dependent individuals during drug use and abstinence: an examination of the behavioral, cognitive and emotional correlates of addiction. J Int Neuropsychol Soc. 2006;12(3):405–15.

    PubMed  Google Scholar 

  89. 89.

    Leeman RF, Robinson CD, Waters AJ, Sofuoglu M. A critical review of the literature on attentional bias in cocaine use disorder and suggestions for future research. Exp Clin Psychopharmacol. 2014;22(6):469–83.

    PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Ashare RL, Schmidt HD. Optimizing treatments for nicotine dependence by increasing cognitive performance during withdrawal. Expert Opin Drug Discov. 2014;9(6):579–94.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  91. 91.

    McClernon FJ, Froeliger B, Rose JE, Kozink RV, Addicott MA, Sweitzer MM, et al. The effects of nicotine and non-nicotine smoking factors on working memory and associated brain function. Addict Biol. 2015; Epub ahead of print.

  92. 92.

    Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci Biobehav Rev. 2009;33(5):631–46.

    PubMed  Article  Google Scholar 

  93. 93.

    Fineberg NA, Potenza MN, Chamberlain SR, Berlin HA, Menzies L, Bechara A, et al. Probing compulsive and impulsive behaviors, from animal models to endophenotypes: a narrative review. Neuropsychopharmacology. 2010;35(3):591–604.

    PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Ersche KD, Williams GB, Robbins TW, Bullmore ET. Meta-analysis of structural brain abnormalities associated with stimulant drug dependence and neuroimaging of addiction vulnerability and resilience. Curr Opin Neurobiol. 2013;23(4):615–24.

    PubMed  CAS  Article  Google Scholar 

  95. 95.

    London ED, Kohno M, Morales AM, Ballard ME. Chronic methamphetamine abuse and corticostriatal deficits revealed by neuroimaging. Brain Res. 2014;1628 (Pt A): 174-85.

  96. 96.

    Dean AC, Kohno M, Morales AM, Ghahremani DG, London ED. Denial in methamphetamine users: Associations with cognition and functional connectivity in brain. Drug Alcohol Depend. 2015. 2015;151:84-91.

  97. 97.

    Claus ED, Hendershot CS. Moderating effect of working memory capacity on acute alcohol effects on BOLD response during inhibition and error monitoring in male heavy drinkers. Psychopharmacology (Berl). 2015;232(4):765–76.

    CAS  Article  Google Scholar 

  98. 98.

    Thompson PM, Hayashi KM, Simon SL, Geaga JA, Hong MS, Sui Y, et al. Structural abnormalities in the brains of human subjects who use methamphetamine. J Neurosci. 2004;24(26):6028–36.

    PubMed  CAS  Article  Google Scholar 

  99. 99.

    Morales AM, Kohno M, Robertson CL, Dean AC, Mandelkern MA, London ED. Midbrain dopamine D2/D3 receptor availability and drug craving are associated with mesocorticolimbic gray matter volume in methamphetamine users. Mol Psychiatry. 2015;20(6):658.

    PubMed  CAS  Article  Google Scholar 

  100. 100.

    Shoptaw S, Reback CJ, Peck JA, Yang X, Rotheram-Fuller E, Larkins S, et al. Behavioral treatment approaches for methamphetamine dependence and HIV-related sexual risk behaviors among urban gay and bisexual men. Drug Alcohol Depend. 2005;78(2):125–34.

    PubMed  Article  Google Scholar 

  101. 101.

    Fletcher JB, Shoptaw S, Peck JA, Reback CJ. Contingency management reduces symptoms of psychological and emotional distress among homeless, substance-dependent men who have sex with men. Ment Health Subst Use. 2014;7(4):420–30.

    PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Martinez D, Carpenter KM, Liu F, Slifstein M, Broft A, Friedman AC, et al. Imaging dopamine transmission in cocaine dependence: link between neurochemistry and response to treatment. Am J Psychiatry. 2011;168(6):634–41.

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Gasbarri A, Pompili A, Packard MG, Tomaz C. Habit learning and memory in mammals: behavioral and neural characteristics. Neurobiol Learn Mem. 2014;114:198–208.

    PubMed  Article  Google Scholar 

  104. 104.

    Grant JE, Chamberlain SR. Impulsive action and impulsive choice across substance and behavioral addictions: cause or consequence? Addict Behav. 2014;39(11):1632–9.

    PubMed  Article  Google Scholar 

  105. 105.

    Brooks SJ, Savov V, Allzén E, Benedict C, Fredriksson R, Schiöth HB. Exposure to subliminal arousing stimuli induces robust activation in the amygdala, hippocampus, anterior cingulate, insular cortex and primary visual cortex: a systematic meta-analysis of fMRI studies. Neuroimage. 2012;59(3):2962–73.

    PubMed  CAS  Article  Google Scholar 

  106. 106.

    Swartz JR, Monk CS. The role of corticolimbic circuitry in the development of anxiety disorders in children and adolescents. Curr Top Behav Neurosci. 2014;16:133–48.

    PubMed  Article  Google Scholar 

  107. 107.

    Tanaka SC, Doya K, Okada G, Ueda K, Okamoto Y, Yamawaki S. Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nat Neurosci. 2004;7:887–93.

    PubMed  CAS  Article  Google Scholar 

  108. 108.

    Radu PT, Yi R, Bickel WK, Gross JJ, McClure SM. A mechanism for reducing delay discounting by altering temporal attention. J Exp Anal Behav. 2011;96(3):363–85.

    PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Rolls ET. Emotion and decision-making explained: a précis. Cortex. 2014;59:185–93.

    PubMed  Article  Google Scholar 

  110. 110.

    Garrison J, Erdeniz B, Done J. Prediction error in reinforcement learning: a meta-analysis of neuroimaging studies. Neurosci Biobehav Rev. 2013;37(7):1297–310.

    PubMed  Article  Google Scholar 

  111. 111.

    Gowin JL, Mackey S, Paulus MP. Altered risk-related processing in substance users: imbalance of pain and gain. Drug Alcohol Depend. 2013;132(1-2):13–21.

    PubMed  PubMed Central  Article  Google Scholar 

  112. 112.

    Subramaniam K, Luks TL, Fisher M, Simpson GV, Nagarajan S, Vinogradov S. Computerized cognitive training restores neural activity within the reality monitoring network in schizophrenia. Neuron. 2012;73:842–53.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  113. 113.

    Keshavan MS, Vinogradov S, Rumsey J, Sherrill J, Wagner A. Cognitive training in mental disorders: update and future directions. Am J Psychiatry. 2014;171(5):510–22.

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Eack SM, Hogarty GE, Cho RY, Prasad KMR, Greenwald DP, Hogarty SS, et al. Neuroprotective effects of cognitive enhancement therapy against gray matter loss in early schizophrenia: results from a 2-year randomized controlled trial. Arch Gen Psychiatry. 2010;67:674–82.

    PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Haut KM, Lim KO, MacDonald 3rd A. Prefrontal cortical changes following cognitive training in patients with chronic schizophrenia: effects of practice, generalization, and specificity. Neuropsychopharmacology. 2010;35:1850–9.

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Harro J. Neuropsychiatric adverse effects of amphetamine and methamphetamine. Int Rev Neurobiol. 2015;120:179–204.

    PubMed  Article  Google Scholar 

  117. 117.

    Zulauf CA, Sprich SE, Safren SA, Wilens TE. The complicated relationship between attention deficit/hyperactivity disorder and substance use disorders. Curr Psychiatry Rep. 2014;16(3):436.

    PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Beck SJ, Hanson CA, Puffenberger SS, Benninger KL, Benninger WB. A controlled trial of working memory training for children and adolescents with ADHD. J Clin Child Adolesc Psychol. 2010;39:825–36.

    PubMed  Article  Google Scholar 

  119. 119.

    Holmes J, Gathercole SE, Dunning DL. Adaptive training leads to sustained enhancement of poor working memory in children. Dev Sci. 2009;12:F9–F15.

    PubMed  Article  Google Scholar 

  120. 120.

    Karbach J, Kray J. How useful is executive control training? Age differences in near and far transfer of task-switching training. Dev Sci. 2009;12:978–90.

    PubMed  Article  Google Scholar 

  121. 121.

    Peijnenborgh JC, Hurks PM, Aldenkamp AP, Vles JS, Hendriksen JG. Efficacy of working memory training in children and adolescents with learning disabilities: A review study and meta-analysis. Neuropsychol Rehabil. 2015;17:1–28.

    Article  Google Scholar 

  122. 122.

    Spencer-Smith M, Klingberg T. Benefits of a working memory training program for inattention in daily life: a systematic review and meta-analysis. PLoS One. 2015;20:10(3).

    Google Scholar 

  123. 123.

    Klingberg T. Training and plasticity of working memory. Trends Cogn Sci. 2010;14:317–24.

    PubMed  Article  Google Scholar 

  124. 124.

    Grunewaldt KH, Skranes J, Brubakk AM, Lähaugen GC. Computerized working memory training has positive long-term effect in very low birthweight preschool children. Dev Med Child Neurol. 2015. [Epub ahead of print].

  125. 125.

    Holmes J, Butterfield S, Cormack F, van Loenhoud A, Ruggero L, Kashikar L, et al. Improving working memory in children with low language abilities. Front Psychol. 2015;6:519.

    PubMed  PubMed Central  Article  Google Scholar 

  126. 126.

    Shinaver 3rd CS, Entwistle PC, Söderqvist S. Cogmed WM training: reviewing the reviews. Appl Neuropsychol Child. 2014;3(3):163–72.

    PubMed  Article  Google Scholar 

  127. 127.

    Melby-Lervag M, Hulme C. Hulme. Is working memory training effective? A meta-analytic review. Dev Psychol. 2012;49(2):270–91.

    PubMed  Article  Google Scholar 

  128. 128.

    Shipstead Z, Hicks K, Engle RW. Cogmed working memory training: Does the evidence support the claims? J Appl Res Mem Cogn. 2012;1(3):185–93.

    Article  Google Scholar 

  129. 129.

    Redick TS, Shipstead Z, Harrison TL, Hicks KL, Fried DE, Hambrick DZ, et al. No evidence of intelligence improvement after working memory training: a randomized, placebo-controlled study. J Exp Psychol Gen. 2013;142(2):359–79.

    PubMed  Article  Google Scholar 

  130. 130.

    Chooi W-T, Thompson LA. Working memory training does not improve intelligence in healthy young adults. Intelligence. 2012;40(6):531–42.

    Article  Google Scholar 

  131. 131.

    Bickel WK, Yi R, Landes RD, Hill PF, Baxter C. Remember the future: working memory training decreases delay discounting among stimulant addicts. Biol Psychiatry. 2011;69(3):260–5.

    PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Sofuoglu M, DeVito EE, Waters AJ, Carroll KM. Cognitive enhancement as a treatment for drug addictions. Neuropharmacology. 2013;64:452–63.

    PubMed  PubMed Central  CAS  Article  Google Scholar 

  133. 133.

    van Dongen-Boomsma M, Vollebregt MA, Slaats-Willemse D, Buitelaar JK. Efficacy of frequency-neurofeedback and Cogmed JM-working memory training in children with ADHD. Tijdschr Psychiatr. 2015;57(7):508–16.

    PubMed  Google Scholar 

  134. 134.

    Dovis S, Van der Oord S, Wiers RW, Prins PJ. Improving executive functioning in children with ADHD: training multiple executive functions within the context of a computer game. A randomized double-blind placebo controlled trial. PLoS One. 2015;10(4):e0121651.

    PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    Vinogradov S, Fisher M, de Villers-Sidani E. Cognitive training for impaired neural systems in neuropsychiatric illness. Neuropsychopharmacology. 2012;37(1):43–76.

    PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

SJB was funded by the NIH NIDA (R21 DA040492), and guided by Prof Dan J. Stein at the Department of Psychiatry, University of Cape Town, and by Prof Steve Shoptaw at the University of California, Los Angeles. With thanks to Ms. Tali Lanesman, Ms Lara van Nunen and Mr Stefano Maiorana for constructive discussions.

In memory of my friend, colleague and mentor, the late Professor Bryan Lask.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Samantha J. Brooks.

Additional information

Competing interests

The author has no competing interests.

Author’s contributions

SJB devised and wrote the manuscript.

Additional file

Additional file 1:

Other neurobiological eating disorder research. Other neurobiological SUD research [4145, 4751, 5358, 6063, 65, 66, 69, 93102]. (DOCX 16 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brooks, S.J. A debate on working memory and cognitive control: can we learn about the treatment of substance use disorders from the neural correlates of anorexia nervosa?. BMC Psychiatry 16, 10 (2016). https://doi.org/10.1186/s12888-016-0714-z

Download citation

Keywords

  • Anorexia nervosa
  • Substance use disorder
  • Working memory
  • Cognitive training
  • Neuroplasticity
  • MRI