The results of this study indicate that the AN and OB individuals show opposite brain reward responses in a taste reward task and using two separate analysis methods. The first analysis, which compared brain response with unexpected reward receipt or omission between groups, suggests that AN individuals have significantly increased brain activation compared with CW in the orbitofrontal cortex when receiving reward unexpectedly, whereas OB have reduced activation in that region compared with CW. The second analysis, which compared known dopamine model neuron response with actual human brain activation to the study task, showed a significantly stronger relationship with the model-derived data in AN vs CW, whereas the OB group response was significantly reduced compared with CW, in the left putamen/anteroventral striatum, right insula, left thalamus, and left and right dorsolateral prefrontal cortex. This suggests that AN may be more and OB less sensitive in dopamine-related pathways compared with CW.
Various studies have investigated reinforcement learning in the context of food intake. Those studies identified brain circuits that involve the ventral striatum, midbrain, insula, orbitofrontal, and anterior cingulate cortex in reward processing (Berridge, 2009
; Small et al, 2001
). The within-group results for unexpected receipt or omission of reward stimuli indicated large areas of activation in those regions, but with different intensities across the study groups.
The group-by-condition analysis finding of an opposite group activation in the lateral orbitofrontal cortex in the unexpected reward-receipt condition is important for several reasons. This area was less responsive to food receipt in OB individuals (Gearhardt et al, 2011
), and it was hypothesized that lower response in that region could be associated with reduced behavior control and suppression of reward response (Boettiger et al, 2007
; Elliott et al, 2000
; Goldstein et al, 2007
). Reduced lateral orbitofrontal cortex activation in OB may then be associated with reduced, and increased activation in AN could be associated with increased impulse control to reward presentation. Thus, increased lateral orbitofrontal cortex activation in AN may be associated with high food-intake control, whereas reduced response in the OB group could be associated with problems controlling eating. The lateral orbitofrontal cortex has also been associated with learning of stimulus reward associations (Noonan et al, 2010
; Tsujimoto et al, 2009
), and high activation in the lateral orbitofrontal cortex might indicate high ability to discriminate rewards in AN, but the opposite in OB individuals.
The computational model regression, that tests how well the in vivo
brain response resembles dopamine neuron activation as it is known from dopamine single-neuron recordings, identified the ventral striatum, cingulate, insular and dorsolateral prefrontal cortex, including Brodmann area (BA) 6 bilaterally and BA 46 on the left, as differing across groups. The parameter estimates indicated that all three groups were significantly different from each other in the left putamen/anteroventral striatum and right insula, as well as the left thalamus, and left and right dorsolateral prefrontal cortex. ANs were greater and OBs were lesser compared with CW. Those regions are part of the taste reward system: gustatory inputs from the tongue, immediately after food contact, and before gut involvement, project via brain stem and thalamus to the primary taste cortex comprised of insula and frontal operculum, and from there to the ventral striatum and amygdala, and subsequently to hypothalamus, midbrain, and prefrontal cortex (Carmichael and Price, 1996
). In this reward circuitry, DA acts as an important learning signal released in response to unexpected stimuli, but it also drives the motivation to approach food and other rewards, called ‘wanting' (Berridge et al, 2010
). The same neural pathways that reinforce those natural appetitive behaviors are also activated in response to addictive drugs (Hyman and Malenka, 2001
). This has led to the hypothesis that prone individuals could get ‘addicted' to food, including increased preference and tolerance, as well as reduction of dysphoria, behaviors that are common in substance-using individuals (Corsica and Pelchat, 2010
; Koob and Le Moal, 2005
). A recent study using the same design as in this study in bulimia nervosa (BN), an eating disorder with repeated compulsive eating of large amounts of food, showed in fact reduced brain response in the temporal difference model-based analysis compared with controls (Frank et al, 2011
). That finding then suggested similarities between BN and reduced brain response in addiction disorders, supporting the above hypothesis. The results of reduced brain response in the OB group also are consistent with this model of reduced brain reward response in an addiction model of food intake (Gearhardt et al, 2011
; Volkow et al, 2008
). Altered brain reward function in underweight AN has not been reported before. A recent study in recovered AN found increased brain response to randomly applied taste stimuli (Cowdrey et al, 2011
), whereas others found reduced brain reward response to repeated sweet taste (Wagner et al, 2008
). The application of repeated and predictable, vs
random and unpredictable taste stimulus receipt most likely accounted for the opposite study results, as unexpected rather than predictable stimulation is related to dopamine activation (Schultz, 2002
). The results of our study, together with Cowdrey et al (2011)
, of heightened brain reward response could be a biomarker of altered brain function in AN, potentially related to brain dopamine. It is unclear whether those alterations are premorbid or develop during the course of illness. The prospective studies in rodents that were exposed to over- or under-consumption of food (Avena et al, 2008
; Carr, 2007
; Johnson and Kenny, 2010
) suggest adaptive dopamine-related changes to food intake, and it is possible that such adaptations also occur in AN and OB. Yet, premorbid traits may predispose to such changes. Carr's study (2007) also indicated that brain reward responses persist after weight recovery, which could indicate that alterations in AN during the underweight state persist long into the recovered state, which could be consistent with Cowdrey et al (2011)
A significance of this study is that it is the first that used specifically a dopamine-related reward paradigm in AN, and used computational model-derived data to identify related brain response. Some other studies investigated the dopamine model-related brain response in psychiatric disorders, such as depression (Kumar et al, 2008
) or schizophrenia (Waltz et al, 2010
), and tended to find reduced brain response in those disorders. Dopamine neuronal reward response can be captured in an algorithm that takes advantage of dopamine neurons responding to unexpected reward stimulus receipt or omission (Schultz, 2002
). That model focused originally on the ventral striatum and midbrain, and has recently been expanded to brain regions such as the amygdala, hypothalamus, and cerebellum that are thought to respond to CS, and may drive or inhibit dopamine activation (Hazy et al, 2010
). The strong regression between brain response and dopamine model data suggests that the fMRI BOLD response can be associated with brain dopamine activation, although we cannot measure dopamine directly with this technique. Furthermore, the results indicate that a network of brain regions is involved in the brain dopamine-related reward response, including the cingulate cortex and various prefrontal cortical areas. This circuitry has recently been highlighted in reward and emotional processing in substance use (Volkow et al, 2011
), and the connectivity of those regions and how they may affect AN food intake will be an important direction for further study.
Previously, recovered AN had increased dopamine D2/3 receptor availability in the ventral striatum (Frank et al, 2005
), but we are not aware of similar studies in an ill group. The dopamine D2 receptor has been associated with brain response to unexpected stimulus omission, whereas the dopamine D1 receptor is thought to mediate response to unexpected reward stimulus receipt (Maia and Frank, 2011
). Thus, altered dopamine receptor function could directly affect brain reward response in AN and OB, but this will require further study using dopamine-specific probes. Research in non-clinical populations suggests this notion. For instance, the dopamine D2 receptor TaqIA A1
gene variant that is associated with reduced receptor density determined food reward response in the brain (Felsted et al, 2010
), and genotype of the dopamine transporter predicted appetite suppression in response to stimulant medication (Davis et al, 2007
Clinically, various dopaminergic drugs have been shown to affect eating and body weight. For instance, stimulants such as methylphenidate or the antidepressant bupropion frequently affect food intake and promote weight loss (Anderson et al, 2002
; Goldfield et al, 2007
). In AN, small studies using the dopamine D2 antagonist haloperidol or the dopamine D2 partial agonist aripiprazole suggested beneficial effects on core symptoms of AN (Cassano et al, 2003
; Trunko et al, 2011
). Importantly, the stimulant amphetamine increased, whereas haloperidol decreased brain response in a human temporal difference model paradigm (Menon et al, 2007
). In summary, dopaminergic drugs affect food intake, which may be related to brain reward function, supporting that dopaminergic pathways may be involved the pathophysiology of AN and OB.
Only one region, the supplemental motor area, distinguished groups for reward expectation, with a lesser response in the AN group compared with CW and OB. That region is associated with planning of complex movements and possibly suggests that the AN individuals may prepare less to the upcoming taste stimulus than CW and OB groups.
The sample size was not large, although 20 participants per cell were usually regarded as providing high reliability (Thirion et al, 2007
). Structural brain abnormalities may contribute to functional alterations, but we did not find significant group differences at a significance level similar to the functional contrast.
It is possible that low or high BMI may be associated with alterations in the cerebral blood flow and fMRI BOLD signal, but little information exists regarding this question. Most studies that have assessed ‘resting' brain activity in AN have used SPECT, and found reduced blood perfusion in frontal, parietal, and temporal cortex (Kuruoglu et al, 1998
; Rastam et al, 2001
). But a recent study found no such abnormalities when a correction for the commonly in AN reduced brain volume was applied (Bailer et al, 2007
), suggesting that baseline blood flow is not reduced. In OB, reduced resting blood flow was found in various frontal cortical brain regions (Willeumier et al, 2011
), but that study did not correct for brain volume. As OB has been associated with reduced brain GM volumes (Gunstad et al, 2008
), reduced regional brain blood flow in OB is not certain, and the found blood-flow reduction may have disappeared if a partial volume correction had been applied, similarly to the results in AN. Furthermore, as blood flow may be rather reduced in both AN and OB, if anything, such alterations would not explain opposite results in this reward model.
Another possible limitation is that somatosensory response in the AN or OB groups were different compared with CW, and that this may have affected brain response and reward-system activation in the group-by-condition analysis. We did not find somatosensory cortical areas different across groups, suggesting that stimulation in the mouth may not have confounded the results. However, to address this concern more directly, we analyzed the positive-prediction error conditions with the Artificial Saliva receipt as control condition, the negative-prediction error condition with No solution as control, and Sucrose expectation with Artificial Saliva expectation as control condition. Those analyses indicated smaller in size, but still highly significant areas of difference across groups that were consistent with the results in the original analysis. This further suggested that our results were not merely an effect of altered oral stimulation.
Comorbid conditions or medication treatment may have confounded the imaging results in AN. We addressed this by comparing AN individuals who were without current mood or anxiety disorder, or medication with CW and OB not on birth control. That comparison indicated similar results compared with the larger three-group comparison for unexpected sucrose receipt or omission, supporting that the findings of the study were not due to medication or comorbid disorder effects. We cannot say whether the results are exclusive to the application of taste or also to non-food reward stimuli, and this will be the focus of future studies. Another potentially confounding factor is the duration of illness on brain function. Both AN and OB groups included subjects that had been ill for up to 20 years, but the exact number of months or years sick vs
partial recovery and relapse are difficult to reliably quantify. In both groups, duration of illness was not related to brain-imaging response, but a prospective study would be better equipped to answer this question. Another limitation is that our results were acquired in groups of ill subjects, and hyper- and hyposensitive reward response could be the state markers of under- and overweight states, whereas it is uncertain whether such reward abnormalities are for instance trait abnormalities in AN. The study by Cowdrey et al (2011)
in recovered AN indicated that hypersensitive response to taste stimuli might be present across different states of AN illness. However, a prospective study will be most informative to tease apart the state-related reward system abnormalities from either traits or neurobiological factors that persist long into weight recovery, and that could be vulnerabilities for relapse.
In summary, this study suggests that AN is associated with heightened, whereas OB is associated with reduced brain reward sensitivity to salient taste stimuli, possibly related to dopamine function. The use of the neurotransmitter model-based tasks and data analysis may have the potential to study those disorders, and could move eating disorders research toward more specific models of altered neurotransmitter system function.