Thanks for listening in today. I'd like to welcome you to this episode of Aphasia Access Conversations Podcast. I'm Katie Strong, Associate Professor in the Department of Communication Sciences and Disorders and Director of the Strong Story Lab at Central Michigan University and serving as today's episode host. Today I'm talking with Dr. Sameer Ashaie from the Shirley Ryan Ability Lab. Before we get into our conversation, Let me tell you a bit about our guest. Dr. Ashaie is a Research Scientist in the Think and Speak Lab at the Shirley Ryan AbilityLab and a Research Assistant Professor in the Department of Physical Medicine and Rehabilitation at the Feinberg School of Medicine, Northwestern University. He earned is PhD in Speech-Language-Hearing Sciences at the Graduate Centre, CUNY. He is recipient of the 2022 Tavistock Trust for Aphasia Distinguished Scholar Award. Dr. Ashaie was also a recipient of NIDILRR's Switzer Merit Fellowship and NIDILIRR's Advanced Rehabilitation Research and Training post-doctoral fellowship. His lab the Shirley Ryan Affective and Emotion Rehabilitation Lab (SAfER) focuses on aphasia rehabilitation, particularly identifying post-stroke depression and related psychosocial disorders. He employs a variety of techniques in his research including eye-tracking and heart-rate variability. In this episode you will: Learn about the value of having researchers integrated into clinical care. Be empowered to think about depression on a continuum and why how we measure depression matters. Hear how network models can be a more useful way to examine complex disorders. KS: Sameer welcome and thank you for joining me today. I'm really excited about this conversation with you, and having our listeners get to know you and your work . SA: Thank you for having me here. You know I listen to the podcast, and I wasn't expecting to be here one day. So, it's a privilege being here. I KS: Congratulations on receiving the Tavistock Distinguished Scholar Award. Can you tell us a bit about the impact of receiving this recognition? SA: It's a big honor. You know, oftentimes as an early career researcher in the field of physiology or I guess any field me especially I'm wondering like, if I'm doing whatever I'm doing, is it making sense? Is it making a difference? Are people noticing it? So getting this award especially and people that have gotten before me and the work they're doing? It really validates what I'm trying to do as an indication of where I'm trying to take my research program and I'm hoping that it has an impact on people with aphasia, and as well as the broader research community. KS: Absolutely! I'm excited to start talking about your research. But before we get to that, I'd love to hear a little bit about how you came into the field of speech language pathology, because it wasn't a direct line. Your story is in fact quite interesting. And I think you refer to it as a winding path. Could you tell us a little bit about how you came to be working in the area of aphasia? SA: I started my PhD in theoretical linguistics, looking at generative phonology. And then I ended up taking a class with Dr. Loraine Obler. It was a class on the historical debates on language localization. And that really got me interested in language. After two years in theoretical linguistics, I switched tracks to neuro linguistics, communication science disorders. Because I really got interested in just language, more than just a theoretical perspective that I had as a linguist. And then, of course, there are two people that really had an impact on my career and continue to have an impact on my career. One is that I did my PhD with Dr. Jamie Reilley at Temple. And that's how I got interested into sort of the semantic aspects of aphasia. And he was really supportive and was really great in how we think about science and how we do science. And then I would say that the person who's had the most impact and continues to have the most impact, and really has made me think about this field is Dr. Leora Cherney. And I'm really indebted to her in terms of how I think about this field, how I think about our participants, how I think about how aphasia impacts their life in totality. And just seeing that kind of dedication and thinking about research that is support to impact people's life. And getting that inspiration from Leora. She has been really critical for me to really falling in love with this field, because you're keeping your participants at the center of the work you do. I mean, you might not see the impact, but you're trying to keep them that that is what your aim is. And I guess that's how I kind of came to this field, you know, some from sort of theoretical linguistics and interested in semantics and then getting a postdoc with Leora. And being inspired by her and the support she's given me to explore things. And carry a different line of research, but always keeping the participants in mind. KS: So, you're a research scientist who works in a rehabilitation hospital. I'm not sure if our listeners know exactly what you do all day long. Would you walk us through a ‘typical day' – if there is such a thing? What do you do in your lab? Would you talk us through that a bit? SA: Yeah, I, myself did not know what a research scientist is what I was doing! It was all new to me as well. It's different than a traditional academic position, and especially in a place like, Shirley Ryan AbiityLab, which is a rehab hospital. Especially the model in our rehab hospital is that researchers are integrated into the clinical care. So, what I mean by that is that our labs are situated right where therapies are happening. So even though we're not involved in therapy that's happening with the patients getting the care at that time, we can see different types of therapies. That might be OT (occupational therapists) giving therapy, or speech-language pathologists, physical therapists. So that's that integration. You really get to see patients. You get to see sort of different issues that you might not think about, because we're so discipline focused, right? So, it opens up your mind to all sorts of possibilities, collaborations, issues you might not think about. For example, physical factors are really important for people, but seeing that live and that being worked on, it has a different impact on you. The second thing is that, as a research scientist, you're not teaching classes. Your primary work is centered around research, which, which has its perks, but also that you miss sometimes that interaction, you might have had students in a traditional setting. Not that we don't get students (at Shirley Ryan AbilityLab), we do. But the primary focus is really getting the research program started. And there are no things like semesters, you have the whole year. We work on the hospital schedule. And as an early career (professional), a lot of what you do is dependent on how you get funded and that's how you established your lab. So we so for example, as an early career person, you might not necessarily have a lot of students working for you because we're not in a Communication Sciences Disorders department. So that's sort of different. But the main thing is that it's an academic environment, but it's not a university. KS: Yeah yeah you're right there in the thick of all of that rehab work. That's fabulous. I had the honor of doing a tour at Shirley Ryan at one of the Aphasia Days before COVID hit and it's just such a beautiful facility. It's just stunning. I love hearing about your path and a little bit about your work life and I've been interested in your research for a while now. I'm so excited to have this conversation. Your work in in mood and depression is something that really is an important area and I was hoping as we get started in this conversation if you could frame for us why this is such an important topic that extends to research and clinical work. SA: This is such an important question. And when I started my post-doc in the field of aphasiology, I was not interested in depression or mood. I was really interested in semantics. But, you know, talking to the patients being embedded in a clinical environment and talking to family members, everybody talked about the importance of mood, and depression. And what I realized is that everybody's talking about its importance. Everybody gives it a nod. But we're not all assessing it in a systematic manner. But we all recognize its importance, and people need this support. So, I started digging in and seeing in the literature what's going on. I came across this meta-analysis that was published in 2017, I think by Mitchell et al., and they looked at I think around 108 studies of stroke and only five studies with people with aphasia have looked at depression. I was like, that does not sound good. And then, studies that are in the field of aphasiology that look at depression used measures hadn't been validated in our field. So, I was like, we all recognize that this is an important problem and people need the support, but before we can go anywhere, that we need to figure out a way, how we can identify depression in people with aphasia, systematically. And of course, the big challenge I started thinking about that time is “how do you ask people that have language deficits about their inner feelings? Without sort of prompting them?” You know, we all use scales, those of us who do assess depression, we might modify them. But sometimes those questions are tricky to understand. And if you're modifying them, you might lead a person on to an answer. That's one thing. We can rely on caregiver reports for depression, and they're good. But we also know that those reports can underestimate and overestimate depression. And they're highly impacted by caregivers' mood itself. That was another thing. So, I wondered what can we do that assesses this systematically? And we can also include people with severe aphasia, who we often just exclude from these studies and who might have some of these most issues when it comes to mood or depression. There's some work in neurotypicals, that use a variety of techniques. For example, eye tracking. Research has shown that people who are depressed, tend to look longer at sad faces, or stimuli that denote sad valence. And their response is blunted away from positive stimuli. For example, if people are depressed they might look longer at a sad face and they might also look away from a happy face. There is also work looking at heart rate variability as well which uses certain metrics that you could derive from variability in between your heartbeats might tell us something about depression. This is also true with the dilation of our pupils, or EEG. And of course, none of these measures are perfect. Like we know with anything, we're not getting perfect measurements. But I started thinking that “yes, they might not be perfect, but can I come up with an algorithm or some kind of a composite that takes all these things into account, because if they all point to the same problem, then that problem must be there.” So that's one of the things I'm trying to do right now is combine pupillometry, heart rate variability, and eye tracking to see if we can come up with some kind of a metric that can identify depression. That way, we can move away from language in the sense that we're only using minimal language in terms of directions. We might just show people a happy face, or some emotion that some stimuli that denotes emotion. The second sort of thing, which is really important is that not thinking of depression as something you either have it or you don't have it. It's on a continuum. It could fluctuate. One day, you could have some symptoms. Another day, you might not have any other symptoms. Or in the same day, it might fluctuate. So, how do we assess that? Related to that is not just relying on some scores. For example, we all just take, like, let's say we take a common scale, like the PHQ-9 (Patient Healthcare Questionnaire-9th Edition) and we might take the scores, and we sum them up and say, “hey, this person they're above a cut off”. But in that kind of approach, we're also missing what these individual symptoms are doing. The person might not endorse every single symptom in that scale. But they might endorse some symptoms. So are we just going to say, “no, they didn't meet a cut off, but they had three symptoms that they were on the scale. For example, ‘I was sad. I was fatigued, I had a loss of appetite.” But everything else wasn't there. Are we just going to negate those symptoms? So how do we take these symptoms into account as well, when we are thinking about depression. Within the broader field of psychopathology, there's a lot of movement thinking about individual symptoms as well. So, I'm just basically taking that and applying it to our field. It's nothing new that I'm coming up with, rather is just really seeing what people in the field of psychopathology are doing, confronting all these problems. And thinking about how this can applied to our field, because they might really have a direct impact on something we're doing when it comes to treatment, right? For example, if we start thinking about individual symptoms and that day a person is fatigued. Well that might directly impacted how they respond to treatment rather than just as a sum score. So that's another angle I'm taking when it comes to this work and depression. KS: That is so important. We all know what matters, but can you help us to know like, how big of an issue is mood depression in aphasia, you know, incidence prevalence or what, you know, do we know anything about that? SA: We do. And if you look at the literature, once again, they're so varied. Some papers might report 70%, some papers might report 30%. But I would say at least, it ranges anywhere from 30 to 70%. But I think a lot of that is also dependent on how we're assessing it. Going back to the scales that we are using and how reliable those scales are. There was a systematic review early on that indicated most of these skills might not even be valid. Are we use a caregiver reports? Are we supplementing that with something? In the general stroke population, we know at least 1/3 of stroke patients have depression. And with aphasia, it's between that 1/3 to 70%. It is most likely much more than that. But I think, to really get at it, we really have to start thinking about the tools we're using. But we know it's an issue because clinicians report it, patients report it, caregivers report it, whatever literature we have, which is not much, those studies report it. In our own study, we looked depression that might not meet the threshold for major depression. And we had around 20%, and those that meet (criteria) for minor depression, those were like, 18% or so. So, it's in that 30-40% range. It's a big issue. But I think the bigger issue is that we are really missing how many people have it? How many people have the different symptoms? And what we also have is an incidence rate, a snapshot of the incidence rate, right? Like, you know, at six months, at one year, but we really need to start thinking about daily and how sort of depression changes over time. It will not be sort of weekly or yearly, we don't have that much longitudinal work, either. When I talk about daily, I talk about real world as well. I don't know if that answers your question… KS: It does. Yes, absolutely. Yeah, I love that, that it's we have some ranges, they are not probably as accurate as they could be, because we don't have the right tools to assess it, and that they're just a snapshot that we're not really looking at this over time or, as you said that day, that daily basis. SA: One thing that I want to point out is that, and even with the lack of tools it's good that we are still assessing for depression. I don't want to make it seem like that there's nothing out there. But I think like for all of us, even the tools we're coming up with, we should always be thinking in our own, how can we improve upon whatever we have. And we all get attached to the methods we use. But I think at the back of our head, we should always be like, “can we improve these methods? Can we do something better?” Because ultimately, it's not about us. It's about people, our patients, our participants, family members that we're trying to do these things for. So it's really great that tools do exist, but we have to be candid, that we might not be getting everything out of them. They're a great steppingstone, but we have to constantly go back and build and just keep on taking new developments in the field of psychopathology in the field of measurement science and applied to them so that our field is moving along as well. KS: It's kind of the essence of evidence-based practice, right? We're using the best tools that we have at the moment, but that certainly we need to be on the lookout for what's coming in the newer literature or tools. Sameer, you have some really cool projects going on related to depression and mood. You talked a little bit about them earlier, but could you give us a little more detail on what you've got going on? SA: So, one thing I could kind of hone in on that I mentioned earlier is on eye tracking. Right now we're trying to come up with some kind of an algorithm where we are relying minimally on language. So just the directions are language based. We're getting people in, and we're doing a combination of eye tracking changes in the pupil dilation and heart rate variability, as people are looking at different stimuli that denote different emotions. We have a paper out that looks at the feasibility of it. And what we're basically looking at trying to quantify that using some existing scales and caregiver reports. Can we then take these metrics and see whether people are looking at sad or happy faces, or any other stimuli that denote emotions, and is that related to these traditional scales. And then how can we then come up with a metric based on these three measures, pupillometry, heart rate, and some of the eye tracking indices that can point out depression in people with aphasia? We're using these tools, but the approach is out there. Anytime people are validating new tools, they have to rely on existing tools and go through these different iterations. So right now, we're in the first iteration trying to see what kind of metrics we can extract and what those metrics can give us that are easy to use. And one thing is that eye tracking or heart rate variability over the years, they have become really accessible, and the tools are not expensive themselves. So, with the aim that down the line, can this be used in the clinical setting? Of course, we're far away from that. But that's the end goal, we hope as a quick diagnostic check. KS: Okay, yeah, that's what I was going to ask you, because we've got a lot of listeners who are clinicians. And, you know, sometimes as clinicians, it's difficult to see the relevance of things like eye tracking and heart monitoring, when you're reading literature, when you're trying to figure out, “How can I help this person right in front of me?” So, I was hoping you could explain a little bit why those tools to track variables are so important. SA: I think this is a great question. And I think the big thing is that sometimes we just need to demystify these tools. I liked the way you framed it. We really have to think of them as tools. They're tools that were trying to use to assess a problem that might be difficult with the traditional language measure. That's really it. It's not they are better than behavioral measures. It's that because people aphasia have difficulties in language production and comprehension, can we use something else that relies minimally on language? That's really it. It's not some kind of fancy approach. Yes the tools themselves might sound fancy and stuff, but really the aim is it's just a tool that's addressing a certain problem. And with heartrate variability, we can already see because now it's so common, right? All our Fitbit or Apple Watches, they all have it. And even at a basic level, we're starting to think like, “Oh, this is what my activity level refers to.” So, I've started thinking about those kinds of things in a clinical setting. And the same thing with eye tracking. If these tools are sort of readily available, can we train people to use them in a quick way? Because of course, you could do fancy analyses, but you could also look at just quick measures that if the pipelines are in a place that people could just pull it out. Just like when clinicians give a battery of tests, if you ask me, I'm not a clinician, that's really complicated. You're working with a human being you have to change it on the fly. But people get trained on it all the time and can do it. It is the same thing with these tools but if we are successful in coming up with these metrics and these algorithms.. why not? Can clinicians be trained on using these tools in a clinical setting. KS: It's exciting to be thinking about that identification of depression or mood disorder. We've got lots of work to do on what to do once it's identified, but just the identification is, as you said, that first step. I was curious if you might be able to recommend something to our listeners, you know, as I said, lots of us are clinicians, about what we should know or do right now about supporting mental health and people with aphasia. SA: I think all the clinicians I've talked to everybody recognizes the problem. That's the biggest step first of all. I think then it is really being aware of systematically assessing it. To be clear, I don't want to negate the support part. That's the end goal. But if we're not assessing depression routinely, then we're missing a big chunk. I want to keep stressing that point. I think the one thing clinicians can do is to start assessing people to the best of one's capability. If you're using a scale, then being systematic with that scale. If you're giving it in one iteration, you're giving it one way, on Day One. When you give it again, try to be as close in how you previously administered it so that we we know that you are assessing that same construct. The second thing is what I've touched on earlier, is that thinking of depression as a continuum and that it fluctuates. It's not enough to just give a screening once, or to assess this person's mood, pretreatment and post treatment. But what about daily? Because if you start looking at daily variability, you might really start thinking, “Oh, no, we're all here. Like the patient he was feeling kind of down today. I don't know if you've put enough effort into it or something along those lines.” Well, low motivation and those kind of things are symptoms of depression. So I'd like to encourage clinicians to start thinking about assessing this daily. And I think then, once we start sort of assessing it routinely, and making it a part of our work and not thinking of it as separate. That's the key. Not thinking about it like language is here, depression here. Like you know, the work you do, Katie, on narratives or stories, this is all interactive. They're all impacting each other in some sort of a loop. And then lastly, once we're getting these, and we're routinely assessing people and getting them, then thinking about getting mental health support. And for that, we really have to start thinking about interdisciplinary work. And you could speak to that as well, because I know that you have those projects going on. We can do everything on our own, working with psychologists, referring people…once we can define these basic systems, and then, you know, down the line and training psychologists or psychiatrists and different techniques that they can work with people aphasia. Or clinicians who are up and coming getting some training. And that this is just part of routine care. It's not something we recognize the importance, but then we kind of put it on the back burner. KS: Yes, right the back burner. Or say, “we don't have the tools, so we don't know what to do but we recognize it's a problem, but we don't do anything about it.” I agree. Sameer, since you brought up the interdisciplinary work and you have developed some relationships in psychology. I feel like you're kind of an exemplar interdisciplinary collaboration. Could you talk about how this collaboration has influenced your work and give our listeners any tips on how to develop such a rich collaboration? SA: All of the work I'm doing in depression and thinking about this is really influenced by people in the department of psychiatry and psychology. Much of my collaboration is with Dr. Stewart Shankman, who is the Chief Psychologist at Northwestern. And being a part of the National Institute of Mental Health (NIMH) thinking about “how do we conceptualize depression?” and things like that. I just reached out to him, because I was interested in his work. I think we have to not be scared that people might not respond if we reach out. I just emailed him, and he was nice enough to respond. And I started attending his lab meetings and presenting our work to the lab and this problem, “how do you assess depression in people that have language deficits. How do we assess their inner feelings when they can't express themselves?” Being embedded in sort of in his work group, I was really exposed to this work. I don't think I would have been exposed to the work that people in that field are doing. For example, debates about how do we think about symptoms? Or how do we integrate these tools? How do we think about different emotions? And then applying it to our field of CSD. And thinking about metrics of depression. My work has really been influenced by how people in that field are grappling and using these issues. One can't do this work in a void. If there are people who are doing this work and that's their field, it only benefits us to form collaborations with them, learn from them, and bring our unique problems to them. So that we could come up with solutions that integrate the best of our knowledge domains. In other words, that team science approach is really the approach I'm taking towards this issue of depression. I think any work we do in the field of psychosocial disorders, mood, anxiety, fatigue, or whatever, I think it's really important that we start working with people who have focused their career on this issue. KS: I so appreciate you sharing that. And even just the simple tip of putting yourself out there to send an email and introduce yourself to someone who's from a different discipline to start that relationship is important. I envision through attending his lab meetings, you're there in his world, learning about things in a way that you wouldn't be, if you weren't a part of what he's got going on. And thinking deeply about how you can apply that to your interests in aphasia. I'm so excited. Our field just needs this innovation and it's exciting to hear about the work you're doing. SA: If I just did all on my own, I would have been just looking at what's in our field, what's in stroke, looking at papers…but you're not embedded in people who are doing this daily. They might not be doing it in our population, but this is what they're doing. And they're grappling with the conceptual issues as well. Tools, measurement, scales, everything. So that's a huge benefit to us because when we think about depression and stuff, yeah, the work has been done, but when you're embedded in that setting, you could take some of the newer things and start applying it as well. Seeing how we can move rapidly. And of course, then the flipside is like, also the collaborators have to be willing to collaborate with you. Dr. Shankman, he's been great. He's been willing and he's been great at mentoring me. I think most people, if you reach out, and you explain what you're trying to do people are willing and you also can contribute to their work, that I think that you know, these relationships will form. KS: Well, that is how cutting-edge work gets done. It's exciting to hear about it. You also have some additional interesting work, particularly in network analysis. Sameer, could you tell us what network analysis is, and why it's important to life with aphasia? SA: In a nutshell, if we start talking about networks, networks are everywhere, right? Most of us are privy to the notion of social networks. That we're a bunch of friends, we're connected to each other. And a group of friends might cluster together, and then that cluster is connected to someone else. Anything, we take a look at it, if it's complex, it forms a network. Consider airports, highways, how they're interconnected. Certain things are central and more important than others. That's a network. People often give an example a flock of birds. Birds might have different characteristics. But when they form a flock, it's made up of different parts, but they're all interacting together to form that flock. That's basically what network is. And it's derived from graph theory in mathematics. But at the end of the day, it's about looking at complexity. Anything that's complex, we could think of it as networks. So the work of network analysis, it's a collaboration between me and Dr. Nichol Castro at Buffalo. Both of us are interested in this approach and we decided to tackle this together. Right now we're building a network model of aphasia. One of the reasons, we decided to think about network approach is that going back, you know, we have these these two approaches, and people do integrate them. People do give nod to them, but impairment-based approach an LPAA (Life Participation Approach to Aphasia). And it's not to say that people that focus on impairment don't care about LPAA, or people that embrace LPAA, don't care about impairment. But generally, there is some kind of distinction being made, either implicitly or explicitly. And you might give nod that one thing is more important than other. But me and Nichol, we started thinking rather than thinking, “Okay, rather than thinking about what is important (language, or depression or anxiety) what about coming up, and thinking about all of them interacting in the network. And not assigning a priori importance to either one of them but rather looking at these interactions between multiple factors, and how they might impact each other, so that we're not missing anything, because aphasia is complex. It's not just about language. It's not just about depression. It's not just about supports (social support). It is about everything. So that's where a network model becomes useful. And then from there on building these initial models, then one could start thinking about treatment. That it is possible in a network, that one thing is more important than the other. And that is taking it one step further in an individual, Individual, A versus B, something might be more important in Individual A, like depression, and in Individual B it's communication confidence. We could start by building a big model first. And of course, all these things have steps and eventually come to that and thinking about how can we identify critical, important factors for a person that we could intervene on? But before we could do that, we wanted to build a bigger model at a group level, and start seeing what things are important in this network? And, and not thinking like, “Okay, I'm gonna just call aphasia…and we all are used to saying ‘aphasia is a disorder of language. Blah, blah, blah,' could be also impacted.' But aphasia is a complex disorder, let's see how these all these things interact.” You don't have to assign the importance to A or B. Or say like, “Okay, I'm going to look at attention, maybe that's about language.” Instead, let's see how all of them are impacting each other and are some things more important than others. I think with this kind of approach…all of us have this thinking. We're just trying to come up with a model that addresses this. And eventually, then this kind of model doesn't have to be just limited to outcomes. People could integrate brain, genetics, you could have different layers. And that goes back to your work about interdisciplinary collaboration. When you start thinking about things as a network, that can also extend to the network of people who are doing work in aphasia. That if it's a complex disorder, and people are looking at all these complexities, because not everybody can do everything that we can take the network of future researchers, and then why not integrate and use that network model for the vision and see all these things? That's what we kind of really are trying to get at. KS: The potential is powerful. Wow. Well, you've got a manuscript in the works that's about this complexity of participation poststroke. I really enjoyed reading about the project. But one thing that really struck me in the findings was how positive affect impacted participation. Could you tell us about this and the project? SA: So this is all pre-existing data. We wanted to establish some sort of causal relationship at Time Point 1. For example at 3 months post discharge, can you predict something at 12 months post discharge? And one the reasons we were interested in positive affect is that we always think about depression, but positive affect is there too, right? And having positive affect could impact people in a positive way. We wanted to look at all these things, put them on the network and see how they're interacting to determine what might be causing or establishing some sort of causality. What was really interesting is that we thought that perhaps social support would predict participation. But it was really positive affect early on, that was predicting many of these things. When you really start thinking about it, it's not that surprising, because if you're feeling positive, and psychology, then you're going to seek out more help. And then you're going to seek out more help, you might participate more in the community. But having that affirmation is critical, because then once again, it goes back to a question mental health support. How can we focus on positive affect, as well, in our treatment? Maybe, if that's kind of integrated with intervention. If people are feeling better, or happier with that sort of, you know, give them some push towards seeking more help? And it's all cyclical, right? And that's what we are seeing, at least in this early work. KS: Oh, it's really interesting. I think clinically we know that in our gut, but is there something we can do to promote that or help support that down the road? This fabulous, fabulous! Well, Sameer, this time has gone by quickly. I've enjoyed the conversation. As we wrap up, do you have any final thoughts you'd like to share with our listeners? SA: Thank you for having me here. And it's a privilege being in this field, especially as somebody who was trained early on as a linguist, and now I'm doing complete something else. And I'm working with clinicians. It's an honor to participate. It's really a privilege. Thank you for having me here. KS: It's fabulous that you're here and doing this important collaborative work. Thanks for spending time with us today. You've given us lots of food for thought. Listeners, check out the show notes and I'll have links to all of the Shirley Ryan AbilityLab details there as well as Sameer's work and some of the other things that we talked about during today's conversation. On behalf of Aphasia Access, we thank you for listening to this episode of the Aphasia Access Conversations Podcast. For more information on Aphasia Access and to access our growing library of materials go to www.aphasiaaccess.org If you have an idea for a future podcast topic email us at info@aphasiaaccess.org. Thanks again for your ongoing support of Aphasia Access. Websites and Social Media Shirley Ryan Ability Lab https://www.sralab.org/ Shirley Ryan Think + Speak Lab https://www.sralab.org/research/abilitylabs/think-speak-lab Shirley Ryan Affective and Emotion Rehabilitation (SAfER) Lab https://www.saferlab.net/ Shirley Ryan Ability Lab on Twitter/Facebook @AbilityLab Interested in Digging Deeper? Ashaie, S., & Castro, N. (2021). Exploring the complexity of aphasia with network analysis. Journal of Speech-Language-Hearing Research, 64(10), 3928-3941. https://doi.org/10.1044/2021_JSLHR-21-00157 Ashaie, S. A., & Cherney, L. R., (2020). Eye tracking as a tool to identify mood in aphasia: A feasibility study. Neurorehabilitation and Neural Repair, 34(5), 463-471. https://doi.org/10.1177%2F1545968320916160 Ashaie, S. A., Engel, S., & Cherney, L. R. (2022). Test-retest reliability of heart-rate variability metrics in individuals with aphasia. Neuropsychological Rehabilitation, 18, 1-25. https://doi.org/10.1080/09602011.2022.2037438 Ashaie, S. A., Hung, J., Funkhouser, C. J., Shankman, S. A., & Cherney, L. R. (2021). Depression over time in persons with stroke: A network analysis approach. Journal of Affective Disorders Reports. https://doi.org/10.1016/j.jadr.2021.100131 Mitchell, A. J., Sheth, B., Gill, J., Yadegarfar, M., Stubbs, B., Yadegarfar, M., & Meader, N. (2017). Prevalence and predictors of post-stroke mood disorders: A meta-analysis and meta-regression of depression, anxiety and adjustment disorder. General Hospital Psychiatry, 47, 48–60. https://doi.org/10.1016/j.genhosppsych.2017.04.001
