Cognitive flexibility refers to the capacity to modify behavioral choices to meet the demands of a changing environment and is crucial for selecting appropriate responses based on context and circumstance (Diamond, 2013). Impairments in cognitive flexibility are common to a range of psychiatric illnesses including schizophrenia (Floresco et al., 2009; Thai et al., 2019), obsessive-compulsive disorder (Gruner and Pittenger, 2017), and substance use disorders (Smith and Laiks, 2018; Sampedro-Piquero et al., 2019), which are characterized by stereotypical patterns of rigid behaviors that persist despite negative consequences, ultimately impacting decision-making. Individuals with a current or previous diagnosis of anorexia nervosa also exhibit rigid behaviors, especially surrounding illness-relevant stimuli such as feeding and exercise (Tchanturia et al., 2012; Tchanturia et al., 2004; Galimberti et al., 2013; Roberts et al., 2010; Steinglass et al., 2006). While impaired cognitive flexibility is most severe in patients acutely ill with anorexia nervosa and likely contributes to perpetuating the condition (Tchanturia et al., 2012; Roberts et al., 2010), the persistence of inflexible behavior following weight recovery and in unaffected sisters of patients with anorexia nervosa suggests that it is involved in the etiology of the disorder (Roberts et al., 2010; Steinglass et al., 2006; Tenconi et al., 2010). What remains to be determined is whether cognitive inflexibility itself predisposes individuals to develop anorexia nervosa and could be used as a biomarker to predict illness onset or severity in individuals at risk. Moreover, a detailed understanding of the neurobiology underlying an inflexibility that persists after weight recovery in individuals with anorexia nervosa is imperative to develop novel pharmacotherapies that can aid in long-term recovery (Tchanturia et al., 2011; Duriez et al., 2021; Errichiello et al., 2016).

While the premise that cognitive rigidity is a fundamental trait of anorexia nervosa is well-accepted, studies assessing cognitive flexibility in patient populations are prone to inconsistent findings, which are likely amplified by large discrepancies in participant demographics and experimental approaches (Miles et al., 2020). It is also difficult to determine from human studies the neurobiological mechanisms that precede the development of anorexia nervosa that could act as targets for early intervention. The question then arises - how can we assess the neural mechanisms of cognitive flexibility in animal models that adequately capture the clinical presentation in anorexia nervosa patients? Rodent models that incorporate key aetiological features, such as the most well-established animal model of anorexia nervosa known as ABA, have been instrumental in identifying the specific neural circuits that contribute to disordered cognitive functioning (Milton et al., 2021). Additionally, the last decade has witnessed an explosion in the availability of innovative tools including optogenetics (Tye and Deisseroth, 2012), chemogenetics (Roth, 2016), and calcium imaging (Lütcke et al., 2010), to manipulate and record neural activity in freely behaving animals. These approaches give an unprecedented ability to answer questions about the relationship between brain function and behavior relevant to a range of human disorders, including anorexia nervosa. However, the interest in new techniques to modify and record brain function has not been matched with adequate enthusiasm regarding the quantification and analysis of behavioral outputs that are critical for the assessment of these relationships.

With this in mind, the study of cognition and behavior in rodents has benefited in recent years from advances in technology that have increased the translational capacity of rodent models of human pathologies (Keeler and Robbins, 2011; Bussey et al., 2008). A major contribution to improving translation has been the incorporation of touchscreens displaying visual stimuli in rodent test batteries that closely mimic those used for human cognitive testing (Bussey et al., 2008), which improves the standardization and interpretation of data. However, touchscreen testing in rodents has thus far required significant time and experimenter intervention to transfer subjects to and from the testing chamber. Indeed, it is well known that experimenter involvement influences experimental outcomes, particularly so for behavioral studies - including those involving the ABA model, in which the outcomes are known to be influenced by experimenter handling (Carrera et al., 2006). Along with stress from handling, which varies between experimenters and, therefore, differentially impacts upon task performance (Deutsch-Feldman et al., 2015; Sorge et al., 2014; Meijer et al., 2007), manual transfer to test chambers at times that suit the experimenter is insensitive to the current motivational state of the animal. Thus, while the wide adoption of touchscreen cognitive testing has already yielded substantial benefits for behavioral neuroscience, the next frontier lies in the automation of the role of the experimenter in gatekeeping touchscreen access (Rivalan et al., 2017; Winter and Schaefers, 2011).

One approach has been to relocate the operant testing modules to inside the home cage for quantification of complex operant and feeding behaviors (Matikainen-Ankney et al., 2021), or to connect individual operant test chambers to the home cage by way of a short tunnel (Bruinsma et al., 2019) to minimize intervention and provide a higher throughput training-testing framework. However, these both have a requirement for animals to remain socially isolated to ensure that the cognitive performance of each individual can be monitored over time. Considering that social isolation itself can induce cognitive deficits (Bianchi et al., 2006; Hemmer et al., 2019) and depression-like behavior (Ieraci et al., 2016; Fone and Porkess, 2008), this is a huge confound for the assessment of cognition in rodent models. In contrast, appropriate social interaction can enhance neuroplasticity (Liang et al., 2019), emotional and social intelligence (Torquet et al., 2018) and influence performance on complex cognitive tasks (Nagy et al., 2020). Recently, the capacity to monitor and track rodents in social groups has become achievable with radiofrequency identification (RFID) technology (Peleh et al., 2019; Redfern et al., 2017) in combination with gating access to test chambers based on a method of automatic animal sorting (Winter and Schaefers, 2011; Caglayan et al., 2021; Kaupert et al., 2017). The development of a fully automated, experimenter-free method for touchscreen-based cognitive testing in rats has been ongoing since the first prototype was constructed in 2017, allowing the successful adaptation of the trial-unique non-matching to location (TUNL) task in an environment that both eliminates experimenter intervention and allows animals to live in social groups throughout testing (Rivalan et al., 2017). This study demonstrated that the learning rate of self-motivated and undisturbed rats was much faster when experimenter involvement is removed.

The potential to more rapidly test cognition in rodents without experimenter intervention and in social groups opens the door to examine whether cognitive inflexibility predisposes individuals to pathological weight loss in ABA – particularly important because the ABA model develops differently in adult compared to adolescent ages (Beeler and Burghardt, 2021). It also allows us to determine the persistence of inflexibility following weight recovery in ABA rats, in order to use this model to screen novel pharmacotherapeutics for anorexia nervosa. In the present study, we used the automated and experimenter-free touchscreen testing system developed from the prototype mentioned above (and now commercially available from PhenoSys, GmbH) to investigate both of these ideas. This automated approach also enables animals to express a more naturalistic behavioral repertoire, a feature ideally suited to comprehensive interrogation with unbiased machine learning approaches to quantify behavioral profiles. Here, we exploited this union with analysis of uninterrupted video recordings of touchscreen sessions using DeepLabCut (Mathis et al., 2018) and B-SOiD (Hsu and Yttri, 2021) to determine the behavioral drivers of cognitive performance. Moreover, the high-throughput pipeline for video analysis from touchscreen sessions that we have established is available openly and may prove useful for future experiments aimed at identifying the behavioral correlates of cognitive performance in rodent models.

Here, we validate and optimize the use of a novel automated and experimenter-free touchscreen testing platform for rats and demonstrate the application of this system for rapid assessment of cognitive flexibility before and after exposure to ABA. Critically, the rate of learning in the automated system was shown to be five times faster (with approximately 10 times higher throughput) than previously reported with conventional touchscreen testing (Milton et al., 2021). While the full spectrum of possibilities arising from the use of the modular PhenoSys touchscreen system is still being realized, the increased throughput, the requirement for fewer animals, and reduced labor time for experimenters represents a major shift in the way these experiments are conducted and analyzed. Our observation that the number of omitted trials is reduced (i.e. engagement is higher) when touchscreen access was limited to the dark phase is consistent with the well-established increase in activity (Milton et al., 2021) and attention (Bruinsma et al., 2019) that rats exhibit during the dark period. Moreover, the ability to rapidly test cognitive flexibility with the automated touchscreen system allowed us, for the first time, to examine the cognitive profiles of animals prior to exposure to the ABA paradigm while ensuring that rats remained young adults for ABA exposure. In addition, we conducted this assay in socially appropriate groups and without experimenter intervention, increasing the reliability of outcomes and removing potential confounds of handling on subsequent ABA phenotypes (Carrera et al., 2006).

Our previous work demonstrated that activity within a specific neural circuit (extending from the medial prefrontal cortex to the nucleus accumbens shell) links pathological weight loss in ABA with cognitive inflexibility on this reversal learning task (Milton et al., 2021) and suggested that inflexibility might be a biomarker for predicting susceptibility to ABA. The results presented here demonstrate that, contrary to our hypothesis, inflexibility does not predispose animals to the ABA phenotype but instead shows that rats that went on to be resistant to ABA were slower to learn the reversal task (i.e. were less flexible) than ABA-susceptible rats. This raises the intriguing possibility that either inflexibility develops coincident with pathological weight loss in the ABA model or that inflexibility in this instance is protective against ABA-induced weight loss. One finding supporting the latter is that rats that went on to be resistant to ABA were hyper-exploratory in touchscreen testing sessions, evidenced by increased rearing behaviors and decreased time spent inactive during the task. Persistently high exploratory activity may result in more errors during touchscreen test sessions as well as hastened food seeking during the feeding window of ABA. Regarding the former, while we did not examine flexible learning during exposure to ABA conditions, the idea that inflexibility and ABA develop in concert fits with the timing of neural circuit manipulation used in our previous work (Milton et al., 2021). That is, both pathological weight loss and inflexibility were prevented by suppressing the same ‘cognitive control’ neural circuit, but suppression occurred during the development of ABA, not prior to ABA exposure. To this end, future studies should delineate the precise stage during the development of the ABA phenotype where inflexibility becomes apparent, thereby defining a ‘therapeutic window’ in which novel pharmacological treatments could be tested with greater translational relevance.

It remains the case that the precise mechanisms underlying cognitive inflexibility in individuals with anorexia nervosa are not well understood, and while this reversal learning task may not capture the prodromal inflexibility that might play a role in the pathophysiology of anorexia nervosa, it may still provide important clues to direct us toward the neurobiological underpinnings of aberrant reinforcement learning in patients. For example, if reversal learning is defined as the ability to adapt behavior based on negative feedback (Robbins et al., 2012), our demonstration that improved reversal learning is associated with increased susceptibility to ABA fits with the hypothesis that maladaptive behaviors in anorexia nervosa act to alleviate negative affect and are strengthened through heightened negative reinforcement learning (Coniglio et al., 2022). Exposure to ABA conditions, but not food restriction alone, significantly impairs cognitive flexibility, indicating that the excessive exercise component of the ABA model disrupts the integration of new and existing learning that is necessary for performance on the reversal learning task.

The development of paradoxical hyperactivity when restricted food access is imposed, a major hallmark of the ABA phenotype, may reflect a compulsive behavior (Miletta et al., 2020) defined by persistent wheel running (energy output) despite the negative consequences of rapidly declining body weight. And yet the ability to effectively adapt behavior to environmentally imposed change (reversal learning) was improved in rats that demonstrated the highest levels of food restriction-evoked hyperactivity and became susceptible to ABA. This challenges our conceptualization of the so-called ‘compulsive’ wheel running that occurs during ABA and precipitates the rapid weight loss characteristic of the model. Even after decades of experimental use of the ABA model, the causes for this paradoxical hyperactivity remain elusive. A recent study in ABA mice demonstrated that a loss of behavioral flexibility following disrupted cholinergic activity in the dorsal striatum was associated with both facilitated habit formation and increased vulnerability to maladaptive eating (Favier et al., 2020) but neither the accelerated formation of habits, or inflexible behaviors were associated with changes in hyperactivity. Similarly, although compulsive behavior in individuals with anorexia nervosa has been described to develop under more habitual than goal-directed control (Foerde et al., 2021) these associations have been restricted to eating behavior rather than exercise. We hypothesize that excessive exercise in ABA rats (and possibly in individuals with anorexia nervosa) represents not a habitual behavior but rather a form of extreme goal-directed control (Hogarth, 2020).

Compulsions have been defined as the repeated, goal-directed selection of a habit (Bradfleld et al., 2017), and perhaps it is the failure to integrate goal-directed (excessive exercise) and habitual (restrictive feeding) action control that underlies the paradoxical behaviors observed in both human anorexia and the ABA model. Recent evidence suggests that only a subset of patients suffering from anorexia nervosa show enhanced habit formation (Favier et al., 2020), which can be differentiated from a more goal-directed patient subgroup by decreased medial orbitofrontal cortex activation during reward anticipation (Steding et al., 2019). Together, these data highlight the need to directly examine the reciprocal effects of ABA and goal-directed action; specifically focusing on the ability to adapt action selection after outcome-devaluation. Since the cholinergic function of the dorsal striatum is associated with both maladaptive eating in mice (Favier et al., 2020) and the interlacing of new and existing learning (Bradfield et al., 2013), the ability to adapt behavior in response to changing outcome values after the reversal of action-outcome pairings (Matamales et al., 2016) may be of more relevance to ABA than the reversal of stimulus pairings examined in this study. Understanding how goal-directed and habitual control of wheel running might change over the course of ABA exposure could inform modifications to cognitive-behavioral therapy for individuals with anorexia nervosa based on a perspective of eating disorder-relevant goals, particularly in those ~80% of patients that engage in excessive exercise (Davis et al., 1997). Combining the ABA model with cognitive behavioral assays that contrast habitual with goal-directed behavior, including outcome devaluation tasks (Watson et al., 2022; Turner et al., 2022), and probing the involvement of the dorsal striatum and orbitofrontal cortex could aid substantially in this understanding.

Our data also suggest that operant training prior to exposure to ABA also alters the subsequent development of anorectic phenotypes, particularly by reducing the maladaptive wheel running that typifies the ABA model. This effect is likely to relate to the procedural aspects of training (i.e. mild food restriction combined with sucrose rewards) rather than to task performance, considering that rats that went onto be susceptible to ABA acquired the reversal task more quickly than rats that went onto be resistant. It should also be noted that the cohort of rats that were experimentally naïve prior to ABA exposure was surprisingly mostly susceptible to weight loss (21/22 rats), a much higher proportion than is normally seen on a cohort-to-cohort basis. This single ‘resistant’ rat was among the fastest ABA-exposed animals to acquire performance criteria in the reversal task (see Figure 4—figure supplement 2), but was not a significant group outlier. While the independent effects of sucrose consumption and scheduled feeding on subsequent weight loss in ABA are yet to be determined, if ABA indeed develops through a failure to effectively adapt to the change in food availability, then our results support the idea that experience with reinforcement learning tasks (regardless of the speed of task acquisition) improves this adaptive capacity. Interestingly, this aligns with recent evidence that increased cognitive flexibility mediates improvements in eating disorder symptoms in patients with anorexia nervosa (Duriez et al., 2021).

While the identification of a behavioral predictor (or biomarker) for pathological weight loss in ABA remains a challenge, the finding that rats exposed to ABA subsequently showed marked impairments in both discrimination and reversal learning, even after body weight recovery, is entirely in line with the clinical presentation of inflexibility in patients with anorexia nervosa long after body weight recovery (Tchanturia et al., 2012; Friederich and Herzog, 2011; Tchanturia et al., 2004). The finding that exposure to food restriction, resulting in mild weight loss and a non-significant decrease in learning performance, indicates that the severe cognitive impairment seen in ABA-exposed rats was related to the speed and extent of weight loss. Intriguingly, this learning impairment induced by ABA exposure was evident from the first session of each phase of training and even within the first 10 min of initial pairwise discrimination performance (see Figure 4—figure supplement 5). This lends weight to the use of the ABA model as an effective tool with which to probe the biological mechanisms underlying cognitive deficits in anorexia nervosa. Our finding is in contrast to the only other published report of flexible learning after exposure to ABA (Allen et al., 2017), in which reversal learning was impaired at low body weight in ABA rats but ameliorated with weight recovery. Although the reasons for this discrepancy remain unclear, the touchscreen testing system used in the present study differs on multiple procedural levels from the attentional set-shifting task previously used to examine flexible learning, and our results suggest that the visual reversal learning task may be preferable for delineating the lasting effects of ABA exposure on cognitive function. That we observed impairments following ABA not only on flexible updating of operant responses but also initial discrimination learning points to a potential motivational deficit induced by ABA. This is in line with our previous work demonstrating a role for the mesolimbic dopamine circuitry in the development and maintenance of the ABA phenotype (Foldi et al., 2017). Considering that exercise behavior in anorexia nervosa is also linked with dopaminergic activity (Gorrell et al., 2020), future studies should define the time course over which motivation or reward-based deficits arise during ABA and the specific influence of ABA on dopamine signaling in response to reward anticipation and receipt using in vivo fiber photometric recordings paired with detection of dopamine release (using the GRAB_DA sensor; Sun et al., 2018) or dopamine binding (using the dLight sensor Patriarchi et al., 2018).

Not only does the automated touchscreen testing system described here allow us to identify cognitive profiles that more accurately reflect the naturalistic behavior of animals, but the incorporation of behavioral segmentation using machine learning also assisted with reducing experimenter biases that are commonly found with manual behavioral scoring. The application of DeepLabCut and B-SOiD to the prediction of behaviors in the present study has allowed for additional exploration of behaviors that could contribute toward cognitive task performance in rats that will aid in the generation of hypotheses to be tested in future studies. Using these tools also enabled the scoring of very large datasets, such as the 185 hr of footage analyzed here. Incorporating this type of analysis with animal models that mimic specific aspects of human pathologies will take us closer than ever before to the identification of biological predictors of pathological weight loss in ABA that could be used in the early detection of anorexia nervosa in at-risk individuals.

The data generated in this paper can be found at https://doi.org/10.6084/m9.figshare.21539685. A data analysis pipeline for providing the key data per session can be found at https://github.com/Foldi-Lab/PhenoSys-codes, (copy archived at Dempsey et al., 2022a). The codes used to create the pose estimation and behavioural segmentation analysis and figures can also be found at https://github.com/Foldi-Lab/PhenoSys-data, (copy archived at Dempsey et al., 2022b).

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Author details

1. Kaixin Huang

   1. Department of Physiology, Monash University, Clayton, Australia
   2. Monash Biomedicine Discovery Institute, Clayton, Australia

   Contribution

   Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Contributed equally with

   Laura K Milton

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9746-7947

2. Laura K Milton

   1. Department of Physiology, Monash University, Clayton, Australia
   2. Monash Biomedicine Discovery Institute, Clayton, Australia

   Contribution

   Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Kaixin Huang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0007-1664-8337

3. Harry Dempsey

   Department of Physiology, Monash University, Clayton, Australia

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5117-6995

4. Stephen J Power

   Department of Physiology, Monash University, Clayton, Australia

   Contribution

   Data curation, Investigation, Writing – original draft

   Competing interests

   No competing interests declared

5. Kyna-Anne Conn

   1. Department of Physiology, Monash University, Clayton, Australia
   2. Monash Biomedicine Discovery Institute, Clayton, Australia

   Contribution

   Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2244-7885

6. Zane B Andrews

   1. Department of Physiology, Monash University, Clayton, Australia
   2. Monash Biomedicine Discovery Institute, Clayton, Australia

   Contribution

   Supervision, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9097-7944

7. Claire J Foldi

   1. Department of Physiology, Monash University, Clayton, Australia
   2. Monash Biomedicine Discovery Institute, Clayton, Australia

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   claire.foldi@monash.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3293-8242

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