Blowing out budget or metrics that look great till the CFO sees them. That's bull spend. And marketers are calling it out in... Dashboard Confessions! I remember telling my boss, it'll be good for the brand when leads were slow. Yeah, it wasn't. Cut the bull spend. LinkedIn lets you target by company, job title and more. Advertise on LinkedIn. Spend 200 pounds on your first campaign and get a 200 pound credit. Go to linkedin.com slash lead. Terms and conditions apply. For many people seeking mental health care, finding the right treatment can be a slow, frustrating process. Clinicians may have to try several different medications and therapies over months or even years until, through trial and error, they land on something that works. But what if that could work differently? What if clinicians could look at a person's symptoms, daily patterns and brain activity and pinpoint from the beginning which treatments are most likely to help? Today we're going to talk to two researchers working toward that goal in the emerging field of precision or personalized mental health care. So what exactly is precision mental health care? How might tools like digital assessments and brain scans help match people with the right therapy? What mental health conditions are researchers looking at? What data privacy and other issues do these new methods raise? And how could they change the experience of patients and clinicians going forward? Welcome to Speaking of Psychology, the flagship podcast of the American Psychological Association that examines the links between psychological science and everyday life. I'm Kim Mills. I have two guests today. First is Dr. Zachary Cohen, an assistant professor of psychology and director of the Personalized Treatment Lab at the University of Arizona. His research uses data-driven approaches to answer the questions, how and for whom do treatments work? His published dozens of peer-reviewed research papers and was named a rising star by the Association for Psychological Science in 2022. His work seeks to improve mental health treatment, accessibility and outcomes, particularly for underserved populations. Next is Dr. Leigh Ann Williams, a professor of psychiatry and behavioral sciences at Stanford University and the founding director of Stanford Center for Precision Mental Health and Wellness. She's internationally known for her work using neuroimaging and computational approaches to identify brain circuit biotypes of depression and anxiety. These are patterns that could help guide more targeted treatment. Dr. Williams has authored numerous scientific articles and led major interdisciplinary initiatives to integrate neuroscience with mental health care with the goal of creating more personalized and effective treatment pathways. Dr. Williams, Dr. Cohen, thank you for joining me today. It's a great pleasure to be here, Kim. Thank you for having us. Let's start as we often do on this podcast with a definition. I talked briefly in the introduction about precision mental health care, but I'd like to hear how you define it in your research. How does it differ from the way most mental health conditions are diagnosed and treated right now? Precision mental health really comes down to understanding why someone's struggling, so not just about what they're feeling. When we're thinking about precision mental health informed by what we know about the brain from neuroscience, we're really aiming to understand the root cause of someone's experience symptoms. And the second piece of precision is that we're using some form of objective test that's to get at the root cause and then to guide more specific treatment choices. And in my case, I'm focusing on brain imaging to get at that root cause and measure it precisely so we can personalize treatments, but there are certainly other ways to go about this as well. People are often feeling a lot of frustration because despite the available personalization and certainly very dedicated clinicians and therapists, it can end up being a trial and error cycle of trying one treatment, waiting a few months and trying another. And so one of the goals of being able to use objective measures and data is to really get answers much sooner and expedite this process of finding the right treatment for the right person. What are the technological advances that have made this possible? I mean, what are you looking at when you're really trying to pinpoint this type of treatment? In my case, one of the huge advances has been the twin development of very sophisticated abilities to deal with a lot of data and specifically advanced ways of imaging the brain directly, which means we can use functional magnetic resonance imaging, which looks at how the brain is functioning, how it's using its fuel, how it's using its oxygen to function. And we can track these very important ways that the brain functions for managing our emotions or feeding our emotions, our thoughts and so on. And a big advances has been mapping the human brain and these functional networks. They're like super highways of the brain. And they govern the very important functions that get disrupted in conditions like depression or anxiety or post-traumatic stress. And now we have the capacity to have a window into how those circuits and networks are functioning in real time for someone experiencing depression, for example, and really pinpoint where is the disruption occurring that's driving their experience of depression. And that's something that has been advancing over the past decade, but in the last few years, I've been very fortunate to be part of developing a process and a method by which we can take that information and quantify and interpret it for each individual so we can identify their particular pattern of disruption. And that's what I call biotypes in my work. I've followed Dr. Williams' work for a long time and admired it. And it feels like science fiction. It's this really advanced and creative approach to understanding the brain. The advances that have allowed my work to start to answer these questions are a lot less complicated in some ways. When I got into this field over a decade ago, I went to oncologists to ask how had they made progress in the treatment of cancer? And the example or the explanation they gave was that in child cancer, everyone who was being treated was being treated at an academic institution or a center of excellence where they were on the same protocol so they could aggregate the data and learn from everyone. And the problem in mental health research was that we had these small trials. There was no coordination of measurement. And we really just didn't have enough data to be able to build the predictive models that would tell us who's going to respond to which treatment. And the big advance or one of the big advances that we've had has, as Dr. Williams mentioned, been the ability to aggregate data. So we've collected the data from over 60 randomized control trials looking at depression treatment outcomes. We're also able to aggregate data from smartphones and other assessment approaches that allow us to measure people at scale. And by getting more data, we're finally able to start uncovering these patterns of response. Let me ask you, Dr. Williams, you mentioned using FMRI. Now, if I go to my psychotherapist because I'm depressed, here she doesn't have access to an FMRI machine. So how does an individual clinician apply what you are learning? I love that question, Kim. It's something I think about almost every day. And part of my work is very much focused on how we build the connection that you're talking about. So your therapist, my therapist, anyone's therapist would have a way of referring for a scan and making that possible. And making it widely available. And I think of a model like migraine screening, which we could think of migraines. They're about 10% of the population or affecting about 10% of the population. And there's the availability of getting an MRI scan for migraines, simile epilepsy, other types of conditions. So we've been building the workflow, which clearly doesn't exist right now, at least not beyond a couple of clinics that we've been fortunate to set up a system for referral in. But working on those workflows, so they could be built in in a way that's obviously integrated, reimbossable, practical, all of the aspects that would be needed. So we have this system, we call it the Stanford etsy re-image processing system, and it's designed to be implemented in a clinical workflow, not just in academic centers, but in the community as well. And so very excited to make that happen. I was at a meeting earlier this week in DC where ARPA-H, the Advanced Research Project and Health Administration, was kicking off a $100 million precision treatment initiative. And there were companies there that were presenting mailable disposable EEG that they can send in the mail. The patient puts it on their head with easily administered gel stickies, and it can be uploaded via a smartphone. And so I think although fMRI is not yet there, it's possible that in the future these things will be more accessible. Right. Well, that's really sci-fi. It is. It is happening, though. So many of us are walking around with smartphones and smart watches and aura rings and things like that, so that we're collecting all of this information. Is there some way of getting that, say, to your therapist or medical provider so that some of these markers can be spotted? I think that's the goal. What needs to happen before that can be useful is we need to establish what the actual value of that information is. We need to develop the actual biomarkers, or we sometimes call these things digital phenotypes. The things that tell us, okay, what does that information actually mean, and how can we make sure that it's reliable, that it's generalizable, that it's not biased towards certain populations? So once we've done that work, I think that's a really useful pipeline that you're imagining. Now, what exactly is a digital phenotype? I know you're doing a lot of work in that area, Dr. Cohen. What does that mean? How would I know what my digital phenotype is? Can I find that out? Yeah. So we think about these important markers or pieces of information that we might be able to capture via passive signals, for example, from your smartphone or wearable device. So your wearable device has an accelerometer. It also often has a heart rate monitor and can collect these things that are very sort of fine grained, right? They're not telling you what your sleep is, but we can use that information to then say, okay, well, when somebody isn't moving a lot and no light is being sensed and there's no sound, we are pretty confident that they're asleep. And so then we can develop a digital phenotype of somebody who is a healthy sleeper, somebody who is having primary insomnia, where they're having difficulty falling asleep, or terminal insomnia, where they're having difficulty waking up. And so those digital phenotypes are how we take the raw information from these different devices and capture meaningful constructs that are then related to health outcomes or that can predict treatment response. We're going to take a short break. When we return, I'll talk more with Dr. Williams about the six biotypes of depression and anxiety that she's identified. So Dr. Williams, I want to ask you, you mentioned earlier biotypes. I know that a lot of your work is focused on that, and you're developing biotypes for depression and anxiety. How does this research work? What is it that you have found? Yes, we found six biotypes that would account for 90% of the variability that you see underlying that broad category of depression and anxiety or broad categories. So let's focus on depression. Very rarely do people share the symptoms of depression. They might have a couple in common, but there's a lot of different individual experiences in how their depression is manifested for them. And so underneath that, what we see in the brain is extraordinary variability in what is the underlying root cause of the types of symptoms being experienced. When we look at these brain circuits that I was talking about before, there's at least six of them that are strongly implicated in depression and in the way that treatments work. And so if we measure those six different circuits, we can actually identify six different types of depression. And this is a little like, if we think of analogy, it's a little like thinking of a broader category like breast cancer in cancer medicine. But once you image the tumor or you get the genetic information, you can identify a subtype or within that type of cancer. So we're doing that with biotypes. And we could say, for example, you might have one circuit that's disrupted, one that's called negative affect. It is a part of the brain that responds to your negative emotions, like sadness, it responds to potential threats. If it gets switched on and stays switched on for too long, people might have the experience of being very acutely, almost a sense of panic, a more trauma type of depression. Whereas another type of depression would involve a different part of the brain. And you can see people who describe having cognitive fog, and it'll be a different frontal part of the brain. And crucially, those those different biotypes that we've identified are very, very specific in how they can predict what treatment you're likely to respond to. And those are medications, behavior therapy, techniques we call neuromodulation, like transcranial magnetic stimulation. And some of these newer treatments we hear talked about like ketamine or even psilocybin. They give us a very good indication of who will do well on those different types of treatments. And that's the way we can expedite the treatment choice. In our studies, we found if we use those biotypes to select treatment, we can actually get close to doubling their chances of getting better, each person's chances of getting better. And is this broadly understood among practitioners that depression is not like a single illness? There are different manifestations, as you've just said. I mean, if I go again, and I'm talking for our listeners who maybe are being treated for depression, perhaps their therapist has really no idea that these biotypes exist. So how do you get the right treatment other than you just keep trying different medications, you just keep trying different therapies? There's certainly a consensus that depression is very varied. In current practice, we do typically use the broader category, you know, the broader diagnostic category of major depression or generalized anxiety inside of our diagnostic system, which is called the Diagnostic and Statistical Manual, DSM. There are some subcategories or specifiers that would say maybe it's this type of depression based on the symptoms. However, right now, the way that is practiced, the broader category or the specifiers don't directly determine what type of treatment might be selected. So for most people, it's that broader category that's used. And it does act as a form of a one size fits all, really, even though each clinician is clearly trying to understand each person's individual experience, but it does act more like a one size fits all. And that's what's leading in many cases to that trial and error cycle. Yeah. Dr. Kahn, let me ask you, could these methods be used for prevention as well as treatments? I mean, in other words, can could they help identify people who are at risk for different mental health conditions and maybe problems could be headed off before they become severe? Yeah, we know depression is highly recurrent and that an individual's likelihood of experiencing relapse increases with the number of episodes they have. So it's incredibly important to try and prevent the onset of depression. And exactly as you're describing, if we can identify either who is at risk for developing depression, or if we can detect it when it's in its early phases before they're really impaired before the symptoms get really bad, we can both reduce suffering and also be more efficient. And so we're actually working on that right now. Our colleague Eiko Fried ran a large study in which he followed over 2000 people without depression for a year and collected all of this rich data, including wearable information, and built an algorithm that could predict who was at risk. And now what we're going to try and do is use that algorithm to say, can we intervene prior to the development in a way that is targeted? And if we can do that, hopefully we can really kind of make a big difference in the global burden of depression by stopping it before it starts. Now both of you have focused a lot of your work on depression and anxiety, but are there other mental health conditions that this type of precision care could be helpful for? Absolutely. In my case, when I first was starting doing research, I focused on psychosis, particularly early onset of psychosis. And I have collaborators focusing on attention deficit disorders and PTSD. There's certainly been many very exciting initiatives in those areas. And I'm sure many others. As a field, it's been motivating to see how this precision approach is really becoming as close to mainstream as I've seen in a major advance in our field. So I see it happening across areas and maybe Dr. Cohen knows of others to highlight. Oh, I totally agree. And I think a related implication or manifestation of this is that we're also thinking about trans diagnostic processes. And what I mean by that is something like rumination or worry. Those are things that occur in depression, but also in anxiety disorders. And so we're also thinking about how can we develop precision or targeted or personalized approaches to help address rumination, whether it's in the context of depression or anxiety. And because we know that comorbidity is really the rule rather than the exception, meaning that people usually don't show up in the clinic with only depression or only panic disorder. Most people are actually going to show up meeting criteria for more than one disorder to have approaches that can help guide treatment in the context of comorbidity. And often those are approaches that are going to think about trans diagnostic processes is really important. One of the reasons I focused on depression and made that choice to make the move is for two reasons. One is much earlier than we expected, depression became the number one leading cause of disability in the world. And that to me is just, it's both staggering and unacceptable how many people's lives are being impacted. So it's really a big public need, health need, meaning people's quality of life and literally the number of years they're getting lost that are being lost to having their function disrupted and shortening of their actual years of life. And related to that is the fact that there are a lot of effective treatments, but we do not know for whom to target, who should we target them to. The other quick thought is that we do know and increasingly have evidence for this that conditions like depression are, have a bi-directional association with other medical conditions and actors, risk factors. So risk factors for other chronic conditions, cardiac conditions, some neurological conditions. So really finding better solutions for depression, anxiety, other disorders will have a very widespread effect on improving health. So what do you both see as the biggest challenges in getting this research that you're doing out of the lab and into clinicians offices? One of the things that I became frustrated by in relation to my own work was that if we develop these treatment selection models and we show that they're reliable and powerful and even if we're able to show that they can generalize to new populations, which is a much higher bar than is typically applied, the ability of the systems that exist to use that information is often shockingly limited. So if I have a model that says somebody is going to respond better to cognitive behavioral therapy versus interpersonal therapy, you need to have a system in which both of those interventions are available and a single clinician usually is only going to be comfortable treating one modality. If I'm in the care of a psychiatrist and the model says you'll do better in cognitive behavioral therapy, well is the psychiatrist going to be happy about that information? Are they going to use it? And so to me there was a lack of actionability in our existing mental health care treatment systems and that drove me to move towards more scalable treatment approaches like digital therapeutics, which can be made more widely available and where we have the ability to implement the personalization ourselves. Now those aren't going to be a fit for everyone and for every disorder. We certainly have need for treatment personalization and precision approaches at much higher levels of care and in different contexts, but to me that was a big barrier that we're now hopefully trying to address. Dr. Williams, what do you think are the barriers? I entirely agree about the systems level challenge in our area of health, psychology, psychiatry. We haven't had systems that incorporate these objective tests, so down to the level of making the referral of the way the EHR is set up, they're not currently set up for those. I actually took on that challenge, which is different level to Dr. Cohen and we have had success in being able to incorporate into the EHR. This is currently a new clinic at Stanford and there's a clinic in Italy doing it and a couple of others in the process of setting up, where there's a way of ordering the scans and the Stanford Xerie biotyping system through the EHR. That took several years because it required actually building out a way to order it. It highlights the need for also regulatory changes. There you have new tools going through FDA for FDA approval. It highlights an educational challenge, so the new psychologists, psychiatrists, and other therapists coming through to be trained in these approaches. I see a lot of excitement around that, but it will meet an update in the way that we train. The last thing I've observed has been interesting, which is often our studies are not actually designed with the clinic in mind. Even thinking about, I realize some of the measures I use for measuring symptom outcomes or treatment outcomes are not the ones that a clinician would use. Really thinking about that level of harmonization as well. I think that's a great point. In work led by a colleague, Jamie Delgadillo, we did the first prospective validation that compared a clinical decision support tool that gave clinicians and patients information about who would be most likely to respond to low-intensity psychological interventions and who needed a higher level of care, like one-on-one CBT, and showed that this information could actually improve patient outcomes. It was a landmark study, incredibly impressive work by Dr. Delgadillo and colleagues, but one of the really interesting things was when he went to implement the study, he met some understandable resistance from the clinicians where they said, why should we use this information? We've been treating our patients and making these decisions about who should go to low versus high intensity and what they should get when they get there. There was a sort of skepticism that needed to be overcome, and he came up with this brilliant intervention where, based on some of Paul Mille's work, this amazing psychologist who showed that our ability to simultaneously hold multiple factors in our head to make these complex or nuanced decisions is pretty limited. We can't really keep more than two or three things in our head, so if I'm going to actually make a decision based on five or six factors, I can't do that. He showed them in a workshop with the clinicians by having them try and do that and showing them how difficult it was. By doing that, as well as by really explaining how these systems work so that it's not a black box, so that the clinicians can have some insight into, well, this is why this recommendation is being made, that those sorts of interventions were necessary and powerful in getting the buy-in that's needed, because if we develop these tools and clinicians and patients don't trust them or they're not on board with using them, then it's not going to really make a big impact. So I really love that work, and it shows really one of the barriers to getting these things used. That's fantastic work, and your point about being transparent and not having a black box, I think that is really crucial. I found that from the brain imaging side as well. And of course, as a psychologist, I know you know how difficult it is to get people to change. You look at hospitals and they have historically had a lot of problems getting doctors to wash their hands and other medical professionals. So if hospitals can't get that behavior change, it's a little bit of hubris for us to think that it'll be easy to get clinicians to change the way they treat patients and mental health. What I have noticed, and maybe Dr Cohen has, there's a big push coming from the individuals themselves, from patients, say being aware because so much is available now through internet and so on. They're aware of the advances and there's so much that's been achieved through the research. So the demand is there, and that I think will push from a different angle for really getting the advances into practice. And again, just from the viewpoint of our listeners, if I am somebody who is experiencing anxiety or depression, how would I access this type of treatment? Is there a place to go? Are there clinical trials? Is there some place I can go to get the treatment that you're talking about? So in our case, yes. So we do have the clinic at Stanford that does mean either being able to come in person to the clinic or being able to find a way to do at least part of it remotely. And that's a base from which we are setting up a broader network and we're actively planning that and very excited about it. The other possibility is through trials. So we have all of that information available on our website about how to express interest in trials and all the through the clinic itself. Yeah, in terms of the sort of stuff that I'm involved in using Dr Del Gaudio's model as an example, he's currently involved in large scale dissemination and implementation trials, meaning evidence has been generated that this can improve patient outcomes relative to the way that they were doing treatment allocation prior. And so now they're rolling it out to clinics across the United Kingdom. They have a national health system there that anyone who is depressed or anxious can get psychological care. And so this system triages over a million people a year, treats over 700,000 people a year. And so now in these clinics across the UK, that clinical decision support tool is being rolled out and studied to make sure that it continues to be effective in these new places and that clinicians are using it with fidelity and all that sort of thing. Just to wrap up, I want to ask if you have concerns about data privacy emerging from this work. I mean, there's so much information about us that's being collected right now. And I don't even know where it goes half the time. I mean, how should researchers and clinicians think about this issue and what should consumers know about protecting their own neurological data? This is something that scares me because it's outside my area of expertise, and I can see the potential harm. And I don't know the system well enough to know how we stop it. But if you think about something like a predictive algorithm that says who's going to be at risk for developing depression or other mental health problems, you can imagine that if an insurance company got a hold of that information and was able to make that determination without your consent, if they were able to scrape your social media data and then say, oh, well, we're not going to ensure this person because they're at high risk for developing disorder XYZ, that that could be a problem. Or your future employer could say, well, we've looked at all of your language from your social media. And based on that, we think you're likely to develop depression. I don't think we're going to hire you. That is scary to me. And I think these systems when used for the good of the patient can be incredibly powerful. But I can also see ways in which they could be used in ways that don't benefit the individual. Finally, I mean, this is the real wrap up, big research questions that you're still trying to answer. Dr. Williams, you want to grab that one? Yeah, now this is a really exciting time we are building on what we think of as a Framingham study for mental health. So there was a very influential impactful study called Framingham that really changed cardiology from not knowing what underlies heart disease at all. It was the same observation, FDA, FDA, his death from heart disease really initiated that project. So what we're thinking of is Framingham for mental health, which means very, very large scale national partnerships with research and with health networks, where we take the evidence that we have, which is robust enough to prospectively identify which treatment to start someone on and to do a very large study where you're actually able to compare back to back all the different available treatments we have across the different types of modalities and then follow people over time. So you're getting a good sense of does this make a difference in the short term? Can we double the chances of getting better sooner? And then can we show sustained positive outcomes? And that would be across different types of treatments. Very excited about that. We have to really kind of accelerate this process, we're very fortunate to have funds from an NIH initiative known as ImpactMH, which is specifically designed to accelerate the link from the biotypes and other measures to the clinic. So it's an exciting time. And Dr. Kohn, what about you? I'm a mental health expert of sorts, and I know well a lot of the people who develop the most commonly used evidence based psychotherapies. And when I try and get treatment for loved ones, for friends, it's a real challenge, even if they can self pay. And so to me, that's such a huge barrier. If I struggle with all of my access and privilege to get good evidence based psychological care for the people who are most important to me, imagine how challenging it is for people who don't have that knowledge and access, who are under resourced. And so the big thing that I'm working on right now with my lab is to try and develop interventions that can really take the evidence based care that we have, and make it more widely accessible, especially for underserved communities. And then not to just deliver evidence based care, but to deliver personalized evidence based care, that's more engaging, that's more effective, and that can really move the needle on getting people well and keeping them well. Well, Dr. Williams, Dr. Kohn, I want to thank you for joining me. This is very important work that you're doing. So thank you so much. Thank you. It's a great pleasure. Yeah, thank you for having us. You can find previous episodes of Speaking of Psychology on our website at speakingofpsychology.org or on Apple, Spotify, YouTube, or wherever you get your podcasts. And if you like what you've heard, please subscribe and leave a review. If you have comments or ideas for future episodes, you can email us at speakingofpsychology.org. Speaking of Psychology is produced by Lee Warnemann. Thank you for listening for the American Psychological Association. I'm Kim Mills.
