RDoC Reward Sensitivity and Depression: From Mechanism to Implementation
Ryan Beveridge: Okay. Hello, everyone. This is Ryan Beveridge from the University of Delaware, and I'd like to welcome you to the second webinar in our series. This is a series focusing on the science-to-service pipeline in psychology and psychiatry. The series is the result of a collaboration between the Delaware Project, the NIMH RDoC unit, and ABCT, who all share a mutual interest in studying the development and the advancement of science-based treatments in the community. We're really happy that you joined us. Thanks for taking your time to join this conversation with us. I hope the webinar will be as interesting to you as it has been to us putting it together. We've really enjoyed our conversations with all these experts, talking about these issues. We'll be focusing today's webinar on basic science that looks at the brain's reward system, and its potential role in depression, the treatment implications of this work, and dissemination and implementation science that's relevant to getting this work out to the community. There are several objectives for the series, and they all relate to rising to the challenge of developing intervention science that's cohesive and interdependent and impactful for the community.
Ryan Beveridge: Throughout the series, we want to inspire new dialogue that crosses different areas of intervention-related research, and creates innovative new research projects, as well as practice. Consistent with the ideas from RDoC, we'll focus this series on a mechanism-based approach that has the potential to advance our understanding of psychopathology, to inform intervention targets, and to facilitate the implementation of more efficient and person-specific interventions. This effort comes as we recognize the enormous global burden of mental health conditions on society. It's Suicide Prevention Awareness Month, as most of you know, and as one example of the mental health burden on society we're currently experiencing, I'd like to mention these sobering facts about suicide from this year, from the World Health Organization. We know that as deaths from cancer, and heart disease, and-- some forms of cancer, and diabetes are all declining, we haven't been as successful at preventing suicide. So our efforts through this series is to develop and train a more impactful intervention science and practice, and we believe that's of paramount importance. The burden of mental health conditions on society may partially be related to a traditional intervention science process that too often has consisted on unidirectional information flow, from basic laboratory research to intervention development to efficacy trials, and this siloing off of community research from the laboratory may be key in why we see the science-to-service gap that we all seek to narrow.
Ryan Beveridge: After this laboratory work is completed, oftentimes researchers field the dragons that are out in the community, that keeps them from going out and doing research in the community, and we lose internal validity, things like that. It's not viewed as traditionally as clinical science to do effective research, or dissemination and implementation research, and, to borrow a phrase, what happens in the lab stays in the lab too often, and we don't want that to happen anymore, so we're interested in developing crosstalk, and an intervention science division that is integrated in nature. This vision was forwarded at the Delaware Project in this paper by [inaudible] that was published in 2014, and it brings effectiveness research and dissemination implementation research into the fold, so each of these areas are not siloed off, and they can inform one another, so that we create interventions that are based on sound mechanism research, but also effective in community mental health settings, where those who need it most will benefit. We believe in the bidirectional flow between these different areas of research, where one informs the other to create an impactful science.
Ryan Beveridge: This model will be exemplified in our webinar today, where we'll have three distinguished experts crosstalking with one another, and with us, about their area of expertise, and the challenges and opportunities in this area of research and practice. Our three speakers today are Dr. Greg Hajcak, who will discuss the reward system of the brain, and its potential role in psychopathology; Dr. Dana McMakin, who will talk about the treatment implication of this work; and Dr. Mary Beth Connolly Gibbons, who will discuss the relevant findings from dissemination and implementation research. Each expert will take approximately 10 to 15 minutes to present their work, and discuss the translational implications. Then we'll have about 30 minutes for open discussion, where you, the audience, can submit questions to the experts in comments using the question and answer utility that you should see at the bottom of your screen. We have a large and diverse audience today, which is exciting to us, but it includes researchers, clinicians, policy makers, and since our integrative aim is to foster discussion across these disparate interests, we encourage you to ask some of the hard questions that have traditionally contributed to these siloed-off areas of our intervention science and of practice, and we hope that your questions, and the discussion that transpires, will ultimately lead to productive, new advances in intervention science.
Ryan Beveridge: So, with that short intro, I'd like to hand it off to Dr. Greg Hajcak, who will be talking about the reward system in the brain. Greg?
Greg Hajcak: Thanks very much. It's exciting to get to provide a very brief overview of reward neuroscience and its intersection with psychopathology today. I thought I'd start by just talking about general questions about why one might want to focus on reward. I think there's been a lot of basic and translational work on reward. There's a figure here from Nassar's groundbreaking work on the reward system in animals, and that work, and human work that I'll tell you about has led to fairly well-defined neural networks, so we have a good sense of the brain's reward network, which involves midbrain dopamine systems, medial prefrontal cortex, and regions of the basal ganglia. I'll focus particular on the striatum, but from the intersection with clinical phenomenon, I think that one might focus on reward particularly, because reward-related dysfunction has been related to anhedonia, or the inability to experience pleasure. When you think of anhedonia, you might think first about depression, and there's been converging evidence across behavioral FMRI and EEG measures for anhedonic deficits and major depressive disorder, but I think it's really important to point out that anhedonia is evident across many other disorders, including schizophrenia, substance use disorders, neurodegenerative disorders like Alzheimer's and Parkinson's as well, and for developmentally inclined folks, I think that there's a really interesting parallel development of depression and the reward circuit function.
Greg Hajcak: So we know in adolescence that there's a precipitous increase in depression and depressive symptoms, and, similarly, it appears that the reward circuit is undergoing some pretty non-linear changes during adolescence, and linking the focus on reward back to NIMH's research domain criteria, threat and reward are both fundamental motivational systems that have been implicated across many different forms of psychology, and I think what's particularly exciting about reward is that it can be studied in both human and non-human animals across all sorts of units of analysis, and I'll touch on that soon. I'll be focusing in particular on initial responsiveness to reward, which is a subconstruct. I was involved in a proposed restructuring of the positive valance domain, and I raise this to just make the point that I'll be talking about initial response to reward, but that's really only one subconstruct of things like reward responsiveness. And when you think of these constructs, you really want to ask yourself, well, how would I study this, which asks the question about what kind of reward tasks you can use, and I'm just going to highlight a few that are pretty common. There's a probabilistic reward task, which measures behavioral changes based on reward contingencies. This was developed by Diego Pizzagalli, and it assesses probabilistic and reinforcement learning subconstructs of reward. Probably the most neuroimage task out there is the monetary incentive delay task, developed by Brian Knutsen and colleagues, and this task is interesting insofar as it allows you to derive neural and behavioral measures of reward receipt and anticipation, and what I'll be talking about for a few minutes are just really simple gambling tasks, where we can evaluate neural measures in response to receiving reward and loss when outcomes are fully equal probable, and I think these tasks are ideally suited for assessing neural measures of initial response to reward, because there's not much else you can look at.
Greg Hajcak: So just to give you a sense, the gambling paradigm we use is called the doors task. It's incredibly straightforward. On each trial, you see two doors, and you tell each participant that they can win money or lose money on each trial, and they're told to pick a door, and then they receive either a downward-pointing red arrow, indicating that they've lost a quarter, or they receive a upward-pointing green arrow, indicating that they've won 50 cents. The important thing here is that outcomes are equally probable, and it's not unlike going to Las Vegas insofar as when we debrief people, people have various beliefs about how they did - that some did well, some didn't - but they tend to actually enjoy this kind of task, and when we look at the neural response to reward in this kind of simple gambling task, we see all of their regions of the reward circuit become active, from ventral striatum to amygdala, medial prefrontal cortex, orbital frontal cortex, and so this is hearkening back to the first slide, where we were looking at the non-human animal reward circuit, and we see similar things in humans. A lot of my work focuses on event-related brain potentials, which are scalp-recorded measures of neural activity that have excellent temporal resolution, and our work-- here I'm showing just two event-related brain potentials where negative is plotted up, and what you can see is right around 300 milliseconds after feedback, there's this relative positivity when you win money compared to when you lose, and our group and others have referred to this as the reward positivity, and what's nice is in the same subjects, we've shown that the reward positivity and activity in the ventral striatum and the medial prefrontal cortex are correlated when subjects do the task in both the EEG and FMRI.
Greg Hajcak: So there's some evidence that you can get convergent validity and construct validity using multiple neural imaging measures. I'm also particularly excited about studying these initial neural responses to reward, because they have good psychometric properties. So, for instance, we've shown in both adolescents and adults that the reward positivity has good internal reliability, and has pretty decent test-retest reliability as well, and we've also found in a large sample of adolescents that the striatal response to reward has good internal consistency. But I think the most exciting findings, actually, are a series of replicated effects in depression, so, for instance, we and others have found that there's a reduced or blunted reward positivity in relation to depression, and, similarly, you see replicated effects in depression related to a reduced FMRI response to reward, and that's really just a selective overview. I think probably the very most exciting findings are that a number of groups have found that both the reward positivity and striatal response to reward relate to risk, and can predict increases in depression prospectively. So the studies here on the left are ERP studies, and the studies on the right are FMRI studies, and all of these studies have shown that blunted response to reward can prospectively predict increases in depression, or novel major depressive episodes, and I think the vast majority of these actually control for other usual suspects, in terms of risk factors.
Greg Hajcak: Just to give you one data slide, this is from a recent study from our group where we looked at the reward positivity in about 500 adolescent girls who were never depressed at baseline. Of those, 404 did not go on to experience a depressive disorder 18 months later, and on the right are 40 girls who went on to have a first onset depressive episode, and, at baseline, you can see that there was a reduced reward response in the girls who would go on to develop a first onset depressive disorder, and these data were true even when you controlled for other known risk factors, like maternal history of depression, baseline depressive symptoms, and so on. We found these in some other data sets, too, but I just wanted to highlight some relatively large-n studies that show that you can use these measures to predict risk.
Greg Hajcak: I think, despite the excitement, there's tons of room for future opportunities. One of the things I highlighted briefly was the issue of convergent validity. I think this is too rarely evaluated. We tend not to look across different measures, and I think this is one challenge within the RDoC framework about how do you integrate across different levels of analysis? What types of correlations should we expect between behavior, FMRI, and ERP measures? In our hands, those correlations have been relatively weak, but sensible, and I think that one of the other things we've been concerned about is looking at these reward-related measures across tasks. So even some tasks that are superficially similar may not show a ton of overlap in terms of the neural response measures. I think this is an important opportunity for future research. I think that we're at the point where it would be useful to start thinking about demonstrating the utility of large-scale neural assessments. I think, in fact, there are cost-effective measures that we could use in relatively large-scale studies that might be useful in predicting risk, or identifying those at greatest risk for psychopathology.
Greg Hajcak: And, of course, I only highlighted this initial responsiveness to reward element, but there are lots of other aspects or conceptions of reward, and I know Dana McMakin's going to talk about some of this, too, but there's a distinction between liking and wanting, from Berridge and his work, and there's other forms of reward, for instance social reward, and I've only highlighted monetary reward tasks. I think for those of us who are interested in developmental issues, that the timing of assessment and intervention prevention is another future opportunity. So when should we be assessing the reward system? When is it the most important to get an indicator of reward system function?
Greg Hajcak: And then finally I'll end with two kind of future opportunity questions that I think will be a relatively good transition to the next talk. One is, are reward-related neural measures a novel target for existing treatments? We can ask these kinds of questions, and we can ask things like, does behavioral activation potentiate the initial response to reward, or maybe behavioral activation is most appropriate for those who are low in initial response to reward? And I think it also might be useful for thinking about parsing the heterogeneity of psychopathology in novel ways. So I think these are some of one possible sets of questions that we could ask in future work, but I think, for me, a more exciting possibility is that reward-related neural measures might be a novel target for absolutely novel treatments, and instead we could ask the question of what things increase initial response to reward, and then we could chase and define and optimize and design new treatments that do those things.
Greg Hajcak: And so I think I'm going to wrap up there, and turn things over to Dana McMakin, who's going to actually talk more about the intervention science.
Dana McMakin: Okay, so in this next talk we're going to move from that basic science that Dr. Hajcak was discussing about reward sensitivity as it relates to depression to the translation of that science into intervention strategies. So, based on the basic science just reviewed, a number of intervention strategies to target reward sensitivity have been proposed. So first, pharmacologic approaches to depression have long had putative reward targets, including enhancing dopamine signalling, and research is now examining the specificity of those effects, or the opportunities to impact reward sensitivity. Deep brain stimulation involves surgically implanting a neurostimulator in the brain, and a seminal pilot study in 2008 demonstrated that targeting a reward-related region, called the nucleus accumbens in the striatum, it was possible to reduce depressive symptoms in treatment-refractory adults. So there have been some exciting but also mixed outcomes since that time, and this remains a really active area of science. Repetitive transcranial magnetic stimulation is a more non-invasive procedure where we're using magnetic field pulses to stimulate regions of the cortex to try to impact large-scale neural networks. There's increasing attention now to more theory-driven application of TMS to specific networks, including reward-related networks, as well as using FMRI to try to increase the precision and localization of TMS.
Dana McMakin: So those translational approaches that I just reviewed share a goal of directly manipulating the brain, or the brain's neurochemistry, but where we're going to focus our attention today is on translational science that aims to use psychotherapy to alter brain activity and function. This can be a really challenging bridge, because we're moving from relatively specific mechanistic studies in basic science to broad cognitive behavioral and emotional processes that are embedded within real world social complexity. But it's likely a worthy goal, because there's strong evidence across many areas of science that the brain is highly plastic and responsive to environmental inputs, such as those that we can utilize in psychotherapy. Within the psychotherapy literature, the most obvious candidate for targeting reward sensitivity is behavior activation. Behavior activation, BA, aims to increase environmental reinforcement, and it's essentially the B part of CBT for depression. It's also been used as a standalone treatment.
Dana McMakin: But most relevant to our discussion today is a BA strategy referred to as pleasant event planning, where therapists and clients come together, and they try to schedule pleasurable activities to increase environmental reinforcement. So a key question raised at the end of Dr. Hajcak's talk was whether patients with higher symptoms of anhedonia, or altered neural markers of reward sensitivity, might be more responsive to these BA strategies, because we already have these strategies in our treatment arsenal, so if they're more responsive, this could be a way to personalize interventions by using BA. So, there's early evidence, from studies that look at symptoms, that anhedonia is predictive of response to behavior activation, either as part of CBT or as a standalone treatment, and in early pilot studies, there's some evidence that reward sensitivity at baseline, as observed using the FMRI tasks just described, does predict BA outcomes as well. But there's a whole lot more to parse here, for example, whether higher anhedonia and reward sensitivity predicts better or worse outcomes varies by study and varies by approach.
Dana McMakin: A second question also raised by Dr. Hajcak is whether BA leads to treatment-related change in reward sensitivity and anhedonia. Again, we have very few studies that directly address this question, but in general, there's not robust evidence that BA uniquely impacts anhedonia or approach-related behavior, at least per self-report. There have been some signals of change in FMRI studies, however, which is very promising, and it suggests that we may be able to learn something unique from neuroimaging. So that is, maybe we're impacting the target of reward sensitivity, but we need to increase that dose in order to actually see an impact on symptoms. There are a number of limitations here, so, for example, studies that have looked at anhedonia have included suboptimal measures of anhedonia with only a question or two. The early FMRI studies have been critical to advancing our field, but they are mostly with open trials, mostly fewer than 10 patients, using different reward tasks and analytic approaches, and these different approaches have led to different results, across studies and within studies. So, for example, looking at anticipation of reward as opposed to initial response to reward yields different findings.
Dana McMakin: So, we don't really have time to review each study in detail today, but the take-home points I want to make here are the following. Number one, we're getting a signal here that is exciting and worth pursuing, and number two, the nuances of these outcomes are not just a nuisance, but they matter both methodologically as well as conceptually. It may be these very nuances that help us to use neuroscience to move beyond what we already know from symptoms and behavior. So to make progress on that, we really need to use solid theories to guide our design decisions, and we also need to keep in mind our clinical goal, which is to address the need to personalize our interventions, to heterogeneous symptoms and to individuals. So how can translational neuroscience help us to make that kind of clinical progress. First, to address what might work and why, we can turn to neuroscience theory. So an important distinction made my Kent Berridge and others is this idea between liking and wanting, or, liking refers to the consumatory experience of pleasure, and wanting refers to repetitive motivation toward reward. So we often want things we like, and vice versa, but this is not always a one-to-one correspondence, and these two features of reward sensitivity are related but separate. So, for example, dopamine systems are really more tied to wanting, as opposed to liking.
Dana McMakin: Accordingly, recent literature has made a distinction between motivational anhedonia and consummatory anhedonia, and it may be that these are two unique subtypes of anhedonia, or that one of these is more characteristic of depression relative to other disorders. Another area of research gaining momentum is the sustainability of pleasure. So after we experience something rewarding, it doesn't usually end there. We reactivate memories of our rewarding experiences, we stay in pleasure through mental replay, reminiscent savoring, social sharing, et cetera, and again, here we see individual differences, where depressed individuals show a faster fading of pleasure, on the order of minutes as well as on the order of months and years, and Aaron Heller and others have demonstrated that this failure to sustain pleasure is associated with differences in connectivity, and activation in reward-related circuitry. So replaying our positive life moments and sustaining pleasure might seem like a kind of superfluous activity, but it likely plays a key role in how our brain learns about rewards, and stabilizes representations and expectancies that later can be drawn upon to drive our motivation to approach goals, even in the face of obstacles. So, in a sense, perhaps this process is how daily pleasure becomes linked with motivational drives and behavior.
Dana McMakin: So with all this in mind, let's now turn back to BA and pleasant event planning. One of the biggest clinical challenges here is that clients struggle to follow through with plans, commonly saying, "I didn't feel like it," or "I didn't want to." So as therapists, we often help to create behavioral contingencies, and we encourage clients to really engage cognitive control to overrule that amotivation or lack of drive, a just do it mentality. But maybe there are ways to draw on reward sensitivity to increase success here, but before we get there, let's use an example to really demonstrate what this model looks like when we move beyond controlled rat models to human life. Perhaps a healthy teenager, Julissa, we'll call her, decides that joining the swim team is something that may bring her pleasure. So she gets there on the first day, so she approaches the potential reward, but then she consciously or subconsciously engages strategies to increase her pleasure. For example, maybe she notices her weightlessness in the water, she feels the warmth of her muscles as they engage, and she appreciates the social support of her teammates. After swimming, she takes a nice hot shower in the locker room, and reflects on how accomplished she feels, how great it felt to be in the water, maybe she posts something on Snapchat for her friends to see, maybe she tells stories about swimming to her family at dinner and they revel with her. And then, two days later, as she's packing for swim practice, Julissa feels pleasure all over again, because she imagines seeing her friends at practice, feeling weightless in the water, and feeling accomplished again when it's all over. So it's these processes that may help to rev those motivational engines, and tip the likelihood that she will feel like approaching that next swim practice, and then perpetuate a positive behavioral cycle.
Dana McMakin: So a number of scientists, including Michelle Craske, Emily Holmes, and myself, have been working on intervention strategies that may be able to draw on some of these insights from neuroscience. The intervention strategies that are in various stages of testing include, for example, positive attentional bias training, savoring the moment, and learning to be mindful of pleasant experiences as ways to really enhance that liking experience; sustaining pleasure, or reactivating memories via savoring or memory training to help to consolidate positive aspects of experiences; pre-savoring, or activating imagery to anticipate pleasant or successful aspects of an experience. And so there's a long way to go here to validate these strategies, and to isolate their impact on features of reward sensitivity, but the long term hope is that the insights we're gaining from neuroscience and these theories can help us to clarify the types of research designs, tasks, and analytic approaches that are really necessary to make progress.
Dana McMakin: So for whom might these strategies work is another question. It's not yet clear whether these approaches might be best for those at extreme ends of a deficit continuum, or as a way to capitalize on a more intact reward system. So also, can modules be applied to anhedonia across disorders, and, if not, how can neuroscience help us unpack what makes anhedonia different across different disorders? And for comorbid disorders that share aberrant reward function, like depression and substance abuse, might it be beneficial to target these shared features as a priority in treatment? And finally, the when. Neuroscience may be able to help us identify sensitive periods, when the brain is really extra responsive and receptive to certain kinds of environmental inputs. In adolescents, we see a threefold rise in depression rates around puberty, at the same time that there are dramatic, normative changes in reward circuitry, [inaudible] reward sensitivity, and, in fact, a growing literature now suggests that the developmental changes that happen in reward-related circuitry around the onset of puberty may go awry for youth who are at risk for depression, and play a key role in the path of physiology of the disease. Targeted interventions in at-risk youth during this time of development could shape neurodevelopment, and potentially reduce risk. Elizabeth McCauley and colleagues have developed a BA protocol for adolescents, so maybe it will now be possible to look at whether BA has more uniquely powerful effects in early adolescence, relative to other times, and also to look at the impacts on neurodevelopment.
Dana McMakin: And as we continue to learn more about the brain, we may also identify other strategies that capitalize on this developmental period. So overall, we're seeing early signals that interventions that aim to target reward sensitivity are really doing just that. However, there is a very long way to go to validate those science, isolate outcome variables, and do more highly controlled work. Ultimately, the nuances we see may really hold the key to improving targeted treatments and outcomes, while also having the opportunity to deepen our understanding of the disorders themselves. The challenges to this work are not small. There are nearly infinite levels of specificity when it comes to mechanistic targets, reaching all the way down to the molecular level. So what level of precision do we really need in the context of targeted psychotherapy trials? If we go too broad, we might miss the mark on how neuroscience can actually move us beyond what we're already doing, and if we're too specific, we might get so mired up in details that we miss the much-needed opportunity to have a near-term clinical impact for those who are suffering. So we could spend years in the pipeline fiddling with strategies, only to find they're really a mismatch with either client needs or the way treatments are delivered, and I know Dr. Connolly Gibbons is going to be speaking a bit about this in just a moment.
Dana McMakin: So I'll just conclude by saying, as scientists, we can't be experts in everything, and the solution, I think, is to engage interdisciplinary teams, so that the entire pipeline, from basic science to treatment dissemination, is in view, and based on the data that I and others have presented and will present today, I think reward sensitivity is one of the promising focal points for translational research to make progress, and have potential for impact. So, with that, I will turn this over to Dr. Connolly Gibbons.
Connolly Gibbons: Thank you. [silence] Okay, thank you. I'm going to try to build on some of the information that Dr. McMakin and Dr. Hajcak have presented. I'm part of the Center for Psychotherapy Research at the University of Pennsylvania, where we've had a program of research for 15 plus years, where we evaluate evidence-based psychotherapies in the community mental health setting. So I'm going to try to build on that experience to talk about the dissemination and implementation challenges that clinicians might face when trying to use these evidence-based strategies to target reward sensitivity in the community setting. Oh, so once again, I can't move my slides. Give me one moment. So I have three goals today. First, I want to describe to you a trial of behavioral activation to target reward sensitivity that we currently have ongoing in a community mental health setting. Next, based on our program of research over the years, I'd like to explore some of the challenges to implementing evidence-base psychotherapies in these community settings, and finally, I'd like to explore some of the strategies that we think might be helpful to address these challenges when we implement these interventions to target reward sensitivity.
Connolly Gibbons: So let me first tell you briefly about the trial we have ongoing. We're evaluating the effectiveness of behavioral activation for adults with major depressive disorder. It's currently ongoing in the community mental health setting, so the consumers that are part of the project are all consumers who are seeking services for depression at the community center. The clinicians are all employed by the community center, and have volunteered to have additional expert training in behavioral activation. This trial takes an experimental therapeutics approach, so our goal is to confirm that the community patients that have deficits in reward motivation benefit most from behavioral activation in this setting. So to measure this target, we're using effort expenditure for reward task at baseline. Our second goal is to confirm some of the evidence-based change mechanisms in this community setting. So I want to mention that what we're talking today is about developing both novel approaches to address reward sensitivity, but also to adapt existing approaches. So although behavioral activation in this context isn't necessarily novel, I think this approach has great value. Our hope is that we can learn more about which patients in the community are actually going to benefit most from this treatment, and by doing a deeper exploration of the change mechanisms in the community, we might be able to learn how to adapt and even focus this treatment to optimize outcomes.
Connolly Gibbons: So I don't have the results from our current, ongoing trial today, but building on our experiences so far in that trial, as well as our experiences across the last 15 plus years, I want to talk about some of the challenges to implementing some of these evidence-based treatments. The consolidated framework for implementation research covers a vast array of possible factors that might impede implementation success. I'm going to talk today just about two factors that we have found to be most salient in our program of research. So, in the outer setting, I want to speak about the patient needs and resources, and here, I want to focus specifically on the very high patient attrition from services in the community. Second, I want to focus on, within characteristics of individuals involved-- I want to focus on the attitudes of the community clinicians, and how that may influence implementation success. So let's start with the attitudes of clinicians. What we've learned in our training program over the years is that in the community mental health setting, most of the clinicians describe themselves as eclectic in practice, and the majority are very interested in gaining new therapeutic tools. What we've also learned is that most of the clinicians, though, have very strong identities in one therapeutic camp. So as we think about how to develop these interventions to address reward sensitivity, we have to be sensitive to the fact that most of the clinicians may not be interested in completely replacing their therapeutic approach, even if they are interested in gaining new tools. Some of the other problems we've faced is that, despite the motivation on the clinician's part to receive this additional training, most of them find that they're very busy, and they don't have time for the training. Some of them have found that they don't want the additional supervision, and finally, there's very high turnover of employment in the community, so many of the clinicians leave employment before they complete the training.
Connolly Gibbons: So, to give you an example from one of our most recent clinical trials in the community mental health setting, we evaluated two evidence-based psychotherapies for major depression. We recruited and trained 41 community clinicians already employed at the center. They all volunteered, were very motivated to receive additional training in evidence-based treatment, but only 20 of them actually made it through the training and received ratings of confidence in the treatment. So a whopping 40%, once they started, were just too busy to participate. Another 27% didn't want supervision. Some of these clinicians felt that they already had tremendous expertise in a particular modality. Some of them felt that they were already doing some of the interventions that we were presenting. In addition, another 20% left the employment before they completed the training, so this really presents a challenge for taking comprehensive packages to address reward sensitivity into the community mental health setting. So what are some of the strategies we might use? I think that one of the first strategies we have to do is improve the implementation climate of the inner setting. So the first strategy is that we have to improve the compatibility of these new interventions, so that they're acceptable in the community. So in the behavioral activation trial that we have ongoing, we started with a phase where we worked with community clinicians and supervisors to develop the training materials. This can sometimes take the form of developing addendum manuals that are more usable and feasible in the community setting.
Connolly Gibbons: The second thing we have to do is focus on the learning climate. We have to work with the community stakeholders to develop feasible training approaches, and unfortunately, for this item, I don't think we have easy answers at this point. I think it's the collaboration that's going to lead us towards feasible training approaches, but I think more research is needed to understand how we train community clinicians in comprehensive, evidence-based treatments in the time that they have allotted. Another strategy that we think might be helpful is thinking about using clinician feedback systems. So in our behavioral activation trial, we've shown that there are reliable and valid measures of the targets of treatment that are feasible in the community setting. We might consider, can we take some of this measurement information and feed it back to the clinician in a way that might motivate them and educate them to use the treatment? So we've had some experience using electronic feedback systems in the community, where consumers are asked to complete electronic measures of barriers to treatment, symptom targets, mechanisms of change, and we're able to produce automated electronic reports for the clinicians. What we found interesting in our last trial is that the number one way that clinicians use these reports was to sit down with the patient and review the report together, and both clinician and consumer found this very useful, so one wonders whether some kind of reports that could feed back some of the treatment targets we're talking about might help educate the clinician. It might help the dyad work together to focus on these new interventions.
Connolly Gibbons: Another strategy I think we're going to have to consider at this point, as we're developing novel treatments, and as we're adapting existing treatments to address reward sensitivity, is we may consider implementing specific intervention modules. So briefer modules, that are fewer sessions but very focused on interventions, might be more easily integrated into clinicians' usual therapeutic approach, and we now have a number of examples where these modular approaches to treatment are both highly acceptable to community clinicians, and equally effective to full therapeutic packages. I think the focus we're talking about, focusing on treatment targets, provides a formula for us on how do we develop these psychotherapy modules without watering down the treatment that we're talking about? So Dr. Hajcak talked about initial response to reward. Our study is focusing on evaluating motivation for reward, and Dr. McMakin is talking about a range of techniques to address wanting versus liking. So I think if we think about maybe developing briefer modules that are focused on these domains within the broad construct of reward sensitivity, we might be able to improve uptake of these interventions in the real world of treatment delivery.
Connolly Gibbons: The second challenge I want to talk about is the extremely high patient attrition from services in the CMHC setting. This isn't unique to the CMHC setting, so a national database shows that the average number of psychotherapy sessions attended in routine practice in this country is only four. So, to give you an example of what I mean in the community mental health setting, our most recent clinical trial in the community, we recruited 237 consumers who were seeking treatment for depression, and we randomized them to two evidence-based psychotherapies. We had very high attrition that was very similar to what they see in the treatment as usual at these centers. So 27% essentially had no treatment. These are the patients that come for an intake session, and then disappear, or come for only one psychotherapy session and disappear. An additional 29% had what we would consider minimal exposure to the psychotherapy, so these patients received six or fewer sessions.
Connolly Gibbons: So what do we do about this very high attrition when we're trying to take these treatments to target reward sensitivity into the community? One of our ideas is to think very strongly about this attrition problem, so we're developing our treatments to target deficits in reward functioning. Our thought is that we also need to personalize these treatments to improve engagement to begin with, and I think the best path to this is to explore the patient variables that might predict engagement in the treatments to target reward sensitivity. So, to give you an idea, in our previous trial, trauma history turned out to be an extremely important variable. So although we were providing evidence-based psychotherapies that had strong data supporting their mechanisms of chance, the patients that had very high trauma histories were much more likely to drop out early from treatment, and much less likely to complete the treatment. So even as we develop a treatment that might target deficits in reward functioning that might ultimately benefit a patient, we have to think about what variables are going to keep the patients from engaging to begin with.
Connolly Gibbons: And I think the target that we're focused on today is a good example. We're talking about deficits and reward functioning. These are patients that are likely to have poor motivation for reward. This alone might keep them from participating in the psychotherapy that ultimately might provide them with benefit. So I think in our behavioral activation trial, we've built in a broad battery of possible patient measures at baseline, and we're going to explore which of those characteristics are going to predict engagement in the service, and I think as we develop these treatments, then we're going to have to think about what techniques can we build in to engage the patients in treatment before we go on to use the specific interventions to target reward sensitivity.
Connolly Gibbons: So in conclusion, today, I think the interventions that we're talking about to target reward sensitivity do have great potential to improve outcomes in the community. But as we develop novel treatments and adapt existing treatments, we have to take seriously both the clinician attitudes in the community, and also the very high patient attrition from services. We need to work on improving the implementation climate as we bring these treatments into the community, and, at this point, when we're developing them, we have to think about how to adapt the interventions now, so that we can improve uptake in the community. Our panel today has tried to show that an experimental therapeutics approach, that develops and adapts interventions to address what we know about reward-related dysfunction, while also taking into account these real world implementation challenges, may have the best success at addressing this science-to-service gap that we've witnessed. Thank you.
Ryan Beveridge: Okay. Thanks, everyone on the panel. This is a difficult task, as I'm sure you can all imagine, to talk translationally across these different areas of intervention science. So we have a bunch of good questions coming in for the panelists. If you haven't already submitted a question, and you have something you'd like to ask our panel, please go ahead and submit that down the Q and A tool at the bottom of the screen, and we'll ask these questions sort of thematically, and skip around. We probably won't get to all of them, but we'll get to as many as we can. Several people have asked about the role of the reward system in comorbid disorders, so we're focusing on depression and anhedonia in this webinar, but people have asked about the role of the reward system in ADHD and borderline personality disorder and anxiety-- those sorts of things. So I'd like to direct that to our panel. Greg, could you take a stab at that first?
Greg Hajcak: Yeah, sure thing. Can you hear me?
Ryan Beveridge: Yep.
Greg Hajcak: Okay. Yeah, I think it's a great question. I was sort of following along a little with the Q and A, and, I mean, I think that a bunch of things come to mind. I mean, in schizophrenia in particular, it's almost odd that they-- the studies in schizophrenia have actually suggested in terms of things like the reward positivity, that it's completely intact in the face of the fact that every other thing that we have examined in schizophrenia seems abnormal. So, in schizophrenia, where you clearly have evidence of anhedonic deficits, you seem to also have an intact reward positivity in the face of abnormal or blunted brain activity of every other sort. So I think that's a real puzzle, and I think that some suggestions for how that could be come from other work that suggests, for individuals with schizophrenia, it may not be that that initial responsiveness to reward is actually impaired, but maybe it's something like a working memory deficit, to be able to hold that experience on line and to guide future behaviors. In somebody asked about ADHD, and I think that literature's a bit mixed, actually. There's a really nice study from Clay Holroyd's group looking at reward-related processes in ADHD, and it seems like poor control individuals, they kind of decrease in response to reward over time, or ADHD kids seem to increase in reward over time. There's work from the Netherlands. Van Meel's group has shown that kids with ADHD seem more responsive to rewards, which kind of makes some sense. The literature in addiction tends to suggest that one somebody's sort of in the throes of addiction, they're pretty blunted in their response to monetary rewards. Kind of as someone was suggesting in the Q and A that the drug of addiction kind of seems to hijack the reward system, take it over.
Greg Hajcak: Yeah. I think someone else asked about remitted depression, or something like that, and would you see it in state versus trait, and there's a nice study from Anna Weinberg and Stew Shankman showing that remitted anhedonic depressives seem to also show this kind of blunted reward response, so at least there it seems like it's maybe more like a vulnerability marker. Those are the things that come to mind off the cuff. It's complicated, and I think this is why there's really a lot more work to do, kind of cross-diagnostically, large-scale studies need to be done, because typically our studies are designed to compare one small group of individuals with depression to a bunch of healthy controls, and that has kind of limited our ability to really generalize, and understand those kinds of questions.
Ryan Beveridge: Great. Thanks, Greg. That was really helpful. Dana or Mary Beth, anything else on that topic? Any treatment implications you'd like to discuss, or dissemination topics?
Connolly Gibbons: Well, I think one of the treatment implications we've started to think about, as we've started to use these measures to measure deficits in reward functioning, it has opened up our perspective, I think, to start thinking about-- whoops. Am I muted?
Ryan Beveridge: No, I can hear.
Connolly Gibbons: Okay. Thought I saw you. We've started to think about behavioral activation and how it might be applicable to other disorders, so I think once we start to focus on psychotherapies from this target perspective, it does help us think about broader applicability of the treatments. We're very early on, but we are starting to think about and test out some of these measures of reward dysfunctions with different populations, like those with substance use disorders, as well as those with remitted depression and current depression, so that we can build on that, and start thinking about whether behavioral activation might have broader applicability in the community.
Ryan Beveridge: Thanks, Mary Beth. Dana, any comments?
Dana McMakin: Yeah, I would just add-- I'm over here Google searching, because I'm trying to find the name of a treatment that I have come across that, to me, appears to be one of the first steps at looking at a targeted intervention using behavioral activation strategies for comorbid depression and substance abuse, and unfortunately I haven't been rapid enough in my Google searching to remind myself of the name of the developers of that, but I know that it's sort of something that's on people's mind, and on the agenda, and I think one thing we haven't talked about a lot today is by having clear theories and models, and then measurements for outcome, I think one of the nice things is that we can start to do things like try a treatment intervention like behavioral activation for comorbidity, and then look at what the outcome is, as a way to give us feedback about the disorders themselves. That is, if there's a part of this reward sensitivity that's intractable when in the presence of comorbidity, then maybe there's something else we don't understand about that particular disorder, and that could set us on the right track to making more progress with the basic science.
Ryan Beveridge: Great. Thank you. Another question, shifting gears a little bit, is related to training. So one of the main focuses, obviously, of the Delaware Project, was what the implications are for this type of translational work across the different levels of trainees that people work with, whether it's graduate students, or postdocs, or medical students, interns-- what are your thoughts about how we might best train our trainees to think translationally across these different areas of intervention science? Anyone want to take a first shot at that? [crosstalk]
Connolly Gibbons: Well, that's a tough one. I'm speaking, of course, from the perspective of training community clinicians. They're a little different than our trainees in that they all have tremendous clinical experience already, so, again, I think when we're training, one of the things we have to do is be very sensitive to the clinician's own expertise and what they already bring to the therapeutic process. So when I was talking about patients dropping out from treatment-- from the training process, we talked to each of the therapists as they decide I don't want to continue with this expert supervision, and although we get a lot of reasons, some of them even talk about feeling criticized in the supervision, or they're already doing some of the interventions we're talking about. So I think as we develop training programs, we do need to target it to whatever group we're training, and take into account the expertise they already have, and help them think about flexibility. They want to be able to use these techniques flexibly within their own program of practice. So I think we need to train in a way that helps them understand how they can bring these interventions into the therapeutic orientation they're already using.
Ryan Beveridge: Great points. Thanks, Mary Beth. Clinicians training is a huge part of this effort. Greg or Dana, any thoughts on training?
Dana McMakin: Yeah, I think I would just add to that, that the more that our training early clinicians, and at least some version of interdisciplinary approaches and understanding of these disorders, and then talking about modular approaches, and how we might integrate these different strategies to target specific outcomes, I think is important for ultimately being able to engage these types of strategies usefully, in a clinical environment. At the end of the day, we're not necessarily going to get into all of the details of wanting versus liking with each client in our clinical setting, but if we have a deep understanding of how these systems work, it enables us to make good choices about the types of strategies we're using, and then hopefully to choose good measurement outcomes, so we can look at whether or not we're actually impacting what we're trying to impact, which of course is what we always want to include in training, but I think these types of dimensional approaches call for a different type of measurement. So rather than measuring depression broadly, measuring that specific anhedonic target that you're going after.
Greg Hajcak: Yeah, I was just going to-- I mean, just to reiterate what Dana said. I mean, if anything, just to take away the idea that you could want something that you don't like, that you could be highly responsive to receiving rewards and not work for it. I mean, these are sort of counterintuitive possibilities that we actually encounter once you start measuring things, in sort of more specific ways.
Ryan Beveridge: Great. Thanks, everyone. Another interesting question-- Mary Beth, you touched on this, but there's a question coming in that-- when integrating innovation into existing practice, so how do we make decisions about adapting interventions to match the context, versus adapting the context to match the intervention? Clearly both are sometimes needed, and are there guidelines for making those decisions based on more basic research or treatment research? What are your thoughts on that?
Connolly Gibbons: Yeah, it's a great point. I'm not sure I have an easy answer to it. I think, when we work in a community, this is why we work with teams of stakeholders, because I think it does have to go both ways. I think when we're taking these treatments into the community, we have to maintain the interventions that we think are targeting the deficit, that we think is going to be most useful, but we also have to listen to what the clinicians that have tremendous experience have to say. So I think I am open-- and it's one of the reasons we've moved towards this idea of a modular treatment. I think we are open to hearing how they want to deliver the interventions. The modular treatment approach, I think, is right in the middle. It allows us to maintain the target focus that we're interested in, and that we think is going to optimize outcomes, but it also allows us to adapt the intervention to what the clinicians are telling us they want.
Ryan Beveridge: Any other thoughts? Dana? Greg?
Dana McMakin: She said what I would have said, so [laughter]. Better than I would have said it, so good choice starting with Dr. Connolly.
Ryan Beveridge: Great. Let's see here. Let's sort through questions, and if you've seen any that you'd like to take a stab at, go ahead and jump in, panel. One question that has been across multiple audience members is how the RDoC framework sort of fits across these different areas in intervention science, so why-- it's sort of the underlying theme of this webinar series and panel, so how we can use RDoC to conceptualize sort of basic research all the way through dissemination and implementation, which you've all been talking about, but any direct thoughts about that?
Greg Hajcak: I'm not sure how complete-- I don't know if I'm answering exactly your question, Ryan, but there've been a number-- for instance, even in the Q and A, you can see a lot of people saying things about "anhedonia" and I think the point is that whatever we've been calling anhedonia, we're not exactly sure what that really is, and there may be anhedonias, and if you really want to understand anhedonia, you have to do a better job of parsing it. So something that "works" for anhedonia in the past-- I mean, is that-- just to use the language we've been using, is that a treatment for wanting? Is it a treatment for liking? Is it a treatment for increasing anticipatory activity in the face of reward, reward-related motivation, the willingness to work, all of the above-- it's probably not a monolithic construct, even, and so I think that's where the RDoC approach could be particularly helpful, to help us clarify what we seem to believe we understand when we say something like anhedonia.
Ryan Beveridge: Any other thoughts?
Dana McMakin: Well, and not to take us to a different question, but I think it links well with this question that I've seen pop up from both ends, which is, one, is there a temperamental difference that sort of leads to this stable, trait-like anhedonic characteristic, and or when we see anhedonia persisting beyond treatment, does that mean that-- beyond behavior activation treatment, does that mean that the anhedonia's not really part of the depressive symptom profile, and I think it links up a little bit with what Dr. Hajcak is saying, maybe that there are multiple aspects of anhedonia, and that we've just begun to parse this, and RDoC may be able to help us get a better handle on that. But also that it may be that there are aspects of treatment-resistant symptoms that persist, and so it's not that they aren't symptoms, and even if they are temperamental traits, it doesn't mean they can't be changed. Maybe we're just not able to change them yet, or haven't found the right phenotype that matches that particular treatment-resistant population.
Ryan Beveridge: Great, thanks Dana. Mary Beth, were you [inaudible]?
Connolly Gibbons: I think-- yeah, I think I would add-- I think your question is how does this RDoC focus, for me, how does this improve the delivery of services in the community, and here I think it has great potential. So we have these enormous systems of psychotherapy that have been around for a while, that have a lot of interventions within packages, but I think it's this focus on understanding the specific deficits that we're targeting, and the mechanisms of change, that are actually operational in the treatments, that are going to help us focus these treatments, personalize them to individual patients, and even the existing psychotherapies we have, they're not working for everyone. So this idea of moving ahead and personalizing and focusing on specific mechanisms I think is the answer to how we're going to improve outcomes today, and hopefully as we develop the novel treatments, how we're going to improve outcomes in the future.
Ryan Beveridge: Great points. Thanks. We have a question about funding. So we know that funding agencies like to see multiple levels of analysis from an RDoC perspective, in addition to self-report. So someone was wondering how do you suggest that we include behavioral tasks to measure reward responsiveness in the intervention studies taking place in the community, so it's feasible and doesn't seem like sort of an afterthought. Dana, or Mary Beth, any comments there?
Connolly Gibbons: Not necessarily a behavioral task, but we're using the effort, which is a computerized task, so it goes beyond the self-report, and we've found that it's feasible in the community setting. We bring in a laptop into the community, and it's a measure that the patients actually like completing. Again, it brings up this idea of how motivated they are for reward, and it gives us a measure that's beyond their own self-report, so I think measurements like that can be used, but we're just starting to pilot those, so hopefully we'll have more information about that in the future.
Ryan Beveridge: Great. Dana?
Dana McMakin: Yeah, and I think the more that-- in Dr. Hajcak's talk, he talked about the lack of coherence across certain measures, so I think the more of that type of work that we do, the more we can get a handle on maybe there are, for example, self-report measures, or subscales of self-report measures that get us closer to some of these targets and concepts that can be produced by really looking at where the type coherences are and where they are not, and be more specific about what we're measuring, even in a self-report sense. And then in terms of behavioral and neural outcomes, I agree that that's a more challenging task, especially within the context of community work, and I'm not sure I have a great answer for that. I really like Mary Beth's response, and that there are some behavioral tasks that are relatively straightforward that I think we can use, but they are not ample at this point. But certainly monetary reward tasks. There are behavioral components to a lot of those tasks that you could potentially utilize in a larger setting.
Ryan Beveridge: Okay, thanks. Thanks, Dana. A follow-up measurement question actually has been submitted. I'm wondering, Greg, if you have thoughts. The question is, are the balloon analogue risk tasks, the Iowa gambling task, and similar tasks, appropriate or sensitive for assessing reward sensitivity as they're impacted by mood or anxiety disorders?
Greg Hajcak: Yeah, it's a good question. I saw that one pop up, too. I mean, one of the challenging things about, for instance, at least most versions of the balloon analogue risk task are that events are highly infrequent, so the balloon explodes once in a while, but most of the time it doesn't, and so things like event frequency are probably important drivers of most neural measures. There are some variants of the balloon analogue risk task that deal with it. My impression of the Iowa gambling task is that it's more a measure of reward magnitudes, or magnitude sensitivity. For those who are interested, I would suggest-- there was a group that was convened to kind of come up with suggestions for tasks to measure distinct reward constructs through the RDoC initiative, and I think if you Google search RDoC reward, positive valence system, there's a whole report there. Maybe we could make that link a little bit more prominent or something, but a group of us kind of made suggestions for specific tasks to use to assess various subcomponents of the positive valance system, so we could maybe point people to that, or something.
Ryan Beveridge: Great. Thanks, Greg. So now let's switch-- this is why this panel is so valuable, because we can switch back between levels, so we'll go to a more macro level. How do you see the information being implemented at a policy level? So state or federal governments. So how can we use this information about reward sensitivity for patients living in communities that have severe, persistent mental illnesses, but are not pharmacologically compliant, those sorts of questions. You start-- anyone want to talk about that?
Connolly Gibbons: My experience in the community mental health system in Philadelphia is that they're extremely interested in evidence-based psychotherapy, so I think they were already putting a lot of policy in place to bring evidence-based psychotherapies into the community setting, so I would say that that's already moving along. I think the more we publish on this research, and validate these mechanisms, the more the communities are going to be interested in these treatments.
Ryan Beveridge: Great, thanks. Any other thoughts? Okay. Dana, I'm seeing a question that maybe you can comment on. Basically, does the type of behavior matter? So how does the type of behavior affect behavioral activation success in relation to the steps in the flow chart? For example, some behaviors might not lead to as deep levels of pleasure or memories as others.
Dana McMakin: Yeah, I think that's a great question, and I don't think we have a lot of data on this point, and so it's been-- this'll be a more anecdotal response, based on my own experience working with-- I particularly work with adolescents, and this is a really critical issue with adolescents, and I think it highlights it really well, because if you think about the adolescent brain, and adolescent behavior, there's a very, very large focus on social aspects of life, and social-- they call it-- I think Eric Nelson's theory is social reorientation, but basically, adolescents are really interested in social status in many ways, and so if you choose activities that are maybe standard for adults, you may really miss the mark on what's rewarding for, for example, a teenager with depression, and I suspect there are also just individual differences across people, as well. So I think it's a really good point, and I think for the most part, at least what we try to do is really connect the activities that we're planning with clients or patients with things that they find meaningful in their life. So beyond just the moment, and more in terms of motivational goals and pursuits, and how they self-identify as contributing meaningfully to this world, and that does, anecdotally, tend to get us better leverage, but I don't think there are great studies on exactly which types of behaviors would be best to choose. It's a great question.
Ryan Beveridge: Okay, thanks. Any other thoughts from the panel for that question? Okay. Mary Beth, I'm thinking about you with this one, but anyone can chime in. You talked a lot about the importance of engaging clinicians in this type of work, and someone has asked what kinds of feedback loops exist, or could be created, whereby clinicians can inform both basic research - that can be a difficult connection - or intervention development? And Dana and Greg, if you want to think about that as well.
Connolly Gibbons: Well, I think the partnership is the vehicle for that. So for 15 years, starting with a NIMH IP risk grant, we brought together a group of stakeholders, and our stakeholders-- we have a group on an executive committee that includes our researchers, it includes our experts in the individual treatments we're trying to look at, it includes clinicians and consumers in the community, as well as the administrators, and the dialogue goes both ways. And so we'll bring from the research literature the interventions that we think are demonstrating the most success, that we think would be useful for them, and they'll tell us what they think, and what they're looking for. So, for instance, we have a program of research on looking at feedback interventions in the community, and I think a lot of our motivation to look at that came from our community partner, who was very interested in these kind of systems, that could be very efficient and could improve outcomes with less cost. So I think it's this collaborative team that is going to make sure that interventions [inaudible] also important to the community clinicians.
Ryan Beveridge: Thanks, Mary Beth. Dana or Greg, any thoughts on feedback loops for your work? [laughter] It's hard.
Dana McMakin: I mean, yeah, it's really-- it's really challenging. I mean, so I'm working-- anecdotal, again. I'm working both in an academic institution and in a hospital setting, and I'm constantly reminded of the challenges of really doing some of these things within the settings where we're trying to implement them, and I think a great example is within the division where I'm working, within the hospital, there's a big push for group-level treatments, because it's an easier way to bill reliably, that if you have an hour for group treatment, and you have multiple patients who are scheduled to show up, then a no-show or two doesn't really affect your overall practice for the day. So I think it's things like that that you don't see from the academic side, that I think it is so critical to have this constant crosstalk and back-and-forth in order to really deliver something, and bring something to the table that will work in the environment, and I do think the environment varies across location as well, so.
Ryan Beveridge: Great. Thanks, Dana. Question maybe for Greg, or whoever feels up for it, but mental illness, someone comments, the observable behaviors are sometimes the last marker to be seen, so the brain changes may have been happening for many years prior, so how do we become more predictive about changes in the reward system of the brain before we see it reach the observable behavior level?
Greg Hajcak: Yeah, I mean, I think that's a really good question. I saw that one, and, I mean, most of our work is on identification and prediction, so, I mean, that's exactly what we're trying to do, and, I mean, maybe to tie in with the last question, I mean, what we're really hoping to do soon is to connect with schools and so the question is how could you actually get these sort of measures in large scales, and in a cost-effective way, and I think it's actually doable, and so I mean, I think the person's question is exactly right. I mean, these reward-related deficits seem to be evident prior to the onset of depression, so they're giving us unique information about someone's kind of risk status, and so I think what we really need to do is to start thinking about, is it possible to conceptualize reward-related neural measures as something that we should be assessing in large scale, and I think it's an interesting, kind of exciting possibility that the answer is yes, and it's feasible, and cost-effective, and all that kind of thing. I think that another example, I guess, of this is, and this gets at someone's question-- someone asked about pregnancy, and I think that's an incredibly important time, because women-- we know that most depressive episodes, for instance, are preceded by a stressor, and we know that pregnancy and giving birth is an incredible stressor, and that one in six women will develop depression after giving birth. And I think it's a particularly pernicious form of depression, because it's happening at a time when you're supposed to feel very happy and excited, and I think that contrast is particularly potentially hard. And so I do think things like behavioral activation and whatnot, just to answer someone's question, is certainly useful. Increasing social support in and around the postpartum period is important, but just to give an example, we've partnered with an OB-GYN clinic in Tallahassee, the North Florida Women's Care Center, and we have an EEG lab in their OB-GYN clinic, and we're collecting reward-related neural measures from pregnant women, and then we're following them, or trying to validate these measures, as predictors of postpartum depression. So in addition to self-report scale, so we're trying to see can we keep producing data to suggest that these things are important and cost-effective to get.
Ryan Beveridge: Right. Thanks, Greg. Other thoughts? Okay. In reference to a point Dr. McMakin made, a good point on psychotherapy protocols and reward sensitivity, is there literature on combined pharmacological and psychotherapeutical treatments specifically for anhedonia, and what could be alternatives for better measurements of anhedonia in comparison for current or self-report questionnaires?
Dana McMakin: Yeah, so, I'll answer this at two levels, because I've seen a lot of medication questions coming up, pharmacotherapy types of questions, and so I just want to kind of clarify that that's-- since that's not my area of expertise or training, I've sort of shied away from talking a lot about that. I provided that one overview slide. So I know that there are, though, pharmaceutical development trials in progress, and preclinical trials, trying to look at whether or not it's possible to augment existing pharmacotherapy with more dopaminergically targeted interventions, but that's something I can't speak to too much more than that, as it's not my expertise, unfortunately. I can say that-- this is the second part of my answer to that question. In one of the studies that I presented as being predictive of outcome when you look at anhedonia at baseline, it was in the TORDIA treatment-resistant depression trial, and what we were looking at was whether or not baseline symptoms of anhedonia, or any other symptom dimension, predicted outcomes to that trial. And that trial was with treatment-resistant adolescents who were engaged in CBT plus or minus SSRI treatment, and at least in that trial, what we found was that anhedonic symptoms at baseline predicted a poor outcome to CBT plus or minus those pharmacotherapy outcomes, and it didn't really matter, to my recollection, what arm they were in. And that was sort of confusing for us, because we thought that behavior activation should improve symptoms in kids who are coming in with higher anhedonic symptoms. So it's possible that the BA, in that protocol, just wasn't a big enough dose. It wasn't frequently endorsed by clinicians as being used for more than one or two sessions, so it could be a dosing issue, or it could be, as I sort of alluded to in my talk, that once you're at a certain level of anhedonic symptoms, maybe the existing treatments that we have just aren't enough to improve those outcomes, and so I think it's a really important area of science, and hopefully we'll make progress on that.
Ryan Beveridge: Great. Thanks, Dana. Looks like we're about out of time. Maybe one last question that's an RDoC-related question. Is there a push or need to develop common cognitive, behavioral, or imaging assays to tap into different processes in reward processing, and generalize or compare the finding transdiagnostically? Does it take us away from providing personalized treatments? We talked about that a little bit, but any thoughts on that?
Greg Hajcak: My two cents is it's exactly what we would need. I think it's a great question. I think one of the problems out there is that my lab uses the doors task, another lab uses a very similar task, and we're all doing one or two tasks that are kind of related to one another using different measures, and I think it's a real problem, and what would be better is to have kind of, as the question suggests, kind of a standard battery, and then we could have the sample sizes that we would really need to ask questions about what's the underlying structure of anhedonia across multiple measures, because I think that to solve these kind of problems, it's really going to require more of a team science approach. I mean, you're going to need n's of thousands, as opposed to hundreds. And then we can personalize medicine [laughter].
Ryan Beveridge: Great. It looks like we're right at our ending time, so I want to thank you all for joining, and thank the panel for preparing these talks, and talking with all of us about their areas of expertise. It's not an easy task, to talk translationally across these really disparate kind of areas of our field, but we hope we've started a conversation that will be helpful. So look forward to our next series. This webinar, as well as our first webinar, will be posted online, so you'll be able to find it and the slides there, and we can hopefully see you at our next webinar. Thanks, everyone.