Session 1: NIMH 75th Anniversary Event 3

Transcript

MAURA LANDERS: Thank you, Dr. Bellamy, for your inspiring presentation. Can I just get another round of applause? Because she really took the theme and very inspiring. So, thank you.

My name is Maura Landers. I am a program analyst in the NIMH Office of Science -- oh gosh, Policy Planning and Communications. And I will be the moderator for our first session.

Just to note that for today's sessions, your speakers will be introduced and their talks will continue in succession. So, I'll go through everybody's bios first, and then folks will come up. Following the last talk, all speakers will return to the stage for discussion and audience questions.

Now I have the pleasure of introducing the speakers for our first session. Dr. Michael Wells is an assistant professor in the Department of Human Genetics at UCLA. Dr. Wells earned his PhD in Neurobiology from Duke University, and he completed his postdoctoral training from Harvard University before launching an independent research program at UCLA. His research focuses on the genetic factors that govern the early development of the human brain and ways in which these processes lead to variation in human traits and disease.

Next we have Dr. Antonio Fernandez-Ruiz, an assistant professor in the Department of Neurobiology and Behavior at Cornell University. Dr. Fernandez-Ruiz completed his PhD at the University of Madrid, where he developed and applied machine learning methods to study the biophysical basis of brain dynamics. The mission of his lab at Cornell is to understand how neural neuro dynamics in distribution brain circuits support complex cognitive functions, and how small imbalances can lead to pathological states.

Next, we have Nicole Provenza. She's an assistant professor in the Department of Neurosurgery at Baylor College of Medicine. She received her PhD in Biomedical Engineering from Brown University and completed her post-doctoral fellowship at Baylor College of Medicine. Dr Provenza's research focuses on the neurophysiological underlying cognition and emotion and the effects of neuromodulation on neural activity and behavior.

And finally, we have Dr. Brielle Ferguson. She's an assistant professor at the in the Department of Genetics and Neurology at Harvard Medical School, and an assistant professor of the Department of Neurology Research at Boston's Children's Hospital. Dr. Ferguson completed a PhD at Drexel University College of Medicine, followed by a postdoctoral fellowship at Stanford University. As a systems neuroscientist, her lab focuses on characterizing circuit mechanisms of attention and cognition, with the goal of identifying novel biomarkers that can be used to inform treatment approaches and a broad range of disorders.

And now, please welcome Dr. Michael Wells to the stage.

MICHAEL WELLS: Hi, everyone. I want to thank you all for joining me today and for giving me this opportunity to share with you my long-term vision of a genetics first approach to understanding human disease through the creation of something we are calling an atlas of human vulnerability. So, everyone in this room is differentially susceptible to a wide range of diseases in a manner that is at least in part, genetically encoded. Now this is especially true for highly heritable diseases like autism and schizophrenia, as well as other conditions that have strong genetic links, like Alzheimer's, certain types of cancers and viral infection.

Now some of the earliest -- some of the earliest work to quantify the contribution of genetics to diseases relied on twin studies and other ways of calculating heritability scores for these conditions. But something huge happened 20, 25 years ago, with the advent of human genome sequencing technologies that really pushed the field forward and allowed us to start identifying specific genomic loci that were associated with these diseases in different human traits. Typically, through these sorts of GWAS and large scale exome sequencing type studies.

Now the hope was that by simply identifying the genetic mutations and variants that were associated with diseases that we'd be able to very rapidly start developing treatments for these conditions. Hasn't quite worked out that way, at least it hasn't worked out at the pace that we were hoping. And really need to ask ourselves, why is that the case?

Well, as it turns out, perhaps unsurprisingly, the genetic landscape of human disease is quite complex. And the manner in which these genetic variants exert their effects and contributed to disease risk is they're influenced by many different biological variables. This includes the fact that some of these risk variants exert their effects when they're exposed to certain hormones. So, sex is an important biological variable. Age or developmental stage is important. Genetic risk for autism appears to manifest primarily prenatally, while genetic risk for Alzheimer's obviously manifest much later in life. Environmental context is also incredibly important. We know that things like stress and pathogens and neurotoxicants can all influence the risk for various diseases.

Finally, ancestry is important. And I'm not talking about socioeconomic factors. You're going to hear a lot about that today. I have already heard about that today a bit. I'm talking about biologically, what we're talking about here is that different ancestral populations experience diseases quite differently. And in fact, one of the things that I'm most concerned about is the fact that there's treatment resistance differences across ancestries.

So, for example, there's many commonly prescribed drugs for asthma and certain types of cancers and diabetes that do not work as well in African Americans and Latino populations. And we need to really start to really start to think about how to combat that. Now, one of the main issues is that some of the existing technologies and approaches for answering some of these questions are limited in their ability to understand genetic risk and disease mechanisms in a manner that takes into account all these different biological variables.

So, we propose the creation of a sex developmental stage, context and ancestry specific catalog of the genetic and molecular risk factors contributing to human disease. And this is what we're calling an atlas of human vulnerability. And through this, we hope to better understand genetic risk and thereby understand human disease, and then hopefully develop treatments based on this information.

So, obviously, this is not something that one lab can do. It's going to require a lot of effort, a lot of money. I'm looking at you people who give out that money. But the question then becomes, well, what will my team, what will my lab, contribute to this massive endeavor? Well, to answer that question, we take a look back about 25 years ago to the beginning of the stem cell revolution.

So, by definition, stem cells can become any cell type in the human body. But it wasn't until about 1998 that we're able to harness that power for research purposes with the creation of the first human embryonic stem cell line. We've thus been able to generate what are called human induced peripheral stem cells, which really opened the door to being able to look at stem cells derived by -- derived from patients.

Shortly thereafter, many different techniques to turn these stem cells in a dish to different types of brain cells. This has dramatically revolutionized our ability to understand human brain diseases using these in vitro models. So, it's with this backdrop that as a postdoc in Kevin Eggan's group and in collaboration with Steve McCarroll at Harvard Medical School, that we wanted to push it just a little bit further.

And the reason the real impetus for this was the fact that many of these in vitro studies looking at human patients were typically looking at a handful of them. So, three or four patients, three or four controls, and most of these types of experiments. And as I just mentioned, that means there are certain biological variables that will not be factored into those studies.

So, we developed a platform that enables population genetic studies in a dish. We're calling this a cell village. The way this works is you take cell lines from many different human donors. They can represent different sexes, ancestries, and disease statuses. And we can get anywhere from 10 to 150 people represented in these villages.

What we do is we culture them all into the same dish. And by doing so, we are eliminating a lot of the technical variation that can reduce your ability to identify genetic signal using more conventional approaches when you culture these cell lines separately. The other real big advantage is that because they're in a uniform environment, now we can isolate genetic contributions to how these individuals respond to different cell extrinsic factors like developmental signaling molecules, viruses, toxicants and even therapeutics.

So, our hope is that using this system, we'll be able to better understand human disease, as well as identify biomarkers for -- if you're susceptible to viruses or toxicants, or even a biomarker for whether or not a drug is going to work in you. So, how exactly this work? I'll spend just a minute or two talking about some of the features here.

Essentially, we can do high throughput phenotyping in this uniform environment. We can look at the cells in our village at the molecular level, using this tool that involves single cell RNA sequencing. And then re identifying the donors based on their natural barcode. So, the barcode is their genome, essentially. So, this gives us donor level gene expression profiles. So, we can do, say, differential gene expression analysis if you have patients and controls in the same village. And we can also build these gene co-expression networks that are quite valuable for understanding the molecular environment of a cell.

We can also do cellular phenotyping using the village system. There's a tool we call census-seq, and it's doing exactly what its name implies. It's running a census of the village. Who's there and what percentage of the village they represent? As you can see, you can do growth assays or viability assays by measuring over time how donor compositions change. Some might increase their presentation, others might decrease. And that gives us valuable phenotypic information.

We can also run facts based assays, which essentially means you can separate the cells in the village based on different markers. Say, a marker for cell type identity, or say, a marker for DNA damage. Whatever it is, you can then do census-seq on those different fractions and use that as a quantitative phenotypic score on a per donor basis. So again, we're doing all of this on 100 people, you know, in the same dish.

So, one of the real selling points of the village system to me is the fact that we can then take all these different data types we generate from the same batch of cells and start integrating them, which means we can cut across different levels of biology. We can, for example, identify relationships between alleles and specific cellular phenotypes in our dish. We can identify genetic variants that influence the expression of nearby genes. And of course, we can find relationships between gene expression profiles and specific cellular phenotypes. So, we are getting a comprehensive understanding of disease mechanisms in a dish using the system as well as I mentioned, identifying specific variants that might confer that risk.

So, what is my lab doing? Let's get into the present a little bit more. As mentioned, we focus on human development, human brain development, I should say. And there are four research pillars that we're either currently addressing or plan on addressing in the very near future. These include genetic models of autism spectrum disorders. We're also interested in identifying biomarkers for risk for environmental neurotoxic hints, as well as neurotropic viruses. And finally, we want to get into pharmacogenomics, which is the study of how your genome influences drug efficacy. So, influences how you respond to a therapeutic treatment.

I'm going to give you a couple of snapshots of some of the work that we have either published or are currently working on. This one was published in 2023 in cell stem cell. And this was our first demonstration of the village for this type of work. So, we were studying the Zika virus. And we're trying to understand what are the genetic biomarkers for risk to Zika virus. And so, we're looking at human neural progenitor cells that we created and put into a village.

And the first thing we noticed is that some donor lines were being demolished by this virus, and others were very resistant. And so, to understand that, we used the village system. We molecular profile these cells that are cellular phenotyping. And in doing so, we're able to identify a single variant in the whole human genome. A single variant that could explain almost 60 percent of the variation in infectivity.

And in doing so, we have nominated that variant, which is in a very powerful antiviral gene. We've nominated that variant as a risk factor for Zika susceptibility. We're following up on this work by looking at other neurotropic viruses like cytomegalovirus and measles. Using the same concept, we're now trying to tackle neurotoxicants.

So, the first one we're focusing on is lead. Lead is a very pervasive neurotoxicant. Unfortunately, it does appear to differentially affect minority populations and low income individuals. And so, it has detrimental effects on brain development. So, again, one of the first things we identified was huge variation in susceptibility. Some donor cell lines were quite resistant to the effects. Others were being demolished by this heavy metal.

So, we're in the process of understanding both the molecular and genetic contributions to that risk, doing some of the types of experiments I just mentioned before. We then hope -- I should mention, we hope to expand this to other neurotoxicants like arsenic as well as ethanol, and some novel emerging neurotoxicants like pesticides and forever chemicals. Okay. And I also mentioned that we are very interested in genetic risk factors for autism.

So, in this case, we built a village of neural progenitor cells that consist of neurotypical controls, as well as patients harboring a micro deletion of 16p11.2 chromosomal region, which is a major risk factor for autism. And at baseline, just culturing these cells and looking at looking at them in a dish, we didn't see dramatic differences between patients and controls.

What really jumped out at us was when we started stimulating these cells with different important signal transduction molecules that play important roles in brain development. And that is what started to show some of these really interesting potential disease mechanisms. Specifically, we noticed that these patient lines are hyper responsive to sonic hedgehog activation, which can have traumatic effects on neurogenesis and dorsal ventral patterning of the brain.

So, we're following up on this in two ways, trying to understand what are the consequences of this on brain development. And also, trying to understand why exactly this is happening in these cell lines. We're excited about this idea as a whole because it potentially opens up this avenue where we can start looking at genetic models of autism and start systematically characterizing their responses to these important signaling cues, which we think will reveal some of these sort of hidden disease mechanisms.

Okay. So, that's what I've been doing. What do I hope we're talking about at the 100th year anniversary of the NIMH in which we look back and say, "Look how big and important villages were." I hope we can say that villages played a really important role in accelerating the personalized medicine revolution. So, what do I mean by this? Well, we typically perform drug screens in a handful of cell lines. And as I mentioned, that means many biological variables are not playing a role -- or not being considered in that type of screening assay.

So, what if we could actually perform the drug screens in a village? So, that's -- pharma companies or researchers are actually moving forward and prioritizing the compounds that were safe and effective in a large portion of the human population. This could potentially start to alleviate some of those disparities I mentioned with existing drugs that do not work well in some patient populations.

We could also then identify the biomarkers that are linked to response or non-response to some of these treatments and that can help in the design of clinical trials. Imagine being able to determine ahead of time whether or not someone would be a responder or non-responder in designing your trial around that information and using that genetic biomarker as a way of selecting individuals.

Finally, along that same idea, if we had biomarkers for efficacy of some of these drugs, that could help doctors prescribe the correct drug or the most effective drug to patients, rather than going through this trial and error period that is actually quite common in psychiatry. Okay. They asked me to think big. And so, I'm going to think really big here. So, bear with me for a moment.

I believe that villages can also help us understand where we came from and where we're going. So, I think villages can be used for evolutionary studies. We can model past scenarios that humans have endured, that have shaped the human genome. So, these sort of selective sweeps, whether it be famine or viruses or migration or climate change. And we can model this in a dish and ask what were the mechanisms that drove us that natural selection and shaped our genome?

Here's where it starts getting a little wild. A lot of people want to leave Earth and go to different planets. Sometimes I feel like I'm one of those people, and there's some issues with that. We know that astronauts are exposed to higher levels of radiation while they are off our planet and traveling. This is a major concern for our ability to do this sort of space travel. And so, by modeling that radiation and identifying biomarkers of resistance, we can actually leverage that information to potentially create preventatives that minimize the effects of this radiation on people who are traveling up to other planets.

And also, if we actually get there, we actually get to one of these different planets, that's going to be a novel environment for people. Also, if we stay here on Earth, there's likely to be novel environments, given the rapidly changing climate that we're experiencing. I believe villages could help protect us from those changes by helping us identify and model those future scenarios, so that we can find biomarkers of resistance. And then leverage that information, leverage that genetic information, to develop preventatives for something that happened -- if something like that were to happen.

So, with that, I want to thank you for your time and indulge in me with some of these wild ideas. And I'll leave a slide up while the next presenter comes up.

ANTONIO FERNANDEZ-RUIZ: Well, hello everyone. It's my privilege to be here speaking in this great symposium. Thank you to the organizer for having me. So, today I'm going to be talking about some of the recent work from my group and how I believe it can inform some future avenues for the research of the medical basis of mental health disorders, as well as for the development of new therapeutic approaches.

So, I want to start by highlighting a very well-known fact that many neuropsychiatric disorders, ranging from Alzheimer's to depression or schizophrenia, they have a variety of causes. They can be genetic mutations in specific genes. They can be environmental factors related to lifestyle or diet, or they can be developmental alterations.

And on the same way, they are characterized by a broad diversity of symptoms, from cognitive decline to affective disorders to psychotic breaks. And you know, traditional approach, let me see -- traditional approaches have been typically focused on taking one of these causes and trying to dissect them to understand them in detail, or focusing on the mechanisms of some specific symptoms.

And I want to propose today a somewhat complimentary approach, by trying to identify common principles, point of convergence across different causes, across symptoms. And we already have a spoiler here because what I'm going to propose is going to be a point of convergence across many of these different causes for multiple diseases are disruptions in neural circuit dynamics in a specific circuits in the brain. Such disruption in neural dynamics lead to impairment of information processing in the circuits.

And I believe that these impairments of information processing and one of the major underlying causes of a cognitive and other types of symptoms that are common across multiple disorders. So, I hope by the end of the talk, I can convince you that there is some value of taking this approach by identifying this point of convergence and try to both understand them and target them for therapeutical means.

Yeah. So, traditional approaches to treatment of mental disorders, for example, for macrological approaches, have been very successful in many cases, but also have some important limitations. Among them, for example, that the effects can be delayed respect to the appearance of the symptoms, and there can be difficultly adjusting in a dynamical manner.

So, the complementary approach I am proposing is based on providing on demand manipulation of neural dynamics based on the detection in real time of abnormal patterns of activity. And these this approach has some clear advantages, such as providing a high spatial and temporal specificity, and can be based on the detection of early biomarkers.

To explain the process, more clearly, I divided in four stages. And I'm going to be briefly explaining them first and then showing some work that we have been doing in each of them. So, first we need to identify dysfunctional biomarkers. And this can be done by performing, for example, brain wide recordings of neural circuits in both human patients and animal models. And I'm going to highlight the value of this comparative approach of comparing animal models and human.

And then we would like to not only detect, not only identify these biomarkers, but to predict them. So, to be able to intervene before alterations even appear. And we have made some progress on this by leveraging machine learning approaches to the analysis of neural data sets. And then the key step is to try to correct these alterations by providing closed loop intervention.

And finally, the last step is to evaluate how this closed loop intervention actually improve cognitive performance. And again, we can start with animal models, with the goal of translating this knowledge to humans.

So, in the first part of my talk, I'm going to show how I believe brain oscillations in particular can be an excellent biomarker of cognitive decline across multiple diseases. And to make my case, I'm going to focus in only one specific type of neural pattern. These are the so called hippocampal ripple oscillations.

So, the hippocampus is this structure in the in the medial temporal lobe that has been shown to be fundamental for learning and memory processes. And its activity is characterized by the presence of those oscillations that I have an example on my screen. These are very high frequency patterns, more than 100 hertz. And they entrain the activity of hippocampal neurons and tend to appear mostly during a slow wave sleep and processing behavior.

So, a remarkable fact of hippocampal ripples that actually is common to other oscillations is that they are present across many animals, virtually across all mammals, with very similar characteristics and underlying cellular mechanisms. So, I think this highlight the point that using these type of patterns by studying them in animal models have indeed a translational potential. And there is a lot of work showing the importance of these hippocampal oscillations in learning a memory. I'm just going to highlight a couple of studies here from humans.

In this case, epileptic patients, when implanted with intracranial electrodes in the in the hippocampus to recall ripples, and they were showing a series of pictures. And after some delay, they were asked to simply recall them. So, when patients were recalling the pictures that they learned before, ripples appear in the hippocampus. And so, this result led to [unintelligible] ideas, memory recall correlate with the presence of this type of oscillations.

But, you know, complex cognitive functions such as memory recall do not depend on only one brain area, but on the activity of many circuits and many areas across the brain. So, this other study actually from the NIH recall at the same time the hippocampus and many cortical areas in human patients. And they show that during the performance of memory task, the hippocampus will produce these ripple oscillations. And they will propagate to the whole cortex and entrain activity in these regions. And this communication was essential for memory performance.

And the cellular basis of that explain why ripples have this important role in memory have been worked out by us and many other groups before in rodents mostly. Here, I bring a very simplified example. Let's imagine that a rat, for example, discover a tasty reward of a corridor. So, when the rat is running from left to right in this corridor, different hippocampus cells will be active in in succession.

So, the sequence of hippocampal cells encodes this trajectory that led to the cheese. So, when the animal goes to goes to sleep, ripples appear in the hippocampus. And the remarkable finding was that the same cells that encode this experience of running along a trajectory and discovering cheese are active during sleep in these hippocampal ripples. And even they preserve the same order of the experience. So, we believe that this sequential activation of hippocampal neurons during a sleep leads to synaptic plasticity and the formation of memories.

And in support of these hypotheses, several studies like the one I am showing here did a disruption of ripples. So, in this case, the rat was learning a special task on a maze, then went to a sleep. And then we disrupted, specifically hippocampal ripples. And what we obtained was that memory was severely impaired. So, establishing a causal relationship between this pattern of activity and memory.

And a wide variety of studies have come out in recent years showing how hippocampal ripples are impaired in different genetic models of disease in rodents from Alzheimer to epilepsy to schizophrenia, suggesting that they can be an important biomarker to predict cognitive decline in these diseases.

And I'm going to highlight just one example from our own work. In this case, we were studying a mouse that has a mutation, a chromosomic deletion that replicates a very common one found in humans that indicates predisposition for schizophrenia. It's a deletion in the 20q 11 chromosome. So, these mice, this genetic mouse model of a schizophrenia predisposition, they have memory deficits, even in very simple tasks, such as recognizing that an object has been moved.

So, they perform much worse than wild type mice. And what we found was that they do not only have these behavioral impairments, but specifically, ripple oscillations in the hippocampus were impaired. And as you can see in the blood trace, ripples were much smaller and shorter. And even more, the degree of impairment in this hippocampal oscillations correlate with the degree of impairment in memory task, suggesting that there may be a causal relationship there.

So, what I would like you to remember from this part of the talk is that brain oscillations are universally conserved biomarkers of cognitive functions. And in particular, a highlighted example of hippocampal ripples as a potential substrate for memory, and they can also be a useful biomarker. So, what I envision as a potential avenue for the future is start to try to identify similar alterations in brain oscillations that can be recalled by noninvasive or invasive means in humans as a biomarkers of cognitive decline in different diseases.

Okay. And now I'm going to show in this, in the second part, how I believe we can leverage this type of basic knowledge, to develop new therapeutic interventions. And the basic approach I am going to be defending is this on demand intervention that is based on performing in the first place recordings of brain activity can be done by any means now invasively and try to detect neural signatures or biomarkers of cognitive functions, such as, for example, will be the case of hippocampal ripples.

And then upon the detection of this neural signature, the liver intervention can be, for example, with the transcranial magnetic or electrical stimulation in a noninvasive manner. And evaluate how this intervention affects the symptoms and reiterate the cycle.

So, despite some clear advantages of this approach as a high temporal specificity, it has not been widely applied. And perhaps, the most well-known causes of success are Parkinson's and epilepsy, but not many, many other diseases. And I believe the reason for this is that for many other diseases, we lack clear functional biomarkers. And we also lack an understanding of the specific circuit and cellular basis of this abnormal activity. And perhaps more importantly, we lack tools for selectively intervening, especially in patients.

So, while there is still a lot of work to do on this sense, we have made some progress, starting with the rodents. I'm going to briefly highlight them here. So, a few years ago, we identified that when rodents were successfully performing memory tasks, this hippocampal ripples that I mentioned before became longer. And there was a very clear correlation between the length of these, these patterns of activity, and the successful performance in memory tasks.

So, we have this idea that okay, whether -- will it work if we try to artificially boost these oscillations and make them stronger, make them longer? Will that have any beneficial effect on animal performance? And to do that, we took advantage of a technique called optogenetics, which allow us to express artificial ion channels in selective neurons. And these ion channels can be -- in this case, in the hippocampus. These ion channels can be activated by light that we deliver with optic fibers implanted in the in the rat brain.

So, by doing that, we were able to detect in real time these oscillations and boost them, make them stronger, longer. And we apply that to animals, to rats, doing memory tasks. And what we found was that, yes, indeed, it improved memory performance, which was a big, a big surprise for us.

But, you know, this is only one type of animals. So, can this be applied to disease models? So, we don't know. But some promising evidence is that in all animals, all mice, and same model mice, the ripple, the hippocampal ripples are also shorter. So, we speculate that by boosting them, maybe we can also restore the cognitive deficits.

So, in the last minute that I have, I just want to highlight some future directions that I believe may be important and hopefully come to fruition in the next years. One is to try to move away from simply detecting abnormal brain dynamics, as, for example, commonly known in epilepsy with seizures, to try to predict them, to anticipate before they happen. And we have done some progress applying machine learning methods in the sense.

They also develop noninvasive techniques. Because optogenetics is very useful in rodents but can be hardly translate to humans. So, there are other means, and we have done some things using transcranial electrical stimulation and trying to focus it to a specific brain circuits. And using this circuit information derived from animal studies to inform our interventions. With the hope to develop a suite of electroceutical treatments that can be applied to human disease.

So, with that, I just want to thank you my lab members and funders, and to all of you for listening.

NICOLE PROVENZA: Hi everyone. It's truly an honor to be here. My name is Nicole Provenza, and I'm an engineer by training. I run a lab in the neurosurgery department at the Baylor College of Medicine in Houston, Texas. And I'm really excited to talk to you all today about the future of neuromodulation for mental illness. And my title side image shows an artist's rendition of the particular type of neuromodulation that I focus on, deep brain stimulation.

DBS allows patients to break through and become unshackled from the hold of mental disorders. So, similar to how pacemakers regulate electrical activity in the heart, DBS regulates electrical activity in the brain. And precise tuning of the stimulation parameters allows the electrical pulses to restore a dysfunctional circuit back to a healthy state.

DBS is commonly used to treat movement disorders like Parkinson's disease. And it's being used more and more for treatment resistant psychiatric disorders like OCD and depression. DBS for OCD is approved by the FDA under a humanitarian device exemption. And it's the only psychiatric disorder for which DBS has FDA approval.

And in OCD, two out of every three patients that undergo DBS surgery actually receive significant clinical benefit. And this is really amazing, because these are patients that have tried everything else out there, everything under the sun, and nothing has helped them before. And these are the patients that I work with the most.

So, I'm going to show you a quick video of what initial stimulation looks like in a patient with OCD. And we and others optimize lead placement in the operating room based on acute responses to stimulation. And one of the things that we often see is a description of increased energy and motivation. And this really looks different for everyone. So, I'll show you this video.

[start of video]

MALE SPEAKER: Okay. Yeah, I mean, like an 11 [unintelligible] something.

MALE SPEAKER: It's good, though, right?

MALE SPEAKER: Yes, very good.

MALE SPEAKER: What do you feel like doing?

MALE SPEAKER: Jumping out of an airplane.

MALE SPEAKER: With a parachute?

MALE SPEAKER: With a parachute, yes.

[end of video]

NICOLE PROVENZA: So, just so you know, this patient was homebound for years before having this surgery. It was like a miracle that this patient actually showed up at the hospital that day for surgery. So, as you can imagine, these effects are often really profound and exciting to see. And we actually think these acute effects are promising indicators of ventral response, but we don't see improvement in OCD symptoms per se until weeks or months after continuous stimulation.

So, this time, lag between stimulation and actual symptom changes makes tuning stimulation really difficult and high burden for clinicians and patients. So, before I talk about the future, I need to talk about the present. Currently, DBS for OCD is open loop, requiring several visits to the clinic after DBS is turned on for the first time to optimize simulation parameters. And so, patient comes into the clinic. DBS is turned on or adjusted.

Patient says, "I think I might feel better, or I feel like jumping out of an airplane." And then they go home. They might feel great for a while. Over time, their symptoms, you know, reemerge, or maybe they're always there. They go back to the clinic weeks to months later, and they don't feel better anymore. And so, what we're proposing is that automatic detection of mental states related to symptoms and side effects would enable data driven intervention.

And so, what would this look like? Modern DBS devices can record neural activity from the brain during ongoing stimulation. So, we can continuously collect both neural and behavioral data at home, process the data and maybe send an alert to the clinician when it's time to check in with the patient. Maybe they need to give the patient a call. Maybe a medication or therapy augmentation is needed, or maybe we can deliver an automatic stimulation adjustment.

And I think this strategy has the potential to decrease patient and clinician burden and improve outcomes by better managing symptom fluctuations. So, what do we need to do to get here? I'm going to cover three broad themes that I think themes -- are themes of this session.

First, we need to better understand the brain behavior relationships underlying psychiatric disorders. So, a better understanding of these disorders would hopefully lead to biomarker identification. Maybe we could identify biomarkers that tell us when neuromodulation is working versus when it's not. Or when someone is relapsing.

And this brings me to my second bullet. This improved understanding would allow us to develop smarter, personalized therapies that improve outcomes. And we could do this by dynamically modulating the therapy to better control symptoms and side effects, or by better matching patients to therapies.

And lastly, and perhaps most importantly, we need a feature where engineers and clinicians partner together to develop neuromodulation devices that patients actually want to have. And first, I'll start by talking about biomarker discovery, where we are, and where we're going.

So, beyond obsessive compulsive disorder, DBS has been tried in a smaller number of patients with varying degrees of success in a lot of other disorders, including depression, Tourette Syndrome, addiction, and substance use disorders and eating disorders. And definitions of these various diagnoses rely on symptom phenomenology.

For example, we diagnose someone as having OCD based on the presence of obsessions and compulsions. And while these are known as the defining features of OCD, more broadly, these symptoms can be thought of a manifestation of cognitive rigidity, or put more simply, stuck ways of thinking, which can also be a prominent feature of other disorders like depression and anorexia, for example.

So, over the last 10 to 15 years, there's been a push toward conceptualizing disorders within a trans diagnostic framework. So, it's possible for several individuals diagnosed with different disorders to overlap on the same dimension. But it's also possible for several individuals with the same diagnosis to separate across that same dimension.

And so, in this example, depression can manifest as cognitive rigidity, but it can also manifest as a melancholic subtype or as flat affect. And we think knowing where individual patients lie on each of these axes is an important step toward identifying neural biomarkers, because we think there's a shared underlying neural mechanism related to dysfunction in each of these domains, regardless of diagnosis.

So, the way that these constructs are classically studied is by pairing computerized behavioral tasks with concurrent neural recordings. And we've learned a lot using these task based methods, but we don't yet know how performance on a behavioral task while someone is sitting in a computer looking at a screen translates to real world behavior.

So, over the past 10 years, the field has really been moving toward more and more naturalistic paradigms. So, instead of a task, we can have someone watch a movie, play a game in virtual reality, or we can even study behavior in the real world using data collected from wearable sensors or their phones.

And so, this is exactly what doing to identify biomarkers in patients implanted with recording capable DBS devices. We can record high quality neural data in the clinic and at home while patients are going about their everyday lives. And there have been several studies using these more naturalistic strategies in OCD over the past five years.

And really briefly, in 2021, our team, in an N=3, identified Delta band as a biomarker related to OCD distress. In 2019, before us, in an N=1, Miller et al identified gamma band as being related to provocations of OCD symptoms. More recently, the Penn group in an N=1 identified that it was actually delta theta alpha and beta activity that's related to increases in OCD symptoms.

And our colleagues at MGH, Vusani et all, and an N=2, found that actually it's alpha that correlates with OCD symptoms. And so, this is by no means an exhaustive list. But using these snapshot style, few minutes at a time recordings and small end studies, there really seems to be a lot of heterogeneity and little to no consensus about what the biomarker is for OCD. And there's clearly something more complex going on than elevated power in a single frequency band. Maybe we need to zoom out and look at longer time scales to figure out what's really going on.

And so, for this reason, our work has focused on looking at longer time scale fluctuations. So, clinical response in OCD is classically assessed using the Yale-Brown Obsessive-Compulsive scale or the Y-BOCS. And it monitors symptom activity over the past two week period. And our goal was to link clinical response to neural activity and identify neurophysiological markers of clinical status by continuously monitoring neural activity in the real world, in the background of everyday life activities.

And the real life part is really important because 99.9 percent of real life actually happens outside of the clinic. So, we set to -- we set out to identify what the biomarker is for these slowly evolving states relevant to DBS for OCD. And we conceptualized OCD as a disorder of pathologically avoidant behavior, where compulsions are an irrational manifestation of that avoidance, and appropriate levels of stimulation induce positive mood and energy effects, as you saw. And it eventually allows patients to become more approachful and less fearful of their triggers.

Overstimulation, on the other hand, can often induce significantly disinhibited behaviors. And our work was recently published in Nature Medicine. And we identified a biomarker of clinical status based on these everyday, 24/7, real-life recordings.

So, we used the implanted DBS device to record neural power every 10 minutes, 24/7. And so, here, I'm showing you days since CBS activation on the X axis and time of day on the Y axis. I have two example patients, a non-responder on top and a responder on the bottom.

And before DBS, at the vertical pink line, you can see that in both patients, there's clearly a peak in brighter blue happening sometime in the morning and a trough in darker blue happening later in the day. And we call this a neural circadian rhythm. And it's really consistent before DBS.

After DBS, in the non-responder, this pattern is extremely consistent. And it's actually stable throughout the entire yearslong monitoring period. In the responder, however, something remarkably different happened. This pattern abruptly changes and evolves into a pattern that looks completely different than the pre-DBS state. It's really high entropy and unpredictable.

And the feature that best captured this slowly evolving change is a measure of neural predictability estimated from our ability to predict future data points from past data points using a linear, autoregressive model.

So, you can see that in the non-responder, there's no significant difference in the predictability after DBS, but the predictability of the neural data is significantly reduced after DBS for the responder. And this finding held up in 12 patients. And it's completely changed the way that I think about neural biomarkers for psychiatric disorders.

This work tells us that neural biomarkers of slowly evolving clinical states relevant to psychiatric disorders might not be episodic variations from baseline, but rather features of the variation in baseline itself. This theme seems to really be picking up steam. There is work published last year by a group, Georgia Tech, Emory, and Mount Sinai, where they were looking for neural signatures of depression based on daily, real-world recordings on board DBS devices.

And they found a biomarker that evolves over the same time course as typical symptom changes, which in depression is weeks. And this work could be useful for distinguishing treatment -- for distinguishing transient distress, unrelated to depression, from depression relapse. In the case of transient distress, further intervention might not be needed. But in the case of relapse, we could use this biomarker to potentially identify danger zones to intervene before the patient descends into another depressive episode.

So, the tools exist to do these recordings, and more are coming. There's two available -- two devices that can chronically track neural data available in the U.S. today. But more of these are on the rise and being developed all over the world. And we can pair these devices with the use of synchronized peripherals and wearables that can track ambulatory behavior. And these approaches are -- relating chronic, ambulatory, neural activity with deep behavioral phenotyping, I think are only going to become more prevalent and widely used.

I want to highlight one example of how we can use passive behavioral monitoring to gage clinical status. So, this is from Justin Baker's lab at Harvard. He's using wrist accelerometry to measure activity levels in patients with depression. So, from top to bottom, he plots clinical rating, self-report, activity levels, and sleep. Red means more activity, and blue remains less activity.

So, looking at this data, it's clear when the patient's sleeping, in blue, and when they wake up, in red and orange. And what's really cool though, is that when the patient ratings start to slip into a depression state, there's a clear change in activity pattern. It's really obvious.

The blue representing the low activity periods when the patient's sleeping start getting longer, indicating later wake up times. And the red starts to shift to orange and green, indicating less activity during the day. After the depressive episode, the activity returns to baseline values. And this is just one example showing how we can use Objective behavioral readouts to inform clinical status.

The last point I want to touch on is that in order to maximize the impact that neuromodulation can have on people with mental illness, we need to increase the acceptability of the therapy. Right now, there's a tradeoff between effectiveness and acceptability, which I have plotted here on these two axes.

DBS can be really effective in treatment-resistant populations, but it's really scary for a lot of people. It involves bur holes drilled into the skull and leaves implanted into deep brain structures permanently. And even for Parkinson's disease, a movement disorder that is much less stigmatized than mental disorders, only 10 percent of candidates choose to undergo DBS for Parkinson's.

So, in general, people don't want this therapy. Noninvasive therapies such as transcranial magnetic stimulation, which involves a coil placed outside of the head, is highly acceptable because it's noninvasive, but the stimulation is less targeted than DBS. Effectiveness is really great in the short term, but durability remains a limitation.

So, our target is to optimize each of these dimensions. Maybe this involves a minimally invasive strategy. There are many companies that have sprouted up over the last couple of years that are trying to do exactly this. And I think this is the sweet spot where we're really going to be able to use neuromodulation to improve the lives of people suffering with mental illness.

And with that, I want to acknowledge my growing lab at Baylor and our funding through the NIH BRAIN Initiative. Thank you so much for your attention.

BRIELLE FERGUSON: All right. Hello, everyone. I'm Brielle Ferguson, and I'm super excited to talk to you about some work that is going to be structured a little bit differently than some of the other talks that you've heard today.

So, I'm going to be talking about the history and impact of some of the recent grassroots movements that are largely trainee led, really aimed at diversifying the sciences. And I'll also talk about how my participation in that work has really served as such an important catalyst for my own career trajectory. And I credit it so much with me being where I am today.

But for you to really understand that, I have to take you back a little bit to kind of who I am and where I came from and how I came to science and really who I thought a scientist could be and more importantly, couldn't be.

So, I grew up in a small town in central Virginia. And if you would have asked me to picture a scientist, these were the images that came to mind. I only saw scientists that look like this, whether it was in my classroom, whether it was in textbooks, whether it was in the media. And this is not a new idea. We know that you can't be what you can't see, but I think it bears repeating. Because it really does shape what you think is possible.

And so, I graduated from high school without a strong sense of direction. I did relatively well in school. And I thought people who do well in school, they go on into medicine [laughs]. I didn't know what the other options were. And with the incredibly supportive parents that I had, they didn't have the cultural access to provide exposure to other types of options for me.

So, I went to the University of Virginia thinking I was going to pursue medicine. And because of limited time, I won't take you on all of the things that happened in between. But ultimately, I was really excited by psychology and the puzzle of the human brain. But where I really found my home was neuroscience. Because I realized that it gave me the ability to not just describe but also really get under the hood and understand, down to small groups of cells, how they contribute to particular behaviors.

But -- so, I decided to apply to grad school. But as you can see with this tortuous path that was littered with many, kind of, unsuccessful completion of courses, I was a tough sell to graduate schools. And so, I was rejected by every graduate program that I applied to, with the exception of one. And to this day, I am so incredibly grateful to Drexel University College of Medicine for really taking a chance on me. Because I credit them with kind of giving me the foundation of being where I am today.

So, started in graduate school at Drexel University College of Medicine. This is me at my first poster presentation. Completed my Ph.D. in 2017 and then went on to do a postdoctoral fellowship at Stanford. And along the way, it was not lost on me that as I looked around, and particularly as I looked up, I saw no one who looked like me. And it's hard to kind of put into words the impact that that has on you, again, when you're trying to imagine yourself kind of moving on to the next career stage.

And so, I went on to Stanford, really feeling the weight of that lack of representation, but I did ultimately go on to open my own lab at Boston Children's and Harvard Medical School. And there were really two key experiences that happened along the way that I really credit with feeling like I could move on to the next step. And I'll talk about each of those.

The first was at Stanford. There -- we founded the -- or not we -- the first Black Postdoc Association was founded, and I was very lucky to be able to have access to that community and later be able to go on to lead that community. And the peer support that I found through that, making me feel like I wasn't alone, validating My experience, was really instrumental in me feeling like I could survive through the postdoc stage.

So, have to thank the Stanford Black Postdoc Association and all of the Black Postdoc Associations that have been born from this idea around the country, that are helping other people feel like they're not alone in this path as well.

The second was the pandemic. And this is not even to mention the lives lost and the physical and emotional consequences of that, but there was this separate layer that I experienced as a Black person, and particularly a Black woman, going -- moving through the pandemic.

We all remember the story of Amy Cooper who called the police on Christian Cooper, who was bird watching in Central Park. And this was just one in a series of really horrific examples of racial violence towards Black people. And the difficult thing about this or the unique thing about this is this was not new. This has been going on since the founding of America.

But what was unique about this moment is that we weren't able to turn away in the same way. We're forced to have a real conversation around it. And from something really tragic, I think there were -- something beautiful was born. So, Black Birders Week was [laughs] a week that evolved with a goal to highlight the stories of Black people to exist outdoors and affirm their right to be there without fear.

And they use the method of themed days with instructions, hashtags, and live events. And so, if you were to go on Twitter during Black Birders Week, you would see these beautiful posts of Black people out in nature. And I remember them just flooding my time on it and being so inspired by that and thinking to myself, is this something that we can do in neuroscience?

Black Birders Week kind of formalized lots of -- or kind of catalyzed forming a larger group that brought together all of these Black and X groups that existed but were not in conversation with one another. And it built upon the efforts that so many people have been doing in the past that really put us under one umbrella.

And so, as I said, I was thinking to myself, is this something that we could do in the neurosciences? And I was lucky enough to see a tweet from our Co-Founder and Founding President, Angeline Dukes, asking, "When are we doing a Black in Neuro Week," where we're we going to do something like Black Birders Week but for us.

And so, I responded to this tweet. This was on July 3rd of 2020. We met two days later. We had our first meeting and secured all of kind of the infrastructure to start planning the week. We started making official announcements in all of our individual social media pages. We were able to secure some early sponsorship that kind of started the ball rolling for future sponsors. And then just a few weeks later, we had the first Black in Neuro Week.

Now, we had the goal of highlighting Black excellence in neuroscience-related fields. We wanted to help Black scholars build community. We wanted to help them provide resources specific to Black scholars and finally, increase visibility of Black scholars to those considering coming into the field or those currently in the field. And we borrowed directly from the methods of Black Birders Week in the two weeks that had come after it, before ours, and did this across social media platforms.

And so, this is just the -- our social media posts from each day. And I won't go through all of the details. But just to give you an overview, over the course of the week, we had several live events. We had seminars. We had panels. We had podcasts. And I think what was really exciting about it is we were able to really engage the community and get everyone excited about the work.

So, it wasn't just Black scholars, it was allies. And everyone was sharing and supporting and amplifying. And at the end of that, we had built a database of over 500 Black in Neuro scholars that was searchable. And you could look for mentors. You could look for speakers. You could look for faculty candidates. And that's only continued to grow.

So, for my kind of personal experience with that, as I said, I always struggled with finding Black peer support and particularly, finding Black mentorship. And I wholly credit the community that I found through Black in Neuro in being able to identify Black people who had gone before me who were not just existing in faculty roles, but really thriving and excelling and being able to learn from them and have them validate my experience and feeling like I could take the chance in going towards the next step.

But what's happened since? So, we've kind of solidified our core goals, which is building community, providing professional development resources, and increasing visibility for Black scholars in neuro. And we target all of our programming and in my role as programming director around making sure we're meeting all of these core goals.

So, just some examples, we had many, many -- we've had many, many virtual socials that are all kind of centered around themes and sometimes in partnership with different conferences. And this is aimed at building community amongst Black scholars. Because so many of us don't know other people that look like us. We've had professional development resources that are particularly tailored towards our community.

But an important thing to highlight is, for all of these, they're open to everyone. And so, it's a resource for us and by us, but it's open for everyone to benefit from.

Finally, to increase visibility, we use our seminar series where we highlight people at various career stages and give them the opportunity to share their science and their journeys, again, to kind of validate the experience that so many of us have had. We can realize that we're not alone and that we exist and that we're thriving in these spaces.

So, what's been the impact on the community? Just an overview of some of the programming and the numbers behind it. Since we started, we've had over 110 free events. And so, I highlighted that these events are open to everyone, but they're also free. So, there are no restrictions to entry for anyone. And importantly, if you're not able to access these events live, they're all archived on our YouTube channel. So, you can go and find them as a catalog of resources for everyone at various stages.

We've had over 40 expert speakers speak across these events. We've had over 22,000 registrants at our events since 2020. And in just the last year, we had 1600 registrants. Throughout this process, though, we focused on steady, sustainable growth. And I think that's really been the key in not us just starting the work but being able to sustain the work and be able to grow the community.

So, since the beginning, we went from just a few hundred members or 500 members that are -- to over 1,000 members as of last year and then steadily growing. This community is made up of people across stages, but it's largely trainees who need this mentorship and need this peer support and need these resources. So, we're really proud of the community that we've grown. But again, we want the database of the community to be a resource for everyone.

In terms of what's next, we're excited about so many things. But some initiatives I want to highlight are our mentorship programs. So, there's been informal mentoring that's been happening throughout our community since the inception. But now, we're kind of formalizing that and building in specific programming, targeting needs of our members.

We were also recognizing that our needs are evolving as time has gone on. So, we started during the pandemic. It made sense to do everything 100 percent virtual. But now, there's a need to really grow those local communities, and we're doing that through local chapters. And so, we had a pilot chapter in New York City that started at Columbia. And we're using this as a model to begin to create these chapters or in -- at institutions and cities around the U.S. and around the world.

So, in closing, what are some lessons learned from participating in the work? As someone who has led in DEI work at every stage, I think what can happen, and I think particularly in academia, is we can think that all of the solutions have to be perfectly executed. And we can let that slow us down from doing the things that we can do at the time. We think every solution has to encompass everything, and it has to be perfectly executed. And I think that can really hinder progress.

So, don't let perfect be the enemy of the good. And When access is so severely limited, and Black and other underrepresented scholars are so kind of just starved for interaction with other people that look like them, the smallest efforts can go the longest way or the farthest.

So, here are some words from our community. "There are others just like me. My experience of interacting with zero Black neuroscience faculty at my local institutions is not unique." And being able to see the really swell of support and excitement around other Black faculty around the world is changing how people view their place in neuroscience.

And then finally, final take-home, which is inspired by Dr. Bellamy's talk, is listen to the authors. Listen to the people with the lived experience. I've been -- in each role where I've worked in kind of leading DEI efforts, I've been so excited to be in the room with people who I think can actually allow me to make a change. And so many initiatives and so many ideas just kind of fizzle out at the workshopping stage.

So, if you have the opportunity, listen to the people with the lived experience, give them the space to do the work, and provide them with the resources, if you have that capability. Because I think you'll continue to be amazed by what we can do. So, with that, I want to thank you all for your attention.

I want to tell you where you can find Black in Neuro work across all social media platforms but do most of our engagement on Twitter. I refuse to say X. You can also find us on at BlackInNeuro.com. And here's some important links if you're interested in supporting or finding that member directory, where you can look up all of our over 1,000 members. And at the bottom is where you can reach me. Thank you.

MAURA LANDERS: Thank you, Drs. Ferguson, Provenza, Fernandez-Ruiz, and Wells for your informative and thought-provoking talks. Now, I'd like to invite all -- you all back up to the stage for discussion and questions from our live and virtual audience.

I do want to point out two things. One, if you're joining us virtually, there is a Q&A function on the -- not sure what platform we're on, but there is a Q&A function. So, you can type your questions in there, and we will have someone read your question out loud here. If you're asking a question in person, please make sure to use the mic so that those who are watching online can also hear your question.

I will join -- it looks like we already have a question from Dr. Gordon. Please go ahead.

JOSHUA GORDON: Hi. Well, thank you. First, I want to thank all of you, not just for your wonderful talks, but also for the work that you've done that and that I've enjoyed learning about, not only today, but in the past. And that inspires me and many others. So, thank you very, very much. And I think we should give them all a round of applause.

[applause]

And one more note of thanks before we move -- I move on to the question, I want to thank you, Brielle, in particular, for Black In Neuro. It is a resource, and I encourage everyone to do this. For me, when I'm trying to remember my colleagues, and I can -- I can go on Black In Neuro. And there's another list of women in neuroscience called anneslist, that I use very, very frequently as I'm trying to make sure that I'm inclusive when I'm thinking about who to invite to events or to write things, et cetera.

So, it's a -- it's really wonderful resource for those outside the community as well, to make sure that we stay connected with those members of our community who belong to groups like that. It's great. It's a great resource.

My question is, you know, there are some common themes that I'd love for some or all of you to comment on about what the future looks like, right? So, when we think about the themes that all four of you talked about, we can think of diversity and heterogeneity, right? And we can think of needing to include that diversity and heterogeneity as we think about approaches that work for individuals with mental illness.

And I wonder if you -- you know, some of you talk more about future visions than others. And I just love to hear any of you who'd like to say something. What do you hope the world of mental health research and perhaps mental health care looks like in another 25 years? I know some of you included that; some of you didn't. So, I'd love -- just love to hear that -- those thoughts.

MICHAEL WELLS: I'll be quick so that everybody has a chance to talk. MM my hope is that we find, you know, treatments for these conditions that benefit all of us. And I think that we're obviously not there yet. And I think it really starts from the ground up, biologically, looking at the genetics, the cellular, molecular mechanisms, going up to circuits and up to individuals. Making sure that our samples represent the full diversity of the human species, I think, is the only way we can achieve this goal of treatments that work for everybody.

NICOLE PROVENZA: We talked a lot about biomarkers today. And I hope for a future where, you know, like, if you break your arm, an x ray can tell you that the -- where it's broken. And for people suffering with mental illness, I think, a lot of times, they are kind of gaslit into believing, like, oh, it's in your head. It's your problem, you know, just like, get over it.

But what if we had biomarkers kind of validating lived experiences, like validating, yes, this is a mental disorder. There is a circuit dysfunction going on in your brain, and here's what we can do to fix it. I think that would help a lot of people.

ANTONIO FERNANDEZ-RUIZ: So, okay, maybe just to bring up a different angle. So, I think part of the limitation, perhaps historically, in the development on treatments for mental health has been the lack of understanding of the underlying causes. And I think that's complicated for complex human diseases, but we now have much better technology that only 10 years ago.

So, I do see, you know, the technology is already very advanced. So, now we need to catch up with new ideas of looking at this massive data set that we are collecting, both from the, you know, genomic, atomic point of view, to the functional and imaging. So, we do need new ways of thinking about the data, how we even start to make sense and look for patents in the data that can help us going into the mechanisms. And how can we translate them from, you know, animal models, up to human patients? And that's a challenging role.

MAURA LANDERS: Would you like to --

BRIELLE FERGUSON: Sure. I don't have much to add. I agree with everything that's been said there, and I didn't talk about this in my talk. But I definitely agree with the approaches that are being used. Can we use the kind of like heterogeneity of these experiences to identify kind of like shared, conserved biomarkers, whether it's at the genetic level, at the cellular level, at the circuit level, that we can use?

I think we can take a kind of a level higher and say, at the symptom level, what are symptoms that are shared across many of these different disorders? And so, we heard about cognition. I focused specifically on attention, and attention is shared across all of these disease states. So, what can we learn about attention and how attention malfunctions in a way that can inform other behavioral dysfunctions and other diseases?

MAURA LANDERS: Thanks, everyone. It looks like we might have another question. Yes? Over here?

DAVID DELAHUNT: Can you guys hear me? Thanks so much for all your work. I'm Dave Delahunt. I'm an advisor to companies in the in the mental health diagnostics and therapy space. All right. There we go [laughs].

And some companies I'm working with are using epigenetic biomarker technology spun out from Rachel Yehuda's lab, as well as Dr. Bob Niculescu -- you might have heard of -- at Indiana University. So, we'd love to hear any your thoughts on the potential for precision medicine.

Most of my background is actually in oncology precision medicine, and that's been very successful there. And I'm really interested and excited about the potential for advancing precision medicine in in neuroscience and psychiatry. And the, you know, the epigenetic pathway has been very successful for -- in the -- applied in the oncology field. So, we'd love to hear any thoughts you have on epigenetic biomarker applications. Thank you.

MICHAEL WELLS: Everybody's handing me the microphone. One thing I didn't talk about is how, with our villages, we are starting to incorporate epigenetics, so that we're capturing gene expression and epigenetics from the same exact cell for these assays. So, we are starting to work on that. That's just commercially available. Those kits are commercially available. We don't have to do anything special to do that.

Yeah. So, you bring up oncology, which I appreciate it, because there's many examples of clinical trials being successful in oncology because they either had a molecular or genetic biomarker for either the tumor or for the individual themselves. That would be great to have, I would say, for psychiatry. We don't really have that.

I alluded to this idea of, if we had a biomarker for drug efficacy, for whatever thing you're trying to get through the clinical trial, how beneficial something like that could be. Because there's often clinical trials that fail even though they helped some people. And what inspired a lot of the work we do is a story I read in some -- maybe Washington Post or something, in 2017.

It was a story about a drug that failed for fragile X. And what was heartbreaking about it is that some of the families were saying to the doctors, "This drug helped my 15-year-old son speak for the first time." But because not enough of those cases took place, the drug failed. And they were not allowed to give that drug to their child anymore.

If we had something to identify who would have -- whether it be epigenetic or genetic -- who would have responded positively to that, it's unlikely that clinical trial would have failed. And that family might be able to benefit from that treatment. I think it actually spawned legislation around the right to try, even if something is not FDA approved. So, my hope is that we don't have future scenarios in which people are being given treatments that work in a clinical trial, and they can't actually take it anymore because of the lack of these sort of biomarkers.

NICOLE PROVENZA: I'm very enthusiastic about precision medicine in terms of neuromodulation as well. I don't know much about epigenetics, but I know that, like DBS for depression, for example, patients have to jump through many hoops. You know, they fail so many medications, fail expert -- like multiple rounds of expert psychotherapy, electroconvulsive therapy, which works for some people, but causes severe like, memory loss and many others, before they can even qualify to get deep brain stimulation.

And so, if we could have, you know, some biomarker that could tell us, like, okay, you failed SSRIs. You know, it looks like you're going to fail everything else. Let's skip to DBS. That would help a lot. So, I think accelerating, like the path to finding the right treatment is very exciting.

MAURA LANDERS: Hopefully, we could be talking about that, how well we did it at our 100th, looking back. On that note, if anybody has other things they would like to share, I'm going to give you three seconds, really quickly. Because we are standing between everyone and lunch. So, I'm not getting any eyes. I'm going to say once again, thank you all for your presentations. They're very informative, leave us a lot of things to think about.

[applause]

Okay. I have a few housekeeping items, and then I promise you can get to lunch. So, before lunch, we ask all speakers and the top -- NIMH top five to please remain in the auditorium for an event photo. All participants are invited to join us for a variety of box lunches, which have been provided by the Foundation for the National Institutes of Health.

Please retrieve your lunch from the lobby. You can eat your lunch in the lobby area or the cafe. There are some tables there. Food and drinks are not allowed back in the auditorium. If -- we encourage everyone to remain inside the building. I know it is gorgeous outside. I would love to go outside.

Please be aware, if you do go outside, you'll have to come back in through security. I've not been out there. I don't know what it looks like. But if there's lots of foot traffic, that could take some time to get back inside. So, just be mindful of timing. I know I'm a few minutes over, so I apologize for time.

Please make your way back to your seats when you hear the time's around 12:35. Thank you all again for an engaging presentation. I look forward to talking to you all at lunch. I won't talk your ear off. But enjoy lunch, everyone.

[applause]

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