Rapid, automated, and experimenter-free touchscreen testing reveals reciprocal interactions between cognitive flexibility and activity-based anorexia in female rats

  1. Kaixin Huang
  2. Laura K Milton
  3. Harry Dempsey
  4. Stephen J Power
  5. Kyna-Anne Conn
  6. Zane B Andrews
  7. Claire J Foldi

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    This valuable manuscript describes a fully automated touchscreen cognitive testing system for rats that reduces the length of training required to learn a task and eliminates the need for daily handling. These features make it possible to assess cognitive behaviors in conjunction with other neurobehavioral paradigms during adolescence, an important advance in the field. The data convincingly show that cognitive flexibility does not promote susceptibility to severe weight loss in the activity-based anorexia (ABA) paradigm. However, support for the claim that cognitive deficits seen in rats that had been exposed ABA adequately capture an important clinical feature of the pathophysiology of anorexia nervosa is incompletely supported.

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Abstract

Anorexia nervosa has among the highest mortality rates of any psychiatric disorder and is characterized by cognitive inflexibility that persists after weight recovery and contributes to the chronic nature of the condition. What remains unknown is whether cognitive inflexibility predisposes individuals to anorexia nervosa, a question that is difficult to address in human studies. Our previous work using the most well-established animal model of anorexia nervosa, known as activity-based anorexia (ABA) identified a neurobiological link between cognitive inflexibility and susceptibility to pathological weight loss in female rats. However, testing flexible learning prior to exposure to ABA in the same animals has been thus far impossible due to the length of training required and the necessity of daily handling, which can itself influence the development of ABA. Here, we describe experiments that validate and optimize the first fully-automated and experimenter-free touchscreen cognitive testing system for rats and use this novel system to examine the reciprocal links between reversal learning (an assay of cognitive flexibility) and weight loss in the ABA model. First, we show substantially reduced testing time and increased throughput compared to conventional touchscreen testing methods because animals engage in test sessions at their own direction and can complete multiple sessions per day without experimenter involvement. We also show that, contrary to expectations, cognitive inflexibility measured by this reversal learning task does not predispose rats to pathological weight loss in ABA. Instead, rats that were predisposed to weight loss in ABA were more quickly able to learn this reversal task prior to ABA exposure. Intriguingly, we show reciprocal links between ABA exposure and cognitive flexibility, with ABA-exposed (but weight-recovered) rats performing much worse than ABA naïve rats on the reversal learning task, an impairment that did not occur to the same extent in rats exposed to food restriction conditions alone. On the other hand, animals that had been trained on reversal learning were better able to resist weight loss upon subsequent exposure to the ABA model. We also uncovered some stable behavioral differences between ABA susceptible versus resistant rats during touchscreen test sessions using machine learning tools that highlight possible predictors of anorectic phenotypes. These findings shed new light on the relationship between cognitive inflexibility and pathological weight loss and provide targets for future studies using the ABA model to investigate potential novel pharmacotherapies for anorexia nervosa.

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  1. Version published to 10.7554/elife.84961 on eLife
  2. eLife

    Author Response

    Reviewer #1 (Public Review):

    In this manuscript, Huang et al., assess cognitive flexibility in rats trained on an animal model of anorexia nervosa known as activity-based anorexia (ABA). For the first time, they do this in a way that is fully automated and free from experimenter interference, as apparently experimenter interference can affect both the development of ABA as well as the effect on behaviour. They show that animals that are more cognitively flexible (i.e. animals that had received reversal training) were better able to resist weight loss upon exposure to ABA, whereas animals exposed to ABA first show poorer cognitive flexibility (reversal performance).

    Strengths:

    • The development of a fully-automated, experimenter-free behavioural assessment paradigm that is capable of identifying individual rats and therefore tracking their performance.
    • The bidirectional nature of the study - i.e. the fact that animals were tested for cognitive flexibility both before and after exposure to ABA, so that direction of causality could be established.
    • The analyses are rigorous and the sample sizes sufficient.
    • The use of touchscreens increases the translational potential of the findings.

    Weaknesses

    • Some descriptions of methods and results are confusing or insufficiently detailed.

    We have been through all methods and results to include additional details as requested by this reviewer below.

    It seems to me that performance on the pairwise discrimination task cannot be directly (statistically) compared to performance on reversal (as in Figure 4E), as these are tapping into fundamentally different cognitive processes (discrimination versus reversal learning). I think comparing groups on each assessment is valid, however.

    We agree that discrimination and reversal are different cognitive processes, and statistical comparisons between these two components of the task were only made when examining the speed of learning in the validation of the novel testing system. Moreover, our inclusion of the pink and purple bars on graphs such as Figure 4C & 4E represent “main effects of ABA exposure”, regardless of learning phase (PD or reversal) rather than, as you describe, comparing PD to R1. Perhaps this comparison wasn’t clear, so we have amended the text to say ‘main effect of ABA exposure p=.0017’ rather than just “exposure”.

    Not necessarily a 'weakness' but I would have loved to see some assessment of the alterations in neural mechanisms underlying these effects, and/or some different behavioural assessments in addition to those used here. In particular, the authors mention in the discussion that this manipulation can affect cholinergic functioning in the dorsal striatum We (Bradfield et al., Neuron, 2013) and a number of others have now demonstrated that cholinergic dysfunction in the dorsomedial striatum impairs a different kind of reversal learning that based on alterations in outcome identity and thus relies on a different cognitive process (i.e. 'state' rather than 'reward' prediction error). It would be interesting perhaps in the future to see if the ABA manipulation also alters performance on this alternative 'cognitive flexibility' task.

    This is an excellent suggestion and we have already begun exploring this in other ongoing work in the laboratory. Due to ‘compulsive’ wheel running being a hallmark of ABA, we are interested in determining if this also translates to a goal-directed action impairment using the well-established outcome-specific devaluation task. Perhaps with ABA it may be more relevant to investigate outcome-reversals rather than stimulus-reversals, and if this is the case, it would further support the use of the ABA model for investigating cognitive dysfunction relevant to AN. We have included an additional section in the discussion text relating to our hypotheses regarding outcome-specific reversal learning in the ABA model.

    Nevertheless, I certainly think the manuscript provides a solid appraisal of cognitive flexibility using more traditional tasks, and that the authors have achieved their aims. I think the work here will be of importance, certainly to other researchers using the ABA model, but perhaps also of translational importance in the future, as the causal relationship between ABA and cognitive inflexibility is near impossible to establish using human studies, but here evidence points strongly towards this being the case.

    Reviewer #2 (Public Review):

    Huang and colleagues present data from experiments assessing the role of cognitive inflexibility in the vulnerability to weight loss in the activity-based anorexia paradigm in rats. The experiments employ a novel in-home cage touchscreen system. The home cage touch screen system allows reduced testing time and increased throughput compared with the more widely used systems resulting in the ability to assess ABA following testing cognitive flexibility in relatively young female rats. The data demonstrate that, contrary to expectations, cognitive inflexibility does not predispose to greater ABA weight loss, but instead, rats that performed better in the reversal learning task lost more weight in the ABA paradigm. Prior ABA exposure resulted in poorer learning of the task and reversal. An additional experiment demonstrated that rats that had been trained in reversal learning resisted weight loss in the ABA paradigm. The findings are important and are clearly presented. They have implications for anorexia nervosa both in terms of potentially identifying those at risk also in understanding the high rates of relapse.

    Thanks for a great summary of the manuscript.

    Reviewer #3 (Public Review):

    Activity-based anorexia (ABA), which combines access to a running wheel and restricted access to food, is a most common paradigm used to study anorexic behavior in rodents. And yet, the field has been plagued by persistent questions about its validity as a model of anorexia nervosa (AN) in humans. This group's previous studies supported the idea that the ABA paradigm captures cognitive inflexibility seen in AN. Here they describe a fully automated touchscreen cognitive testing system for rats that makes it possible to ask whether cognitive inflexibility predisposes individuals to severe weight loss in the ABA paradigm. They observed that cognitive inflexibility was predictive of resistance to weight loss in the ABA, the opposite of what was predicted. They also reported reciprocal effects of ABA and cognitive testing on subsequent performance in the other paradigm. Prior exposure to the ABA decreased subsequent cognitive performance, while prior exposure to the cognitive task promoted resistance to the ABA. Based on these findings, the authors argue that the ABA model can be used to identify novel therapeutic targets for AN.

    The strength of this manuscript is primarily as a methods paper describing a novel automated cognitive behavioral testing system that obviates the need for experimentalist handling and single housing, which can interfere with behavioral testing, and accelerate learning on the task. Together, these features make it feasible to perform longitudinal studies to ask whether cognitive performance is predictive of behavior in a second paradigm during adolescence, a peak period of vulnerability for many psychiatric disorders. The authors also used machine learning tools to identify specific behaviors during the cognitive task that predicted later susceptibility to the ABA paradigm. While the benefits of this system are clear, the rigor and reproducibility of experiments using this paradigm would be enhanced if the authors provided clear guidelines about which parameters and analyses are most useful. In their absence, the large amount of data generated can promote p-hacking.

    The authors use their automated behavioral testing paradigm to ask whether cognitive inflexibility is a cause or consequence of susceptibility to ABA, an issue that cannot be addressed in AN. They provide compelling evidence that there are reciprocal effects of the two behavioral paradigms, but do not perform the controls needed to evaluate the significance of these observations. For example, the learning task involves sucrose consumption and food restriction, conditions that can independently affect susceptibility to the ABA. Similarly, the ABA paradigm involves exercise and restricted access to food, which can both affect learning.

    In the Discussion, the authors hypothesize that the ABA paradigm produces cognitive inflexibility and argue that uncovering the underlying mechanism can be used to identify new therapeutic targets for AN. The rationale for their claim of translational relevance is undermined by the fact that the biggest effect of the ABA paradigm is seen in the pair discrimination task, and not reversal learning. This pattern does not fit clinical observations in AN.

    In summary, the significance of this manuscript lies in the development of a new system to test cognitive function in rats that can be combined with other paradigms to explore questions of causality. While the authors clearly demonstrate that cognitive flexibility does not promote susceptibility to ABA, the experiments presented do not provide a compelling case that their model captures important features of the pathophysiology of AN.

    We thank the reviewer for this detailed review and note that we have now both explicitly defined the most useful parameters for analyses from the novel touchscreen system as well as removed some comparisons that could be considered superfluous. We argue that the additional information provided by the machine learning analyses are, at this stage, exploratory, and rather than reveal independent descriptions of behavioural change in ABA exposed versus naïve rats this information will aid in the generation of hypotheses to be tested in future studies. Therefore, the figures pertaining to these analyses have now been provided as supplements to Figures 3 & 4 (Figure 3-figure supplement 3; Figure 4-figure supplements 3&4). We have also clarified our intention to explore possible behavioural differences using this technique in the methods and discussion.

    We have also completed the essential control experiment, defined in the “essential revisions” section of this review, whereby we show only moderate impairments in reversal learning following a matched period of food restriction without rapid weight loss, suggesting that the substantial impairment seen following ABA exposure was not due to food restriction alone (see updated Figure 4 and supplements).

    However, we do not agree with this reviewer “that the biggest effect of the ABA paradigm is seen in the pair discrimination task” and point to the outcomes of both reciprocal experiments.

    In the first experiment, rats that went onto be susceptible or resistant to ABA did not differ on pairwise discrimination learning but specifically on performance at the reversal of reward contingencies (Figure 3B & E). Although this result was not in the hypothesised direction, this suggests that reversal learning specifically and not pairwise discrimination can differentiate those rats that go on to be susceptible to weight loss. We have included additional discussion in the text related to this finding (see line 490-497).

    In the second experiment, it is clear by the number of ABA exposed rats that were unable to learn the reversal component even after being able to learn pairwise discrimination, that flexible learning is more impaired by ABA. While it is true that ABA exposed rats that were successful in learning the reversal task were slower to learn the pairwise discrimination component than naïve rats (Figure 4E), this was not related to their ability to learn the reversal task overall – with equivalent learning rates in pairwise discrimination to ABA exposed rats that failed to learn the reversal component (Figure 4G-I). The absence of significant differences between ABA exposed and naïve animals in Figure 4F relates to the fact that the large proportion of ABA exposed animals never reached performance criterion in the reversal phase of the task and therefore data from these animals could not be included in the figure. This is where the trials completed within each session becomes important for interpretation (i.e. Figure 4-figure supplement 1M-O), whereby ABA exposure caused impaired responding specifically within the reversal phase of the task. The results text has been updated to better reflect this critical point.

    Overall, this suggests that the impairment in cognitive flexibility caused by ABA exposure was related both to an associative learning impairment (slower to learn PD than naïve animals) and an impairment in the integration of new and existing learning (failure to learn R1 in a large proportion of animals).

  3. eLife

    eLife assessment

    This valuable manuscript describes a fully automated touchscreen cognitive testing system for rats that reduces the length of training required to learn a task and eliminates the need for daily handling. These features make it possible to assess cognitive behaviors in conjunction with other neurobehavioral paradigms during adolescence, an important advance in the field. The data convincingly show that cognitive flexibility does not promote susceptibility to severe weight loss in the activity-based anorexia (ABA) paradigm. However, support for the claim that cognitive deficits seen in rats that had been exposed ABA adequately capture an important clinical feature of the pathophysiology of anorexia nervosa is incompletely supported.

  4. eLife

    Reviewer #1 (Public Review):

    In this manuscript, Huang et al., assess cognitive flexibility in rats trained on an animal model of anorexia nervosa known as activity-based anorexia (ABA). For the first time, they do this in a way that is fully automated and free from experimenter interference, as apparently experimenter interference can affect both the development of ABA as well as the effect on behaviour. They show that animals that are more cognitively flexible (i.e. animals that had received reversal training) were better able to resist weight loss upon exposure to ABA, whereas animals exposed to ABA first show poorer cognitive flexibility (reversal performance).

    Strengths:
    - The development of a fully-automated, experimenter-free behavioural assessment paradigm that is capable of identifying individual rats and therefore tracking their performance.
    - The bidirectional nature of the study - i.e. the fact that animals were tested for cognitive flexibility both before and after exposure to ABA, so that direction of causality could be established.
    - The analyses are rigorous and the sample sizes sufficient.
    - The use of touchscreens increases the translational potential of the findings.

    Weaknesses
    - Some descriptions of methods and results are confusing or insufficiently detailed.
    - It seems to me that performance on the pairwise discrimination task cannot be directly (statistically) compared to performance on reversal (as in Figure 4E), as these are tapping into fundamentally different cognitive processes (discrimination versus reversal learning). I think comparing groups on each assessment is valid, however.
    - Not necessarily a 'weakness' but I would have loved to see some assessment of the alterations in neural mechanisms underlying these effects, and/or some different behavioural assessments in addition to those used here. In particular, the authors mention in the discussion that this manipulation can affect cholinergic functioning in the dorsal striatum We (Bradfield et al., Neuron, 2013) and a number of others have now demonstrated that cholinergic dysfunction in the dorsomedial striatum impairs a different kind of reversal learning that based on alterations in outcome identity and thus relies on a different cognitive process (i.e. 'state' rather than 'reward' prediction error). It would be interesting perhaps in the future to see if the ABA manipulation also alters performance on this alternative 'cognitive flexibility' task.

    Nevertheless, I certainly think the manuscript provides a solid appraisal of cognitive flexibility using more traditional tasks, and that the authors have achieved their aims. I think the work here will be of importance, certainly to other researchers using the ABA model, but perhaps also of translational importance in the future, as the causal relationship between ABA and cognitive inflexibility is near impossible to establish using human studies, but here evidence points strongly towards this being the case.

  5. eLife

    Reviewer #2 (Public Review):

    Huang and colleagues present data from experiments assessing the role of cognitive inflexibility in the vulnerability to weight loss in the activity-based anorexia paradigm in rats. The experiments employ a novel in-home cage touchscreen system. The home cage touch screen system allows reduced testing time and increased throughput compared with the more widely used systems resulting in the ability to assess ABA following testing cognitive flexibility in relatively young female rats. The data demonstrate that, contrary to expectations, cognitive inflexibility does not predispose to greater ABA weight loss, but instead, rats that performed better in the reversal learning task lost more weight in the ABA paradigm. Prior ABA exposure resulted in poorer learning of the task and reversal. An additional experiment demonstrated that rats that had been trained in reversal learning resisted weight loss in the ABA paradigm. The findings are important and are clearly presented. They have implications for anorexia nervosa both in terms of potentially identifying those at risk also in understanding the high rates of relapse.

  6. eLife

    Reviewer #3 (Public Review):

    Activity-based anorexia (ABA), which combines access to a running wheel and restricted access to food, is a most common paradigm used to study anorexic behavior in rodents. And yet, the field has been plagued by persistent questions about its validity as a model of anorexia nervosa (AN) in humans. This group's previous studies supported the idea that the ABA paradigm captures cognitive inflexibility seen in AN. Here they describe a fully automated touchscreen cognitive testing system for rats that makes it possible to ask whether cognitive inflexibility predisposes individuals to severe weight loss in the ABA paradigm. They observed that cognitive inflexibility was predictive of resistance to weight loss in the ABA, the opposite of what was predicted. They also reported reciprocal effects of ABA and cognitive testing on subsequent performance in the other paradigm. Prior exposure to the ABA decreased subsequent cognitive performance, while prior exposure to the cognitive task promoted resistance to the ABA. Based on these findings, the authors argue that the ABA model can be used to identify novel therapeutic targets for AN.

    The strength of this manuscript is primarily as a methods paper describing a novel automated cognitive behavioral testing system that obviates the need for experimentalist handling and single housing, which can interfere with behavioral testing, and accelerate learning on the task. Together, these features make it feasible to perform longitudinal studies to ask whether cognitive performance is predictive of behavior in a second paradigm during adolescence, a peak period of vulnerability for many psychiatric disorders. The authors also used machine learning tools to identify specific behaviors during the cognitive task that predicted later susceptibility to the ABA paradigm. While the benefits of this system are clear, the rigor and reproducibility of experiments using this paradigm would be enhanced if the authors provided clear guidelines about which parameters and analyses are most useful. In their absence, the large amount of data generated can promote p-hacking.

    The authors use their automated behavioral testing paradigm to ask whether cognitive inflexibility is a cause or consequence of susceptibility to ABA, an issue that cannot be addressed in AN. They provide compelling evidence that there are reciprocal effects of the two behavioral paradigms, but do not perform the controls needed to evaluate the significance of these observations. For example, the learning task involves sucrose consumption and food restriction, conditions that can independently affect susceptibility to the ABA. Similarly, the ABA paradigm involves exercise and restricted access to food, which can both affect learning.

    In the Discussion, the authors hypothesize that the ABA paradigm produces cognitive inflexibility and argue that uncovering the underlying mechanism can be used to identify new therapeutic targets for AN. The rationale for their claim of translational relevance is undermined by the fact that the biggest effect of the ABA paradigm is seen in the pair discrimination task, and not reversal learning. This pattern does not fit clinical observations in AN.

    In summary, the significance of this manuscript lies in the development of a new system to test cognitive function in rats that can be combined with other paradigms to explore questions of causality. While the authors clearly demonstrate that cognitive flexibility does not promote susceptibility to ABA, the experiments presented do not provide a compelling case that their model captures important features of the pathophysiology of AN.

  7. Version published to 10.1101/2022.11.15.516539v1 on bioRxiv