A conversation with Dr. Anqi Qiu (OHBM 2025 Keynote Interview Series)
Authors: Rujing Zha, Xuqian Michelle Li
Prof. Qiu is a global STEM scholar, Professor at the Department of Health Technology and Informatics at the Hong Kong Polytechnic University, and an Adjunct Professor at the Department of Biomedical Engineering at Johns Hopkins University. Her past roles include Deputy Head for Research & Enterprises at the Department of Biomedical Engineering and Director for the BME Innovation Center at the National University of Singapore’s Suzhou Research Institute. Specializing in computational analyses, Dr. Qiu is deeply committed to understanding the origin of individual health differences throughout the lifespan. Her team develops new technologies in statistical and deep learning-based medical data analysis to leverage complex and informative datasets that include disease phenotypes, neuroimaging, and genetics to further her research.
You can also read our interview with Dr. Qiu on becoming a OHBM fellow last year.
Rujing Zha [RZ]: To start off, could you tell us a bit about your lab — what is it like, and who makes up your team?
Anqi Qiu [AQ]: My lab is highly interdisciplinary, bringing together diverse expertise essential for neuroimaging research. Three of my team members are trained medical doctors—radiologists and neurologists—currently pursuing PhDs or postdoctoral fellowships. Others come from backgrounds in psychology, neuroscience, engineering, and mathematics. This diversity reflects the complexity of the field and strengthens our ability to tackle multifaceted research questions.
RZ: You originally trained in Electrical and Computer Engineering at Johns Hopkins. How did you move into neuroscience, and how do these two fields intersect in your work?
AQ: During my master’s in biomedical engineering, I worked with a supervisor conducting neuroscience research using animal models. His focus was on functional organization of the brain, particularly the auditory system. At the time, our tools were quite limited—we used single electrodes to record activity from specific brain regions while presenting auditory stimuli. The signals were extremely noisy, so I spent a great deal of time processing them, trying to isolate activity from individual neurons. That was my first hands-on experience with neuroscience.
It was fascinating, but the work was narrowly focused on a single pathway. Over time, I became increasingly interested in studying the human brain. This motivated me to pursue a PhD in human brain imaging, where I shifted back toward engineering, applying computational methods to analyze structural MRI data. In the early 2000s, imaging quality was still relatively poor, so much of my work involved developing mathematical models to characterize brain morphology and shape from T1-weighted images.
Later in my PhD, I began collaborating with psychiatrists and neurologists at Johns Hopkins. We applied those modeling techniques to clinical datasets, investigating conditions like aging and ADHD. That experience truly bridged the engineering and neuroscience aspects of my training—and that integrative approach continues to shape my work today.
RZ: Your research has shed light on how maternal mental health during pregnancy can shape early brain development in children, particularly in areas like the hippocampus and functional brain networks. How did you come to focus on this area, and what continues to drive your interest in these questions?
AQ: During my research in Singapore, I joined a pregnancy-birth cohort study that was initially designed to investigate metabolic health, not brain development. I proposed incorporating brain imaging into the study, which led to the launch of our neonatal imaging work in 2009—well ahead of its time.
Our central question was: when and how do mental health problems begin? Brain development starts as early as the fetal stage, and emerging evidence had linked maternal mood during pregnancy to increased risks for neurodevelopmental conditions such as ADHD and autism. However, the mechanisms underlying these associations remained unclear.
Neonatal imaging offered a critical advantage. By scanning newborns within the first week of life, we were able to capture the effects of prenatal influences without the confounding impact of the postnatal environment. This provided a unique window into how maternal mental health during pregnancy might shape early brain development. We have since published extensively on how prenatal maternal mood affects neonatal brain structure and function. With longitudinal follow-up data now extending to age 10, we can track developmental trajectories over time.
What’s particularly exciting is our recent expansion into biological sampling during pregnancy—including blood, saliva, and placental tissue. These samples allow us to investigate the biological pathways linking maternal mood to brain development with far greater precision.
It has been a long and evolving journey. For me, the pregnancy period is especially compelling because the connection between mother and fetus is direct: hormones, nutrients, and stress signals are transmitted via the placenta, unlike in the postnatal period when infants are already engaging with the external world. I believe these early exposures can have particularly deep and lasting effects—and this belief continues to drive my research today.
RZ: Building on that, what is the most exciting project your lab is currently working on?
AQ: Diagnosing conditions like ADHD or aggression before age six is extremely challenging—unlike autism, which often presents more clearly in early development. Young children change rapidly, their behavior is highly variable, and clinical assessments rely heavily on parental reports, which can be subjective. So while we know that many mental health issues originate early, we often miss the optimal window for intervention.
Our goal is to develop more objective and scalable methods to estimate risk—not necessarily to diagnose, but to flag children who may benefit from early support. To that end, we use neonatal brain imaging, and in the future, we hope to incorporate genetic and other biological data as well.
In one of our earlier studies, we applied an unsupervised machine learning approach to neonatal MRI data. Instead of predicting outcomes directly, we clustered the scans based on brain features and then tracked behavioral outcomes at ages two and four. Interestingly, one small subgroup—about 10%—consistently showed the highest scores on behavioral problems. This kind of approach could be especially powerful in high-risk populations, offering the opportunity to intervene early, before problems become entrenched.
At the time, the paper faced resistance—some reviewers preferred a direct prediction model. But I still believe our approach is more realistic for clinical settings, where outcome data are often incomplete or unavailable at the start. What excites me most about this work isn’t just the methodological innovation, but its translational potential. I’m at a stage in my career where publishing another paper is no longer the primary goal—I want to develop tools that can genuinely make a difference in children’s lives. That’s what drives this project forward.
RZ: You’ve mentored many students and postdocs over the years. What advice would you give to early-career researchers who are just beginning their journey?
AQ: The journey in research is never easy—and that’s true for everyone. We often hear about the successes, but the reality is that every PhD student, postdoc, and even senior researchers face challenges constantly. There’s no such thing as a smooth, linear path in science.
For early-career researchers in particular, I believe the most important qualities are consistency and persistence. I often tell my students: if something isn’t working, don’t give up too quickly. Let’s talk about it. Try to convince me why it won’t work—or let me convince you that we just haven’t figured it out yet. That kind of back-and-forth is part of the process. It’s absolutely okay to pivot or change direction, but only after you’ve made a sincere effort and reflected on why something isn’t working. Developing that kind of patience and self-awareness is essential.
Research can be stressful, so it’s equally important to learn how to take care of yourself—to find ways to relax, recharge, and stay motivated. These are lessons we each have to discover for ourselves, but they are just as important as the research itself.
Xuqian Michelle Li (ML): And finally, as one of the keynote speakers at this year’s OHBM meeting, could you share a bit about your journey with the conference and what it has meant to you over the years?
AQ: I first attended OHBM as a PhD student in the early 2000s — one of my advisors encouraged me to go. I’ve loved it ever since, and have only missed a few meetings in the past 20 years.
In those early days, I especially enjoyed the poster sessions. Our lab’s posters were often grouped together in a single row, creating a kind of informal hub. You’d see your labmates and friends, present side by side, and get a real sense of what each group was working on. I still think that was a fantastic way to organize things, even if it's harder to do now.
What I’ve always appreciated about OHBM is the breadth of topics. Early in my career, it gave me exposure not just to neuroscience, but also to the technical underpinnings—imaging physics, acquisition methods, preprocessing—things we often take for granted now. I learned about everything from diffusion MRI to task-based fMRI, from clinical applications to cognitive neuroscience. Even today, you can leave the meeting with ideas spanning genetics, computational modeling, and translational science.
OHBM has been a meaningful part of my professional journey. I’m deeply grateful for all I’ve learned through the years—and for the strong sense of community it continues to foster.
