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date: 14 November 2019

Developmental Translational Research: Adolescence, Brain Circuitry, Cognitive Processes, and Eating Disorders

Abstract and Keywords

In this concluding chapter, we discuss the interplay between brain development during adolescence; the changes in anatomy, function, and neurocircuitry during this period; and its impact on different symptom presentations in eating disorders. The main premise is that there is, even in normally developing adolescents, a mismatch between capacities for top-down cognitive control and reward-seeking behavior. This mismatch leads to increased behavioral impulsiveness during adolescence. However, this mismatch is also sensitive to both environmental and social processes, so that, together, these biological and environmental processes may generate a range of impulsive behaviors common to externalizing disorders of adolescence. On the other hand, in some adolescents, excessive cognitive control leads to the anxious, inhibited problems associated with internalizing disorders. The two main eating disorder groups—bulimia nervosa and anorexia nervosa—may represent opposite ends of this spectrum. To discuss this possibility, we review adolescent brain development with a specific focus on cognitive control and its relationship to eating disorder types with particular reference to recent neuroimaging findings. The implication of these data for diagnosis, intervention, and future research in child and adolescent eating disorders are discussed in concluding comments.

Keywords: cognitive control, externalizing disorders, internalizing disorders adolescents

As discussed in previous chapters, risk (Jacobi, Jones, & Beintner, 2011, Chapter 10, this volume) epidemiological (Pinhas & Bondy, 2011, Chapter 5, this volume), cognitive developmental processes (Zucker & Harshaw, 2011, Chapter 3, this volume), biological (Sadler and Peebles., Chapter 8, this volume), and treatment response data (Wilfley, Kolko, & Kass, 2011, Chapter 15, this volume; Fitzpatrick, 2011, Chapter 14, this volume) all suggest that adolescence is a critical period for the onset and treatment of eating disorders. In addition to these factors, adolescent brain development is likely a contributor to increased risk for the onset of eating disorders. Adolescence is a key period for evolving and integrating executive functioning, and data suggest that such processes play a role in a range of psychiatric disorders, including eating disorders (Briskman, Happe, & Frith, 2001; Chamberlain et al., 2007; Happe, 1996; Park & et al., 2006; Sanders, Johnson, Garavan, Gill, & Gallagher, 2008). Understanding how these processes operate in an adolescent population of eating disordered patients may help to explain why the adolescent period is a high-risk time for the onset of the disorder, as well as potentially explain why adolescents may be more responsive to treatment during this period (Steinhausen, 2009).

During adolescence synaptic pruning, elaboration of dendritic arborization and increased myelination contribute to the most significant remaking of the brain since early childhood (Luna & Sweeney, 2004). The purpose of these changes in brain structure is to support the integration of brain circuitry—in particular those circuits in the prefrontal cortex (PFC) and subcortical structures. These circuits are associated with executive functioning areas of the brain. Executive functioning encompasses a wide range of “top-down” processes (p. 306) and includes inhibition (Southgate, 2005), selective attention, goal setting, planning, set maintaining, decision making, and flexibility. This maturational process does not always go smoothly and is sometimes associated with a range of externalizing or internalizing behavioral difficulties (Casey, Jones, & Hare, 2008; Marsh, Maia, & Peterson, 2009). In this chapter, we review the general process of developing cognitive control in adolescents, as formulated by Casey and colleagues, and consider the specific application of this developmental model to eating disorders. Next, we review the literature on cognitive processes in eating disorders and their relationship to clinical phenotypic presentation (Marsh et al., 2009). We also consider recent translational research in neuroimaging to shed additional light on the implications of differing trajectories of cognitive control on the expression of eating disorder symptoms.

Dynamics in the Development of Cognitive Control over the Lifespan

To better understand the role of cognitive control in symptom development in eating disorders, a short review of current thinking on this subject will be helpful to provide a developmental context. Brain development undergoes significant alteration in adolescence (Keverne, 2004), and development of executive functioning skills is a particularly dynamic process during this period (Nelson, Leibenluft, McClure, & Pine, 2005), associated with increasing abilities pertaining to decision making, social processing, and inhibitory control. These refinements lead to what has been called the “collaborative brain” (Luna & Sweeney, 2004), wherein improved connections allow the PFC to modulate critical interconnected subcortical structures. In the past several years, an increasing number of studies have begun to examine the developmental neurobiology of adolescence (Somerville & Casey, 2010). These studies suggest that the view that adolescent behavior associated with risk-taking and impulsiveness is secondary simply to an immature PFC leading to poorer cognitive control is inaccurate. Instead, more recent studies find that adolescents show a developmentally informed sensitivity to reward systems that tax the immature cognitive control system. This leads to dysregulation of the frontostriatal circuit connecting the PFC and the striatum in a way that is unique to adolescence (Casey, Getz, & Galvan, 2008; Casey, Jones, & Hare, 2008). In addition, these studies highlight the importance of understanding the network and interaction of the frontostriatal circuit rather focusing on the maturation of the PFC alone.

Evidence suggests that cognitive control capacities develop in a linear fashion, whereas the capacity to inhibit responses is attenuated by motivational cues (Somerville & Casey, 2010). Studies of adolescents using a gambling task showed that adolescents make more risky gambles than do adults, but only in emotionally charged conditions (Figner, Mackinlay, Wilkening, & Weber, 2009). Other studies find that the sensitivity to rewards and incentives peaks during adolescence (Steinberg, Graham, & O’Brien, 2009). Thus, motivational cues and rewards are particularly salient during adolescence and may thereby undermine the capacity for limited cognitive control in the developmental period (Somerville & Casey, 2010).

Evidence that supports this model of frontostriatal circuit function in adolescence can be found in a range of human and animal studies (Pasupathy & Miller, 2005). In human neuroimaging studies using diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), Casey and colleagues found that increased connection between the PFC and striatum leads to increased cognitive control (Casey et al., 2007). Further, rather than the maturity of each subsystem alone, it appears that it is the circuit connecting them that is key to understanding the dynamics of cognitive control and reward processing during adolescence. To illustrate this point at a microanatomic level, Fair and colleagues found a lessening of short-range functional connections between adjacent regions and a strengthening of connections to more distal regions from childhood through adulthood (Fair et al., 2009; Stevens, Skudlarski, Pearlson, & Calhoun, 2009). In addition, adolescent cognitive control is mediated by environmental cues and rewards. Compared to adults, adolescents demonstrated greater sensitivity to reward using a monetary reward task, with evidence of increased activation in the dorsal and ventral striatum on fMRI (Van Leijenhorst et al., 2010). It also appears that this increased response to reward is correlated with actual risk-taking behaviors (Galvan, Hare, Voss, Gover, & Casey, 2007).

These types of studies suggest to Casey and colleagues that, unlike the PFC, which follows a linear developmental trajectory from childhood to adulthood, the striatum takes an inverted U-shaped course, with evidence of increased relevance for adolescents compared to younger children and adults (Somerville & Casey, 2010). In support of this view, (p. 307) recent work by Ernst and colleagues found that adolescents’ cognitive control could be increased by the promise of monetary reward much more than in adults (Ernst et al., 2005), and the neural underpinnings of this exaggerated response were identified in the ventral striatum using fMRI by Geier et al. (Geier, Terwilliger, Teslovich, Velandova, & Luna, 2010).

How might this view of the frontostriatal circuitry map onto thinking about phenotypic presentation, symptom development, and related features and comorbidity in eating disorders? There is convergent evidence that dividing psychiatric disorders broadly into internalizing and externalizing disorders is reasonable (Kreuger, 1999; Kreuger, Caspi, Moffitt, & Silva, 1998). Internalizing disorders include such disorders as depression and anxiety disorders, whereas externalizing disorders include antisocial personality disorder, conduct disorder, and substance abuse disorders. Interestingly, a recent report by Kendler using a sample of over 2,000 Norwegian twins found genetic support for this broad dichotomous categorization (Kendler et al., 2011). In addition, Kendler and colleagues found that eating disorders had a unique pattern of risk because it was the only diagnostic group that appeared to require high-risk genes from both the internalizing and externalizing dimensions. The sample used in this study could not or did not distinguish between types of eating disorders, thereby apparently putting both restrictive and binge–purge disorders together in the analysis. This might explain the finding that eating disorders as a group straddle the internalizing–externalizing divide because anorexia nervosa (AN) is phenotypically associated with anxious, inhibited, and avoidant states similar to internalizing disorders, whereas bulimia nervosa (BN) is associated with the externalizing phenotype of undercontrolled, impulsive, and disinhibited cognitions and behaviors.

Casey and colleagues portray the dysregulation in the frontostriatal circuit using the example of substance abuse disorders in adolescence (Casey & Jones, 2010). These authors illustrate the behavioral impact of under control relative to salient environmental cues over activating the reward systems in the striatum. A mismatch in the direction of under control maps well onto the phenotypic presentation of bulimia, in which loss of control and other symptoms of impulsive behavior are common. How does this model map on disorders of anxiety and overcontrol? As with externalizing disorders, internalizing symptoms often are exacerbated during adolescence, with rates of anxiety disorders, depression, and AN increasing (e.g., Costello, 1995). In these cases, though, instead of increasing impulsiveness and loss of cognitive control, patients display symptoms of obsessiveness, compulsiveness, and avoidance. These types of symptoms suggest that there is an alternative outcome to the mismatch between PFC and the striatum. The findings suggest that dysregulation of this circuit through another process leads to excessive control and inhibition. How might this be?

Examining the fronto-amygdala circuit in adolescents, a few studies suggest differences in anxious adolescents compared to controls (McHugo, 2010). A recent study by Hare and colleagues compared emotional reactivity and cognitive inhibition in adolescents who were highly anxious to those who were not using a Go/No-Go fearful faces task (Hare et al., 2008). These researchers found that both groups showed initially high activation in the amygdala, but the normally developing adolescents habituated to the task and activation levels decreased with repeated exposures, whereas the anxious group continued to display exaggerated activation. The failure to habituate to the fearful cues suggests an inability for anxious adolescents to learn as well from their experiences and to adjust their emotional responses (Casey et al., 2010). In other words, they continue to have fear responses when they are no longer appropriate. Such a pattern might underlie the symptoms of anxiety and obsessiveness that characterize internalizing disorders.

In line with this view that persons with internalizing disorders may not habituate to threat or may over-react to threat, Kaye and colleagues have suggested that, in AN, there may be oversensitivity to reward—even a small stimulus may lead to an overwhelming response that is unpleasant enough to lead to attempts to avoid its repetition (Kaye, Fudge, & Paulus, 2009). They postulate that the neurobiological basis may be related to a dysregulated dopaminergic system (Kaye, Frank, & McConaha, 1999). As we discuss in more detail below, neuroimaging studies using both images of food and taste provide some support for this hypothesis. In contrast, evidence from some adult BN neuroimaging studies indicate decreased activation in areas of cognitive control (Marsh, Steinglass et al., 2009). In the case of internalizing disorders, environmental and motivational cues excessively engage higher-order cognitive control in a way that leads to anxious, compulsive, and avoidant features. Thus, it is possible that, in vulnerable adolescents, dysregulation (p. 308) of the frontostriatal or fronto-amygdala circuits can lead to either under- or overcontrol depending on how reward is experienced. In some adolescents, reward is pleasurable and behaviors (including risky and impulsive ones) that increase rewards are predictable; whereas in other adolescents, reward is actually not rewarding but ego dystonic (as in obsessive compulsive disorder [OCD] and AN), so that similar behaviors are to be avoided in the future. A few studies support that this alternative outcome dysregulation of the frontostriatal circuit for internalizing disorders is plausible for disorders such as OCD (Casey et al., 2010; Marsh, Maia, & Peterson, 2009).

In the following sections, we detail the implications of this model of adolescent brain development for eating disorders.

Cognitive Process, Eating Disorders, and the Adolescent Brain

Evidence from neuropsychological studies suggests that persons with eating disorders demonstrate inefficiencies and strengths related to executive functioning (see also Zucker & Harshaw, 2011, Chapter 3, this volume). For example, cognitive flexibility is needed for efficient perspective taking, goal setting, and decision making, and appears to be a problem for many people with eating disorders, particularly those with AN (Byford et al., 2007; Tchanturia et al., 2004; Tchanturia, Morris, Surguladze, & Treasure, 2002). Set-shifting, a cognitive task that assesses cognitive flexibility and involves the ability to move back and forth between tasks, operations, and sets, is more difficult for those with eating disorders (Steinglass, Walsh, & Stern, 2006). Individuals with AN take significantly longer to set-shift than do subjects with similar IQs who do not have AN (Tchanturia et al., 2004), and this is the case even after they are recovered from AN (Tchanturia et al., 2002, 2004). These inefficiencies in set-shifting have also been found in unaffected relatives, suggesting a familial genetic origin (Holliday, Tchanturia, Landau, & Collier, 2005).

In addition to executive functioning related to cognitive flexibility, the ability to process and organize information into meaningful wholes is a key cognitive function. To accomplish this requires what is referred to as central coherence cognitive capacities. The concept of central coherence was introduced by Frith (1989) to describe a failure to integrate highly detailed information into a meaningful whole in the context of autism (Frith, 1989). Thus, inefficient or weak central coherence suggests a bias toward local or analytical processing. The result is a focus on “trees” rather than “forest” type of thinking. There are benefits to this detailed type of thinking for certain tasks, and evidence suggests that AN patients excel at finding detail. For example, researchers using the Matching Familiar Figures Test found that, compared with healthy controls, AN women showed greater efficiency on this task through faster response times and superior accuracy in identifying the target pictures (Roberts, Tchanturia, Stahl, Southgate, & Treasure, 2007). Similarly, women with AN excelled in their performance of finding figures embedded in a field (detail focus) using The Embedded Figures Test compared with matched comparison subjects (Tokley & Kemps, 2007). However, weak central coherence can also be a liability, leading to a perseverative thinking style associated with excessive preoccupation with details to the neglect of the gestalt (Gillberg, Rastam, Wentz, & Gillberg, 2007). Gillberg et al. (1996) found that, compared with matched controls, the performance of adolescents with AN on the object assembly task—a task that requires seeing the figure as whole to be successful—was significantly poorer (Gillberg, Gillberg, Råstam, & Johansson, 1996). Similarly, Lopez and colleagues, when comparing women with AN and healthy matched control women, found the AN sample also took longer to produce appropriate (i.e., global) completions on the Homograph Sentence Completion Task than did the controls, indicative of a conflict between local and global processing. In addition, Sherman et al. (2006) found that AN patients displayed a piecemeal drawing style when copying and reconstructing the Rey-Osterrieth Complex Figure (ROCF) and that their recall of the figure was also less accurate (Sherman et al., 2006). There is comparatively less study of weak central coherence in BN, but the available data suggest that BN subjects also have an overly detailed processing style. However, their profile differs in some respects as they appear to be worse than AN subjects or matched comparisons in finding embedded figures and in copying and recalling the ROCF (Lopez, Tchanturia, Stahl, & Treasure, 2008).

Turning to cognitive inhibition in BN, explanations for apparent differences between AN and BN may be related to behavioral impulsiveness. In contrast to the anxious, overly inhibited, and cognitively rigid characteristics associated with AN, disinhibition and impulsivity are hallmarks of BN (p. 309) (Bruce, Koerner, Steiger, & Young, 2003; Rosval et al., 2006). This impulsivity often extends into other areas of life (Wagner et al., 2006) as individuals with BN report alcohol and drug abuse, self-harm, sexual disinhibition, and shoplifting (Rosval et al., 2006). Some data suggest that the basis of cognitive and behavioral disinhibition in BN may be found at a neurocognitive level. For example, in a study comparing healthy controls to subjects with BN who use laxatives, it was found that those with BN made significantly more errors of commission on a Go/No-Go task, a task designed to assess cognitive control (Bruce et al., 2003). Using a motor stop signal paradigm and a motor Stroop task also designed to assess cognitive control, researchers found that restricting-type AN patients displayed superior response inhibition overall with fewer impulsive errors than did patients with binge–purge subtype AN (AN-BP), suggesting that those with binge–purge characteristics were less able to inhibit responses (Southgate, 2005). A recent study compared cognitive flexibility—a cognitive capacity related to cognitive inhibition—in AN, AN-BP, BN, and recovered patients of these types with healthy controls. Although cognitive inflexibility was found among all the eating disorder groups whether ill or recovered compared with controls, the greatest difficulties were among those who had binge–purge characteristics (Roberts, Tchanturia, & Treasure, 2010).

Reward Processing and Cognitive Control Studies in Anorexia Nervosa

Studies using fMRI have employed a variety of tasks to explore functional changes in AN. For example, some have studied food-relevant paradigms and demonstrated elevated temporal lobe activation (Gordon, Dougherty, & Fishman, 2001) and elevated medial PFC and anterior cingulated cortex (ACC) activation in both underweight and recovered patients (Kurosaki, Shirao, Yamashita, Okamoto, & Yamawaki, 2006). Researchers have speculated that there might be differential brain activation in AN patients in areas related to multiple cognitive functions including visual–spatial, reward processing, and neural responses to food stimuli. For example, a recent study employing a self-report measure found evidence of heightened sensitivity to reward and punishment in AN, consistent with the notion that individuals would try to minimize exposure to these experiences, including those associated with eating, and in this way reinforce restraint and dieting (Jappe et al., 2010). To test the possibility that reward processing might differ in AN using fMRI, weight-restored AN subjects and controls were asked to taste a sucrose solution or water while undergoing scanning (Kurosaki et al., 2006). The results supported the hypotheses that reward processing differed in AN. Weight-restored patients with AN showed comparatively decreased activation in the insula and striatum during the sucrose tasting, but increased activation was found in the striatum during the non–food related reward processing task. In another study testing reward processing in AN, Wagner and colleagues using a monetary reward task found differences between AN subjects and comparisons. They found that AN subjects had increased dorsolateral PFC activation compared to controls (Wagner et al., 2007).

In addition to reward processing, recent neuroimaging data has examined how cognitive control may play a role in AN through the involvement of frontostriatal brain circuitry (Marsh, Maia et al., 2009; Zastrow et al., 2009). Neuroimaging studies in healthy controls have delineated the main regions associated with this circuit, which include the dorsal ACC, and inferior, middle, and superior frontal gyri. Activity in these regions helps to focus attention and planning and modulates activity in the posterior and subcortical regions. A few studies have identified abnormalities in these regions consistent with the hypothesis that differences in neural activity in this region may play a role in AN. Previous studies found evidence of hypoperfusion in the ACC and medial PFC (Frank et al., 2007). Other more recent studies have identified neural correlates of cognitive inflexibility in a sample of adults with AN (Zastrow et al., 2009). Zastrow and colleagues (2009) found decreased activation in the ACC and striatum associated with impaired cognitive-behavioral flexibility in patients with AN (Zastrow et al., 2009). A recent study using a stop signal task (Go/No-Go task) found that, with relatively easy inhibition challenges, there were no differences between restricting AN and comparison healthy controls in activation levels in the medial PFC, but as difficulty increased, significant differences emerged (Oberndorfer, Kaye, Simmons, Strigo, & Matthews, 2011). Subjects with AN showed comparatively lower activation levels in these regions, suggesting that they required fewer cognitive resources than did healthy controls to inhibit response. These results support the notion than AN subjects have greater abilities for cognitive control (p. 310) and that this may also be a contributor to their risk for the disorder.

Reward Processing and Cognitive Control in Bulimia Nervosa

Assessment of reward circuitry using fMRI has yielded some interesting preliminary findings related to cognitive control in BN. Studies found activation in the lateral fusiform gyrus and inferior parietal cortex to body image cues was less marked in people with BN, and aversion ratings were positively correlated with activity in the right medial apical PFC compared to healthy controls (Uher et al., 2005). Further, BN patients show increased sensitivity to appetitive motivational system in response to food that parallels findings in substance abuse. These results suggest that binge eating and substance dependence might share alternations in brain reward circuits. In addition, an fMRI study by Frank et al. (2004) identified reduced ACC activity compared to controls in response to a glucose challenge in recovered bulimic subjects (Frank, Bailer, Henry, Wagner, & Kaye, 2004). The ACC is a cuneus area that is involved in error monitoring and also anticipation of reward. In the paradigm used by Frank et al., the subjects knew which taste stimulus to expect, therefore higher activity in controls would suggest higher reward expectation by controls than anticipated by BN subjects. Interestingly, a more recent study of adults with BN using a taste challenge and anticipatory reward found decreased activation in gustatory and reward regions (i.e., left middle frontal gyrus, right posterior insula, right precentral gyrus, and left thalamus) (Bohon & Stice, 2010). Whereas some behavioral studies might anticipate greater activation in these areas (Farmer, Nash, & Field, 2001), the authors of this report speculated that chronic stimulation from repetitive binge episodes may blunt activation.

Turning to cognitive control in BN, a few fMRI studies have investigated disinhibition. When food images are presented to BN subjects while being scanned, areas related to affective processing and control and planning of behavior are activated (i.e., the limbic system, the ACC, and PFC) as opposed to the inferior parietal lobe and left cerebellum, which were activated in the healthy comparison group (Uher et al., 2004). At the same time, though, among the BN subjects there was less activation in the dorsolateral region of the PFC—an area associated with cognitive inhibition (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003). Marsh, Steinglass, et al. (2009) also examined cognitive inhibition in patients with BN and found that BN subjects were more impulsive and made more errors than did healthy controls. Further, those subjects with the most severe symptoms performed the most poorly. The authors found that patients failed to activate frontostriatal circuits to the same degree as healthy controls. Specifically lower activation was found in the left inferolateral PFC, bilateral inferior frontal gyrus, lenticular and caudate nuclei, and the ACC.

In a recent study of adolescent BN, cognitive inhibition was tested using the Go/No-Go task (Lock, Garrett, Beenhaker, & Reiss, 2011). The authors hypothesized that brain activation associated with inhibitory control would differ in adolescents with eating disorders compared with healthy controls and that that those with binge–purge behaviors would have abnormal activation in the frontostriatal regions typically associated with impulsivity compared with healthy controls and those with restricting type AN. The subject pool included only adolescents between the ages of 12 and 18 years. There were 15 female restricting-type AN adolescents, 16 with binge–purge behaviors (12 with BN and four with AN-BP), and 15 healthy control subjects. A three-group ANOVA found a significant main effect of group in the bilateral hypothalamus, right dorsolateral PFC, right ACC, right middle temporal gyrus, and bilateral precentral gyri. In a follow-up between-group analysis, group differences were accounted for by increased activation in the binge–purge group. Specifically, this group displayed increased activation in the right dorsolateral PFC, suggesting that inefficient or possibly compensatory activation was needed for executive control. This finding suggests that recruitment of additional brain regions might be needed to improve cognitive inhibition processes (Han, Bangen, & Bondi, 2009). The increased hypothalamic activation also identified in this group could suggest that the binge–purge group is more stressed during response inhibition (Ahs et al., 2006), perhaps as a result of the increased effort needed to inhibit.

Although the frontostriatal regions were activated in adolescent BN, the direction of activation differs from that found by Marsh, Steinglass, et al. (2009), where decreased activation in frontostriatal regions in adults with BN was found. This difference could be due to task differences, developmental differences in terms of age and cognitive maturity, or clinical severity (e.g., duration of illness, binge–purge behavior frequency). It could also be a result (p. 311) of somewhat differing populations, as March used only those with a BN diagnosis, whereas Lock and colleagues combined BN with binge–purge type AN (Lock et al., 2011). Nonetheless, together, these studies support the idea that abnormalities in the PFC associated with executive control of behavioral and cognitive processes likely play a role in eating disorder symptoms. Further, these data suggest that the role may differ between disorders based on differences in inhibitory control.

Clinical and Research Implications of Inefficient and Dysregulated Cognitive Processes in Eating Disorders

There are a number of possible implications of the forgoing discussion should data continue to support findings suggesting a key role of cognitive processes—particularly cognitive inhibition—in the development of eating disorders. Here, we briefly explore some of these in the areas of diagnosis, treatment, and future research.

Continued confusion exists about how best to categorize eating disorders (see Couturier & Van Blyderveen, 2011, Chapter 7, this volume). Using current Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnostic criteria, most patients with eating disorders, whether adults or children, are placed in the nonspecific category of eating disorder not otherwise specified (EDNOS; Turner & Bryant-Waugh, 2004). As prognosis and treatment of this heterogeneous group might differ, it would be helpful to find a strategy for more accurate diagnosis. Further, there appears to be high rates of cross-over from AN to BN, at least in adults (Eddy et al., 2008). To remedy this, a range of suggestions have been made. On one hand, there is the suggestion that there are such similarities between eating disorder subgroups that a single transdiagnostic category of Eating Disorder would simplify matters (Fairburn, Cooper, & Safran, 2002). However, as the foregoing discussion illustrates, there is a developing neurobiological basis for distinguishing between restricting and binge–purge disorders. The Lock et al. study discussed above (Lock et al., 2011), for example, suggests that, during adolescence, subtypes may be distinguishable in terms of neural correlates of inhibitory control. If this distinction holds, it may also suggest that treatments targeting cognitive processes associated with them is warranted.

Cognitive remediation therapy (CRT) (see Fitzpatrick, 2011, Chapter 13, this volume) may be an important adjunctive treatment to the usual treatment of eating disorders, to address the cognitive processes that may underlie these disorders. Cognitive remediation therapy may be useful for both externalizing and internalizing disorders, as it has been used with schizophrenia (Penades et al., 2005; Wykes et al., 2003), OCD (Buhlman, 2006), and AN (Baldock & Tchanturia, 2007; Davies & Tchanturia, 2005; Park & et al., 2006; Tchanturia, Whitney, & Treasure, 2006; Wykes et al., 2003; Wykes & Reader, 2005; Wykes, Reeder, & Corner, 1999).

In its original application, CRT focused on difficulties in memory, attention, and other aspects of executive functioning (Kurtz, 2003). Cognitive remediation therapy utilizes practice and targeted skill building (Davies & Tchanturia, 2005; Tchanturia, Whitney, & Treasure, 2006). A review of CRT found that it leads to significant improvements in motor dexterity, attention, and verbal memory skills. Evaluating the impact of CRT on brain activity, Wexler et al. (2000) identified positive correlations between performance on verbal working memory and activation in the left inferior frontal lobe. Based on these findings, the authors suggested that CRT strengthens neural circuitry and activation in areas targeted in training. In a study by Wykes and Brammer. (2002), fMRI data supported increases in frontocortical activation in this patient group (Wykes and Brammer., 2002).

Tchanturia and colleagues developed a CRT package focused on the cognitive flexibility and central coherence for eating disorders based on the strategies used on CRT for other disorders. Preliminary studies suggest that this is a feasible and acceptable treatment that also demonstrates change scores on measures of cognitive flexibility and central coherence (Baldock & Tchanturia, 2007; Tchanturia et al., 2006). A refinement of this model has been developed for adolescents that is also being piloted (Tchanturia & Lock, 2010).

Conclusion

Research

As this chapter illustrates, there are few neurobiological studies of adolescents with eating disorders. Nonetheless, the studies that are available suggest that developmental factors related to executive functioning and cognitive processing are likely important in the risk, maintenance, and treatment of eating disorders. A range of neuroimaging strategies to identify biomarkers of neuroanatomical and neurofunctional basis is under way in a number of disorders; this research is helping to guide the genesis of better animal models of human brain disease. (p. 312) Future studies should be conducted to in this area to shed light on possible biomarkers of cognitive processes in younger patients with eating disorders and thereby provide the next logical step in identifying underlying anatomical and functional correlates of the disease in a developmental context.

As suggested by the tentative findings described in this chapter, if anatomical and functional neural correlates are better understood, the etiology of these deficits could be described on a biological level. Such data may provide information about these underlying mechanisms as well as suggest future translational research that targets such processes with psychological, cognitive, or psychopharmacological treatments. Studies examining treatments, such as CRT, targeting the cognitive processes are also under way, and information gathered from these studies could add to our knowledge about how cognitive process may be addressed in the evolution of eating disorders.

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