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Research suggests that working memory has an important role in instrumental learning when reinforcement histories of multiple options must be tracked (Collins et al., 2017; Collins & Frank, 2012, 2018). Working memory capacity not only reflects the ability to maintain items, but also to update and shield items against interference in a context-dependent manner; functions conceivably also essential to instrumental learning in an ever-changing environment. To address the relationship of working memory capacity and the dynamics of instrumental learning in a volatile environment in a previous unpublished study, we studied choice behavior and EEG of healthy young people who performed a probabilistic reversal learning task with sudden changes in reward probability (20, 50, 80%)(Humann et al., 2020). Working memory capacity, as assessed with the automated Operation Span (AOSPAN) task, positively correlated with learning performance. A hybrid Rescorla-Wagner/Pearce-Hall model was implemented to capture dynamic changes in people’s learning rate around changes in reward probability. Low-capacity participants modulated learning rates less dynamically around such changes. Their choices were more stochastic and less guided by learnt values, resulting in less stable performance and higher susceptibility to misleading probabilistic feedback. Single-trial model-based EEG analysis furthermore revealed that reward prediction errors (RPE) and learning rates were less strongly represented in cortical activity of low-capacity participants, while the centroparietal positivity, a general correlate of behavioural adaptation, was independent of working memory capacity. Whether working memory capacity modulated learning performance in this task (causation), or performance on both tasks relied on a common underlying mechanism (correlation), is an open question that we aim to address in this online behavioural study. By experimentally manipulating the load on working memory in the original task, its causal role in learning performance and dynamics can be investigated. Our second aim is to conceptually replicate our previous correlations between working memory capacity and learning performance as well as dynamic learning rate, under the load manipulation that is most similar to the load in the original task.

Obesity may be associated with differences in central dopamine levels, a neurotransmitter important for motivation, cognitive control and decision making (Horstmann, Fenske, & Hankir, 2015). Such differences in the dopaminergic system might explain impaired cognitive processes like reinforcement learning and working memory found in obese compared to normal weight people (Horstmann, 2017; Mathar, Neumann, Villringer, & Horstmann, 2017; Yang, Shields, Guo, & Liu, 2018). However, recent research results from animal studies suggest that not only obesity itself contributes to such changes, but that the consumption of saturated fat and refined sugar (HFS) can independently lead to alterations in the dopaminergic system. Interventional studies in rodents have provided evidence for altered dopaminergic transmission, e.g. downregulated dopamine receptor expression (Adams et al., 2015; Hakim & Keay, 2019), decreased dopamine release (Barnes, Wallace, Jacobowitz, & Fordahl, 2020; Patel et al., 2018), and reduced dopamine clearance (Cone, Chartoff, Potter, Ebner, & Roitman, 2013) after short- or long-term exposure to HFS. Such changes in the dopaminergic system might influence cognitive processes and behavior in humans. In fact, correlational observations provide first evidence for a link between HFS and general cognition. Higher intake of saturated fat and sugar was associated with poorer global cognition (Zhang, Mckeown, Muldoon, & Tang, 2006), cognitive decline (Okereke et al., 2012) or poorer memory (Attuquayefio et al., 2016). The impact of HFS on specifically the human dopaminergic system and possible behavioral effects have not been investigated so far. Our first study, investigating the link between HFS and the dopaminergic system in humans revealed that HFS consumption was associated with elevated peripheral dopamine precursor (pDAP) availability and altered effects of a dietary dopamine depletion on working memory (Hartmann et al., 2019). Availability of pDAP can be regarded as proxy for striatal dopamine levels (Leyton et al., 2004; Montgomery, McTavish, Cowen, & Grasby, 2003), hinting at first evidence for possible HFS-associated differences of the dopaminergic system in humans. Efficient cognitive control, the ability to exert goal-directed choices, is substantial in everyday life. Maintaining mental representations of goal-relevant stimuli in working memory and being able to update these representations when necessary, but also securing them by ignoring irrelevant stimuli is essential for guiding goal-directed choices. Frontostriatal loops have been implicated in governing these types of working memory processes. Flexibly updating task-relevant information has been shown to involve the dorsal striatum (Fallon & Cools, 2014; Fallon, van der Schaaf, ter Huurne, & Cools, 2017), whereas stable maintenance of representations and ignoring irrelevant information seems to rely on the prefrontal cortex (PFC; (Curtis & D’Esposito, 2003; Fallon et al., 2017). Both brain regions receive dopaminergic input from the midbrain; the dorsal striatum is innervated by dopaminergic neurons from the substantia nigra via the nigrostriatal pathway (Prensa, Giménez-Amaya, Parent, Bernácer, & Cebrián, 2009) and the PFC receives dopaminergic signaling from the ventral tegmental area (Westerink, Enrico, Feimann, & De Vries, 1998). Neurocomputational models suggest that D1 and D2 receptor activation in the striatum and the PFC modulate the ability to update or ignore distracting stimuli (Durstewitz & Seamans, 2008; Hazy, Frank, & O’Reilly, 2007; O’Reilly & Frank, 2006). One of our main hypotheses is that, due to changes within the dopaminergic system, HFS consumption is associated with altered working memory updating and ignoring in a delayed match-to-sample task with distractors (adapted from Fallon & Cools, 2014; Fallon et al., 2017). Striatal and prefrontal dopamine transmission, determined by e.g. dopamine levels and dopamine receptor availability, are also depending on genetic variability, which determines the baseline of the dopaminergic system. Among many single nucleotide polymorphisms (SNPs) known to play a role within the dopaminergic system, the Val158Met polymorphism of the catechol-O methyltransferase (COMT) and the Taq1A polymorphism within the ANKK1 gene are fairly well studied, especially in the context of working memory stability/flexibility. Carrying the Val allele of the COMT Val158Met polymorphism results in higher COMT activity and subsequently lower synaptic dopamine levels (Chen et al., 2004). The effect of this polymorphism is supposedly most prominent in the prefrontal areas where dopamine active transporters are sparse (Sesack, Hawrylak, Matus, Guido, & Levey, 1998) and has been related to executive function and working memory in humans (S. J. Fallon, Williams-Gray, Barker, Owen, & Hampshire, 2013; Joober et al., 2002; Xu et al., 2007). Though not directly coding for a protein within the dopaminergic system, the Taq1A polymorphism affects dopamine D2 receptor (DRD2) density in the striatum (Jönsson et al., 1999; Pohjalainen et al., 1998) and is supposed to modulate working memory vicariously (Nymberg et al., 2014). Combined, the COMT and Taq1A polymorphisms seem to influence general working memory and specifically updating of working memory in an interactive manner (Berryhill, Wiener, Stephens, Lohoff, & Coslett, 2013; Garcia-Garcia, Barceló, Clemente, & Escera, 2011; Stelzel, Basten, Montag, Reuter, & Fiebach, 2009). Reuter and colleagues proposed a COMT-DRD2 Equilibrium model where COMT and Taq1A genotype combinations suggesting a balance between prefrontal dopamine and striatal DRD2 density perform differently on tasks of working memory than unbalanced genotype combinations (Garcia-Garcia et al., 2011; Reuter, Schmitz, Corr, & Hennig, 2006; Reuter et al., 2005) We hypothesize that the COMT Val158Met and the Taq1A polymorphism will interact with dietary style (low vs. high fat and sugar) and the presumed associated alterations within the dopaminergic system to modulate working memory updating and ignoring.

This preregistration gives an overview of the background, overarching hypotheses, and design of a large study consisting of several subprojects. Detailed information about the subprojects and the respective analysis plans can be found in separate preregistrations.

The neurotransmitter dopamine plays a central role in several cognitive domains such as motivation, reward-processing, reinforcement learning, decision-making and executive functions including working memory and attention (Arnsten, Wang, & Paspalas, 2012; Logue & Gould, 2013; Rogers, 2011; Schultz, 2002; Volkow, Wise, & Baler, 2017). Results of our recent studies point towards prominent alterations in these domains in overweight and obesity and suggest the existence of a non-linear relationship between markers of obesity and central dopamine tone (Horstmann, Fenske, & Hankir, 2015). Dopamine tone in the brain depends on both genetic and environmental factors. Genetic differences in important genes within the dopaminergic synapse, i.e. COMT (important for dopamine degradation) and dopamine receptor D2 (A1 allele of Taq1A), lead to altered dopamine signaling and can therefore contribute to higher vulnerability for diet-induced obesity (Annerbrink et al., 2008; Epstein et al., 2007). One important environmental factor that is hypothesized to affect dopamine transmission in obesity is an unhealthy diet. Accumulating evidence from animal research suggests that observed differences in dopamine signaling in obesity may be induced by diet rather than obesity (Geiger et al., 2009; Narayanaswami, Thompson, Cassis, Bardo, & Dwoskin, 2013; van de Giessen et al., 2013; Vucetic, Carlin, Totoki, & Reyes, 2012). However, the evidence for diet-induced alterations in dopamine signal transmission and their effects on dopamine-mediated cognitive processes in humans is scarce. In the proposed study, we aim to address the effect of diet high in fat and sugar on dopamine signal transmission in the brain and on dopamine-mediated cognitive performance.

In order to navigate goal-directed behavior in an ever-changing complex environment, a fine balance between maintaining and updating of goal-relevant information is required. A cognitive system crucially involved in these processes is our working memory (WM). While maintenance of WM representations is thought to be implemented by recurrent excitatory networks in the PFC (Seamans et al., 2003, Curtis & D’Esposito, 2003; Fallon et al., 2017), updating is accomplished via fronto-striatal go/no-go pathway modulations (O’Reilly and Frank, 2006, Fallon & Cools, 2014; Fallon, van der Schaaf, ter Huurne, & Cools, 2017). Importantly, neurocomputational models, and fMRI and PET studies have demonstrated the involvement of dopaminergic signaling in the modulation of these two complementary processes (Durstewitz & Seamans, 2008; Hazy, Frank, & O’Reilly, 2007; O’Reilly & Frank, 2006). In line with this, flexible maintenance and updating of WM representation has been associated with certain single-nucleotide polymorphisms (COMT and DRD2), both of which are indicative for frontal and striatal dopamine levels, respectively. (Käenmäki et al., 2010; Syvänen et al., 1997; Hirvonen et al., 2005; Leyton et al., 2004; Montgomery et al., 2003). With respect to the COMT gene, Met allele carriers (who supposingly have high prefrontal DA concentrations) are reported to perform better on tasks that require stable maintenance of working memory representations (Berryhill et al., 2013, Farrell et al., 2012; Savitz, Solms, & Ramesar, 2006). Val carriers (who supposingly have low prefrontal DA concentrations) however, perform better on tasks that mainly probe cognitive flexibility (Cameron et al., 2018; Colzato et al., 2010). With respect to the DRD2/ANKK1-TaqIA polymorphism it has been shown that carriers of the A1 allele have up to 30% reduced D2 receptor density in striatum and outperform non-carriers on measures of cognitive flexibility (Jönsson et al., 1999; Pohjalainen et al., 1998; Stelzel et al., 2009). Importantly, recent studies suggest that these two SNPs act on WM interactively, where there are balanced and unbalanced combinations of genotypes (Gracia-Gracia et al., 2011; Reuter, Schmitz, Corr, & Hennig, 2006). In obese individuals, the accurate adaptation of WM representations seems to be compromised. Behaviorally, obese participants have been shown to perform worse on task that require cognitive adaptation (Steenbergen & Colzato, 2017). Furthermore, obese individuals prefer short-term rewards despite of negative long-term outcome (Horstmann et al., 2011; Mathar et al., 2015) or repeat previously rewarded action, despite of current devaluation (Horstmann et al., 2015b). These findings hence suggest that obese individuals might fail to update the negative consequences of their actions, which could contribute to dysfunctional preservation of maladaptive (eating-) behaviors. Additionally, functional changes in the above mentioned WM-related brain areas have been observed in obesity. Several studies report altered central dopamine levels and dopamine receptor density in striatum (Horstmann, Fenske, and Hankir 2015a, Wang et al., 2001; Dunn et al., 2012; Eisenstein et al., 2013; Kessler et al., 2014; Cosgrove et al., 2015; Horstmann et al., 2015a), as well as changes in grey matter density in PFC in obese individuals (Horstmann et al., 2011; Mathar et al., 2015). While there is general consensus that obesity is associated with alterations in WM functioning (Yang, et al., 2018, Gonzales et al., 2010; Coppin et al., 2014), it remains unclear to what extent and which of the above mentioned WM mechanisms contribute to these impairments. In the present study, we therefore aim to tackle this gap and elucidate on potential impairments in the specific sub processes of WM functioning in obesity. To this end, we implement a task-based fMRI, using a modified version of a delayed match-to-sample task originally designed by Fallon and Cools (2014), which compiles specific conditions that probe updating and maintenance of mental representations. Participants have to remember two target stimuli, signaled by the letter “T” centered between the two stimuli, and match with a probe presented at the end of each trial. The task consists of three task conditions: update, ignore, and maintain. In the update condition participants are first presented with two target stimuli (signaled by the centered “T”). Then they get to see a new pair of target stimuli that displaces the previous target stimuli and has to be matched with the subsequently presented probe. In the ignore condition participants are presented a new set of non-target stimuli (signaled by a centered “N”) after the first set of target stimuli. They have to ignore the non-target stimuli and match the previously presented target stimuli with the subsequent probe. In the maintain condition no distractor stimuli are presented, participants only see one pair of target stimuli that they have to match with the subsequent probe. Instead of the non-target stimuli (maintain condition with matched target-probe delay to the ignore condition) or the first target stimuli (maintain condition with matched target-probe delay to the update condition) a fixation cross is presented. Stimuli and fixation cross remain on the screen for 2000 msec in the first phase of a trial, and 1000 msec in the second phase of a trial. The time of the probe being presented and thus the time for the participant to make a choice is 2000 msec. Feedback whether the choice made at the end of each trial (“Is the present probe one of the two target stimuli?” Yes or No) is not provided. The task is separated in four blocks, with feedback on performance between each block. The three task conditions were equally distributed over all four blocks. Each block consists of 32 trials, amounting to a total of 128 trials. Each trial is separated by a inter-trial interval with a jitter of m 2000 to 6000 msec. The task stimuli are unique, randomly computer-generated, monochromatic RGB “spirographs”. Outcome measures are accuracy and reaction time. The total duration of the task is approximately 30 minutes. At the time of submission, data acquisition has started. All datasets have been collected. The data has not been looked at yet, except for general hypotheses-independent sanity checks (e.g. motion and segmentation checks in the fMRI data, performance above chance checks for the task data)

Working memory (WM) is a mental workspace where information is maintained but also updated in order to flexibly adapt to an ever-changing complex environment. A fine balance between these two functions of WM—maintenance on the one and updating on the other hand – is therefore essential to navigate goal-directed behavior. While fMRI and PET studies have successfully demonstrated the involvement of dopaminergic fronto-striatal loops in these adaptive processes (Fallon et al., 2014; Fallon et al. 2017, Landau et al. (2009), the underlying neural dynamics remain poorly understood. Information gating is also controlled via the balance between excitation and inhibition in the receiver network (e.g., Vogels & Abbott, 2009). As so, persistent maintenance of WM contents in the PFC depends on recurrent excitatory networks and global feedback inhibition (REF). The inherent ratio of ones excitatory and inhibitory connections thus may pose a predisposition to certain malfunctioning of the to-be-balanced processes. However, the exact interplay of excitatory and inhibitory signals to navigate maintenance and selective updating remains elusive (also see Dipoppa, Szwed, and Gutkin, 2016) Long-Range Temporal Correlations (LRTC) in ongoing oscillations during rest have been demonstrated to characterize the inherent balance of inhibitory and excitatory connections within a neural network (Poil et al., 2012) and emerge when neuronal systems operate at a near critical state (Meisel et al., 2017; Palva et al., 2013; Nikulin & Brismar, 2004; Linkenkaer-Hansen et al., 2001). Near critical system dynamics were associated with optimal information processing (Beggs and Plenz, 2004; Shew & Plenz, 2013; Shew et al., 2011) which, when disrupted, will lead to significant loss of function (Poil et al., 2012; Linkenkaer-Hansen et al., 2005; Monto et al., 2007; Montez et al., 2009; Nikulin et al., 2012). Interestingly, LRTC have a high heritable component (Linkenkaer-Hansen et al., 2007) as well as high test-retest reliability (Nikulin & Brismar, 2004), which hence makes them an eligible candidate for conclusions about inherent network characteristics. Furthermore, previous research also suggests that LRTC may play a crucial role in higher brain functions such as attention (Irrmischer et al., 2018) and adaptive decision making (Colosio et al., 2017). And more recent work indicates that higher LRTC can be related to better working memory performance involving phasic adaptive control and moment-to-moment processing (Mahjoory et al. 2018). Taken together, these findings indicate a potential role of resting-state LRTC for adaptive working memory processes. In the proposed study we therefor aim to investigate how resting state LRTC may support maintenance and/or flexible updating of mental representations. To this end, we will record 10 minutes eyes-closed rest EEG. After that participants will conduct a modified version of a delayed match-to-sample task originally designed by Fallon and Cools (2014), which compiles specific conditions that probe updating and maintenance of mental representations. Participants have to remember two target stimuli, signaled by the letter “T” centered between the two stimuli, and match with a probe presented at the end of each trial. The task consists of four task conditions: update, ignore, and a respective control condition for each of these. In the update condition participants are first presented with two target stimuli (signaled by the centered “T”). Then they get to see a new pair of target stimuli that displaces the previous target stimuli and has to be matched with the subsequently presented probe. In the ignore condition participants are presented a new set of non-target stimuli (signaled by a centered “N”) after the first set of target stimuli. They have to ignore these non-target stimuli and match the previously presented target stimuli with the subsequent probe. In the maintain condition no distractor stimuli are presented; participants only see one pair of target stimuli that they have to match with the subsequent probe. Instead of the non-target stimuli (maintain condition with matched target-probe delay to the ignore condition) or the first target stimuli (maintain condition with matched target-probe delay to the update condition) a fixation cross is presented. Stimuli and fixation cross remain on the screen for 2000 msec. The time of the probe being presented and thus the time for the participant to make a choice is 2000 msec. Feedback whether the choice made at the end of each trial (“Is the present probe one of the two target stimuli?” Yes or No) is not provided. The task is separated in four blocks, with feedback on performance between each block. The four task conditions were equally distributed over all four blocks. Each block consists of 32 trials, amounting to a total of 128 trials. Each trial is separated by an inter-trial interval of 2000 msec. The task stimuli are unique, randomly computer-generated, monochromatic RGB “spirographs”. Outcome measures are accuracy and reaction time. The total duration of the task is approximately 30 minutes. To further capture gating processes that take place during this task, we will extract the novelty-P3 ERP component from task EEG. This component has been established as a brain signature of the updating of contextual information (Barcelo et al.,2006; Barcelo et al., 2007; Perianez and Barcelo, 2009) and relates to task switching performance in a dopamine-dependent fashion (Garcia-Garcia et al., 2011). At the time of submission, data acquisition has started. 38 full datasets have been collected. The data has not been looked at yet.

Neuromelanin is a dark pigment, synthesized in the cytosol by iron-dependent oxidation of dopamine and other catecholamines (Sulzer & Zecca, 1999). It is accumulated with age and mainly found in substantia nigra and in locus coeruleus (Zecca et al, 1996) of primates (Marsden, 1961), with the highest concentrations found in humans. Neuromelanin binds iron and other toxic metals, appearing to have a neuroprotective effect (Sulzer et al, 2000). The loss of neuromelanin containing dopaminergic cells in substantia nigra is seen in almost all patients with Parkinson’s disease (Kastner et al, 1992; Fahn & Sulzer, 2004). Cassidy et al, 2019 were able to correlate neuromelanin concentrations as estimated by MRI in humans with dopamine release in the dorsal striatum and with resting-state blood-flow within substantia nigra. Based on these results and since it is being synthesized from dopamine and DOPA derivatives (Sulzer et al, 2000), neuromelanin has been suggested to be an approximation for dopamine (Cassidy et al, 2019). Obesity has been associated with changes in striatal dopamine (e.g., Wang et al, 2001) in humans, and overweight has likewise been associated with changes in dopamine binding and synthesis (Wallace et al., 2014; Lee at al., 2018). Animal models have shown that following a diet high in fat and sugar induce dopaminergic changes similar to those seen in obesity and overweight (e.g., van de Giessen, 2013). Dopamine drives motivated behaviour, executive functions, reward-related learning, and decision-making, and people with altered dopamine functionality show impairments in cognitive domains such as working-memory, association learning, and cognitive flexibility (Kimberg et al., 1997; Mehta et al., 2001; Nieoullon, 2002; Cools et al., 2009). However, how these diet-induced alterations relate to long-term changes in dopamine signalling and to changes in the dopaminergic input regions of the striatum has not been investigated. Using quantitative susceptibility mapping (QSM) it is possible to visualize neuromelanin as a proxy for dopamine in vivo (Cassidy et al, 2019) and using customized MRI sequences, both fMRI and QSM can be obtained simultaneously (Metere et al, 2017). In this study, we will investigate how neuromelanin concentrations are affected by a high fat and high sugar diet.

Dopamine is a neurotransmitter that plays a crucial role in adaptive behavior. A wealth of studies suggests obesity-related alterations in the central dopamine system. The most direct evidence for such differences in humans comes from molecular neuroimaging studies using positron emission tomography (PET) and single-photon emission computed tomography (SPECT). The aim of the current review is to give a comprehensive overview of molecular neuroimaging studies that investigated the relation between BMI or weight status and any dopamine target in the striatal and midbrain regions of the human brain. A structured literature search was performed and a summary of the extracted findings are presented for each of the four available domains: (1) D2/D3 receptors, (2) dopamine release, (3) dopamine synthesis, and (4) dopamine transporters. Recent proposals of a nonlinear relationship between severity of obesity and dopamine imbalances are described while integrating findings within and across domains, after which limitations of the review are discussed. We conclude that despite many observed associations between obesity and substrates of the dopamine system in humans, it is unlikely that obesity can be traced back to a single dopaminergic cause or consequence. For effective personalized prevention and treatment of obesity, it will be crucial to identify possible dopamine (and non-dopamine) profiles and their functional characteristics.