Mariana Lazar, PhD, associate professor of radiology at NYU Grossman School of Medicine, leads a research group that develops and applies neuroimaging techniques to explore phenomena that accompany and underlie a range of psychiatric disorders. Her investigations into aspects of autism, schizophrenia, and psychotic spectrum disorders have been supported by awards from the National Institute of Mental Health. In a study published on September 7 in Molecular Psychiatry, Dr. Lazar and coauthors find a relationship between the amount of iron present in the brain’s subcortical regions and the severity of symptoms in early psychotic spectrum disorders. Our conversation has been edited for clarity and concision.
How would you describe your research in broad terms?
My work focuses on developing methods for brain imaging with the purpose of applying them to disease in order to better understand what brain deficits characterize different disorders and to understand their mechanics. Some of my focus is on psychotic spectrum disorders, which include schizophrenia and other conditions that may have psychotic features, for example bipolar disorder.
So you’re trying to understand two types of issues: brain deficits and mechanisms that underlie them.
Yes, by deficits I mean things that go wrong with the brain. In my lab, we usually study this by comparing groups of people who may have a disease with healthy groups.
With mechanisms, it’s a bit more complicated in that we’re trying to see what leads to the deficits. Sometimes, we discover changes that occur in the brain as the disease progresses. In that sense, studying mechanisms is different from the study of deficits.
What led you to develop an interest in applying imaging tools to this particular set of medical problems?
I think it started with my PhD. I did method development for tractography and created an algorithm to map white-matter connections. At the time, I thought that such methods would to be very useful in psychiatric disorders and conditions related to aberrations in brain connectivity. But I also felt that I couldn’t just stop at putting a method out there. I became more interested in seeing whether a method is useful in understanding particular disorders—this type of research can help patients even more directly, and that is very motivating for me.
What sorts of challenges do you face when conducting research with this patient population?
These are very serious disorders and can impact the life of young people tremendously. Symptoms typically start in late teens and young adulthood and can basically stop people in their tracks, preventing them from finishing school or developing relationships. But it’s a great population to work with. Study participants have been very generous with us, and I find this work very rewarding.
The challenge sometimes is recruiting people. But for someone who’s quite young, it’s not easy to be diagnosed with a disorder, come to terms with it, and then also volunteer for research studies. With chronic patients, who are older, recruitment tends to be a bit easier.
Have you found any particular methods of outreach to be more effective than others?
We’ve been using multiple ways to try to reach people who may have these disorders and it hasn’t been easy, but I think we’ve been successful. We advertise in the community, we work with NYU Langone’s psychiatry department, and also use resources like NYU Langone’s data core—a system in which, based on an approval by the institutional review board, a search of patients’ medical records can identify people who meet study inclusion criteria and have already indicated interest in participating in research. That’s in terms of schizophrenia.
More recently, we started doing studies in aging, and the Alzheimer’s Disease Research Center has been extremely supportive in recruiting for us. That’s been wonderful—having that resource at NYU Langone is a tremendous asset.
How do you identify the imaging methods that you think may be appropriate to a research question you’re posing? Also, what would you say is the mix of technical development you may be doing versus reliance on existing techniques?
We always have our set of tools that we’re very experienced with, but at this point I’m to a large degree driven by the scientific questions we’re trying to answer to understand the disease.
Often, our method development is driven by trying to solve questions that the field has about certain disorders and also trying to understand better our own data, because sometimes you come with a set of hypotheses and they’re all nice and neat, but then you start to apply them to the data and nothing comes out the way you thought. This happens often, and then you ask why and try to understand what’s really going on.
That has led us, for example, to our recent paper in Molecular Psychiatry. To start with, we were looking at myelin. We had a study of chronic patients with schizophrenia, and our data showed what we thought was dysmyelination. So we started a new study on a younger population to further test and confirm the dysmyelination hypothesis. But then it turned out that the younger patients were quite different from the older group. In the older group, when we look at differences in some of the diffusion metrics indicative of deficits in myelin, the differences were striking. In the younger people, there were barely any differences.
What sort of age groups are we talking about?
The brain develops up to about age 30, then it has a bit of a plateau, and around 40-50 it begins aging. We didn’t want results to be confounded by either development or aging, so we chose the most stable period: middle age, approximately 30-50. In people who have had the disease for quite a number of years, myelin deficits are obvious. We hypothesized dysmyelination in the younger group, too, but the differences are not so apparent. That discrepancy led us to look at disease progression and yielded interesting findings.
As the disease progresses the deficits deepen, but some of the prefrontal cortical regions show maladaptive, increased myelination. In subcortical regions, we didn’t see any differences in macromolecular proton fraction (MPF)—a metric specific to myelin—but we did see strong differences in R1, which describes both myelination and iron. So we thought maybe it’s iron. To investigate further, we extended existing methods and developed our own estimate of R2*—a metric indicative of iron—based on the data we had already collected.
This is why the paper calls it a synthetic measure.
Yes, because we created a model of R2* as a function of R1 and MPF, validated this model through a set of separate experiments in which we acquired all the metrics directly, and then applied it to our preexisting data.
So you ran a mini study in service of your primary study.
Yes, and the model worked really well. We saw significant differences in young adults with psychotic spectrum disorder—particularly the participants with schizophrenia or schizoaffective disorder—in the amount of iron present in subcortical structures. That’s quite interesting and, in my opinion, a very exciting finding in terms of potential for treatment.
Schizophrenia and psychotic disorders have a number of so-called positive symptoms, like hallucinations and delusions. But there are also negative symptoms like cognitive disfunction or the “flat effect,” characterized by showing little emotion. Cognition in particular has been shown to be predictive of patients’ functional outcomes like having a job, having a relationship, and so on.
In our study, we find an association not only between subcortical iron and positive symptoms but also between subcortical iron and cognitive function: the less iron the more positive symptoms; the less iron the worse the cognition. So if iron deficit is an underlying problem, perhaps down the line something like iron supplementation may improve these conditions—that would be a totally new treatment.
Hypothetically, if medical researchers were to test an iron supplementation therapy for schizophrenia, would doing so be as simple as giving oral supplements or would getting the iron into the subcortical areas be quite complicated?
That’s a good question. Iron is very highly regulated in the brain and less iron during development is known to affect cognition and behavior—we know that from many longitudinal studies, not necessarily in brain imaging, but in populations in areas where iron deficiency is a problem. At the same time, too much iron is also bad for brain function. So we need to be careful and make sure that iron supplementation is done in a way that doesn’t lead to excess.
It might be the case that if we use iron supplementation, iron content in subcortical structures will increase—perhaps depending on the developmental stage at the time of intervention. At the same time, iron might be used to catch up on some other deficits. We do plan to conduct intervention studies in collaboration with Donald Goff, MD, [who is a coauthor on the iron study] in the psychiatry department. But I think we’d be very wise to look at brain microstructure and function more generally—not just at iron per se—because those might change first.
The lead author of this paper—Yu Veronica Sui, MPhil—is one of your students. In April, another student you advised in the biomedical imaging and technology PhD program defended her doctoral thesis. What is it like to lead young scientists who are working toward a PhD and trying to contribute to the field?
As a kid, I wanted to be a teacher, so sharing what I know with other people is very fulfilling. It’s really exciting to work with graduate students. Building a scientific family is one of my career goals.
Can you say more about the idea of a scientific family?
When you have your students, and down the line they have their own students, that’s a kind of scientific family. When I first met the PhD advisor of my PhD mentor, he basically told me that he’s my scientific grandfather—that was really nice and generous of him.
I’m always interested in interacting with people and building relationships, so working with students is a part of that. Doing all these application studies where I work with a lot of patients and often go the MRI scanner and meet the study participants, I think it’s something that really works for me. During my postdoctoral training I did method development—it was just me, my computer, and a few people from the group in an office. I felt that was a bit too restrictive and in some sense a bit claustrophobic—I don’t know whether that’s the right word, but I felt that I needed more interaction with the world.
Medical imaging, psychiatry, neuroscience—these are very complex fields. What would you say to people who may be interested in stepping onto a scientific path in these domains but are not yet doctoral candidates or graduate students, and may not know where to start?
These are very interesting and rewarding fields. Just go for it. Learn as you go, because you’re never going to be fully prepared for anything, so you cannot wait until you feel ready. You need to start doing the work. When you do the work, you’re going to learn. You’ll be highly motivated because you’re trying to solve a problem—if you need to know something, you’ll read the right books and find out. I think that’s the best way to do it. So my advice for young people is just get involved in a research project and use your problem-solving skills in a new environment, building on the knowledge that you have.
Do you have an overarching goal that you think of as you choose what problems to investigate? Is there some horizon that you imagine your research is tending toward?
There’s the overarching goal of trying to better understand disease in the hopes of informing new treatments. Another goal, particularly in some of these psychiatric disorders where treatment nowadays is a bit of trial and error—because we don’t yet have all the tools to understand what’s happening—is to have biomarkers that would tell us what the best treatment for a certain person is. Having imaging tools that can do that is I think a very longterm prospect.
Currently, available diagnostic tools for conditions like schizophrenia and the disorders that you study are primarily behavioral tests. Those are more subjective ways at arriving at a diagnosis. What is your sense of how far our knowledge and imaging technologies are from providing more objective alternatives?
Yes, to some degree behavioral evaluations obviously are more subjective. Also, not every treatment works in everyone—it always needs to be adjusted to the patient in a more empirical way, so it would be nice to have measurable indicators of what treatment would work best. It’s not easy to reach that point, but that would be a goal. I’m an optimist, and I do hope that’s going to happen in the next decade or two.