8 Big Questions Rice Is Asking about the Brain
... and how the answers could change the world.
Why study the brain?
By Sarah Rufca Nielsen
The brain is both essential and elusive. It governs movement, language, emotion and decision-making, but it is also one of the least understood organs in the body. When it begins to fail — through Alzheimer’s disease, depression, anxiety, traumatic brain injury, or a host of other neurological and psychiatric conditions — the effects are rarely neat or contained. They unfold across cognition, behavior and personality, often altering not just health but the very sense of self.
And the scale of that disruption is growing. By 2050, nearly 130 million people worldwide are expected to be living with dementia. Mental health conditions are rising across age groups, shaping how children learn, how adults work and how people age. These issues sit at the center of modern medicine’s biggest challenges — and some of its biggest unknowns.
But the real story doesn’t stop at the hospital. Brain health shapes how people learn, how they work, how they relate to one another and how they participate in society. That’s why researchers increasingly talk about “brain capital” — the combination of brain health and the cognitive skills that make modern life possible: adaptability, creativity, attention, problem-solving.
At Rice, that idea is reflected in a deeply interdisciplinary strategy around brain science and brain health. The Rice Brain Institute, launched in 2025, brings together more than 65 faculty members across engineering, natural sciences, social sciences and policy to study the brain from molecules to behavior and from laboratory discovery to large-scale intervention. Its three pillars — neuroscience, neuroengineering, and brain and society — reflect the view that no single field can solve the challenges of brain disease on its own.
By 2050, nearly 130 million people worldwide are expected to be living with dementia.
“Brain health is one of the defining challenges of our time — not only for individuals and families, but for the long-term strength of our economy and society,” Rice President Reginald DesRoches says. “At Rice, we are uniquely positioned to lead because of our ability to bring together world-class research, data science and policy expertise in one place. Through partnerships and initiatives like DPRIT and Project Metis, we are not working in isolation — we are building a collaborative ecosystem that accelerates discovery, translates insight into action and ultimately delivers impact at a scale few institutions can match.”
The RBI’s goals include reducing mortality from neurodegenerative diseases, lowering rates of mental health disorders, improving quality of life for people with brain injuries and expanding opportunities for individuals with neurodevelopmental conditions. Its first round of seed grants supports projects aimed at controlling seizures without surgery, developing a blood test for patients at high risk of brain hemorrhage, improving outcomes in brain tumor surgery and creating longer-lasting treatments for abnormal blood vessels in the brain. These are the kinds of advances that can change clinical care one patient at a time while also building the scientific foundation for broader impact.
“Brain health is deeply personal to me — my mother lived with dementia in the final years of her life, and my grandfather lived with it for almost a decade,” says Amy Dittmar, Rice’s Howard R. Hughes Provost and executive vice president for academic affairs. “I know how profoundly neurological diseases affect not only the individual but the family and caregivers who support them. For me, that experience brings both urgency and clarity to this work.”
It goes without saying that brain research is about care: earlier diagnoses, better treatments, fewer invasive procedures, improved recovery. But brain health is not only a medical priority, it’s a social and economic one. Brain disorders cost the global economy an estimated $5 trillion each year, with projections climbing as high as $16 trillion by 2030.
That’s why the Global Brain Economy Initiative, which Rice unveiled in January at the World Economic Forum in Davos, Switzerland, aims to position brain health and human cognitive skills at the center of global economic development, especially as artificial intelligence transforms work and as societies confront the realities of aging.
“Making meaningful advances in a topic like brain health requires connections that span far beyond the boundaries of traditional academic disciplines,” says David Sholl, Rice’s executive vice president for research. “Rice’s amazing research strengths across science, engineering, the social sciences and beyond give us a wonderful opportunity to lead in this important area.”
Brain disorders cost the global economy an estimated $5 trillion each year, with projections climbing as high as $16 trillion by 2030.
If scientists can better understand how attention develops, how stress affects cognition or how environments shape learning, that knowledge can influence how schools structure curricula, how cities design public spaces and how governments allocate resources for mental health services. It can lead to policies that prioritize early childhood development, reduce cognitive strain in high-risk professions, and expand access to interventions that support emotional and psychological well-being.
“The human brain is one of our most critical assets, but we are only just starting to understand how it truly functions and how our environment or systems impact the brain’s health,” says Harris Eyre, the Harry Z. Yan and Weiman Gao Senior Fellow in Brain Health at Rice University’s Baker Institute for Public Policy. “When we invest in brain health and brain skills, we contribute to long-term growth, resilience and shared prosperity.”
To study the brain, then, is to engage with a problem that is at once biological, psychological and social. Because it is where health and identity meet. Because its disorders are among the most complex — and most personal — challenges we face. And because understanding it offers something bigger than treatment — it gives us a road map for building systems that actually support how people live.
What happens inside the brain as creativity unfolds?
By Sarah Rufca Nielsen
For centuries, creativity has carried an aura of mystery — something spontaneous, intuitive and difficult to pin down. But in the Music, Mind and Body Lab at Rice, researchers are beginning to map what happens inside the brain as imagination unfolds in real time.
The lab, a collaboration between the Shepherd School of Music and the Medical Humanities Research Institute, brings artists and scientists together to study music and creativity as lived, embodied experiences. Instead of asking performers to complete simplified tasks in tightly controlled settings, researchers design experiments around real performance, capturing the neural dynamics of creativity as it happens on stage.
Their most ambitious project so far is “Free Rein,” a collaborative performance merging dance, music and neuroscience that premiered in January 2026 and turned a dance performance into a neuroscience experiment. Created by Anthony Brandt, professor of composition and theory at the Shepherd School, with NobleMotion Dance, Musiqa and multimedia artist Badie Khaleghian, the 35-minute work featured five dancers and four musicians moving between fixed choreography and improvisation.
During performances, two dancers and one musician wore wireless mobile brain-body imaging equipment, allowing researchers to track neural activity as the performance unfolded. The goal was to identify the brain’s signatures of creativity — the patterns that emerge when artists improvise rather than execute a rehearsed sequence.
Across nine performances, “Free Rein” became one of the most extensive neuroimaging studies of creativity ever staged. Real-time projections translated neural data into visual imagery, giving audiences a glimpse of the brain at work during acts of creation.
Real-time projections translated neural data into visual imagery, giving audiences a glimpse of the brain at work during acts of creation.
Earlier Music, Mind and Body Lab projects have also pushed performance into the realm of experiment, including concerts in which pianist Chelsea de Souza ’24 performed improvised and prepared variations on classical and jazz themes, and collaborations in Bali where dancers and musicians wore mobile brain-imaging equipment while rehearsing and performing new work in the traditional gamelan style.
Performance-based experiments introduce a degree of unpredictability that traditional laboratory settings avoid. But Brandt argues that the trade-off is essential if scientists hope to understand creativity in its natural environment.
“Scientists often want the music to be well behaved,” he told The New York Times. “But music isn’t designed to be tame. It’s meant to be a representation of human expression in all its full-bodied glory.”
Can a simple blood test map the living brain?
By Silvia Cernea Clark
For decades, understanding what genes are doing inside the living brain has required difficult trade-offs. Imaging techniques offer indirect clues. Tissue samples provide detail but capture only a single moment in time. Researchers have long sought a way to track the brain’s molecular activity continuously without invasive procedures.
Rice bioengineers are working toward that goal with an approach that may sound surprisingly simple: a blood test.
Their work centers on released markers of activity, or RMAs — engineered proteins produced by targeted brain cells. These synthetic markers are designed to cross the blood-brain barrier and circulate in the bloodstream, where they can be detected through a routine blood draw. Each marker corresponds to gene activity in specific neurons, offering a potential window into the brain’s inner workings without surgery or complex imaging.
But early versions of the technology had a limitation. Once released, RMAs lingered in the bloodstream for hours, creating background noise that could obscure new signals.
The Rice team devised a solution that functions like a molecular reset. In a study published in Proceedings of the National Academy of Sciences, researchers engineered RMAs that can be erased inside the bloodstream. A targeted enzyme — acting like molecular scissors — cuts the markers apart, clearing old signals and allowing scientists to measure new changes more precisely.
“The key advance here is a new way of thinking about serum markers — that we can modify them inside the bloodstream when we need to,” says Jerzy Szablowski, assistant professor of bioengineering at Rice and a corresponding author on the study. “This broad concept has many potential applications, ranging from extending the marker’s half-life to improve detectability, or erasing them to remove the background signal and improve temporal resolution. Currently, markers are usually extracted from the body and interpreted ‘as-is,’ which limits their usefulness.”
For neuroscience, the promise is straightforward: the ability to watch the brain change over time.
In animal models, a single injection of the enzyme removed about 90% of the background signal within half an hour. With the signal cleared, researchers could detect subtle changes in gene expression that had previously been difficult to observe.
The technology builds on earlier work showing RMAs function not only in mice but also in monkeys — an important step toward clinical use.
“Our study shows it is fairly easy to translate this noninvasive technique between species,” Szablowski says. “This is exciting because RMAs are an extremely sensitive tool that could be used to track as few as tens to hundreds of neurons at a time with the potential for highly multiplexed readout — no existing noninvasive imaging or monitoring technique can give us that capability.”
For neuroscience, the promise is straightforward: the ability to watch the brain change over time.
“In brain research, longitudinal monitoring is especially important,” Szablowski says. “To understand conditions like addiction, you need more than a single snapshot of the brain. We need to see the movie, not just a photograph. Tracking the living brain over time lets us watch which genes drive these changes as they happen.”
Could soft implants change how we detect and treat brain cancer?
By Silvia Cernea Clark
For patients with glioblastoma, the most common and aggressive form of adult brain cancer, time is often measured in weeks and months. After surgeons remove a tumor, clinicians typically rely on MRI scans every two months to see whether the cancer has returned. But by the time imaging reveals new growth, the disease may already be advancing.
Rice assistant professor of materials science and neuroengineering Christina Tringides is working on a technology that could change how that monitoring happens — and potentially how the disease is treated. Her team is developing Conductive Hydrogel Arrays with Multiple ELEctrodes Optimized for Neurons, or CHAMELEON — soft, sensor-filled brain implants designed to monitor glioblastoma from inside the brain.
“My group works with hydrogels, very soft materials that match the properties of the brain exactly,” Tringides says. “Most existing implants are made from rigid materials like the ones used in regular electronics, which do not embed well with the brain and can cause tissue damage or stop working over time. Our goal is to develop implants that work seamlessly with the tissue.”
By embedding carbon nanomaterials such as nanotubes and graphene flakes into the gels, Tringides’ lab creates flexible, conductive electrode arrays that can drape over — or even flow into — delicate brain surfaces without damaging them. Implanted in the cavity left after tumor removal, the devices could track electrical signals associated with cancer progression in real time.
Beyond monitoring, Tringides’ team hopes to one day integrate drug-delivery nodes directly onto the electrodes, allowing clinicians to both detect disease activity and deliver therapies using the same platform. If successful, Tringides’ soft implants could offer clinicians a new way to watch — and respond to — one of the most difficult cancers to treat.
What can LLMs teach us about our own brains?
By Silvia Cernea Clark
Large language models — the artificial intelligence systems behind tools like chatbots and automated writing assistants — are built to predict and generate language. Trained on vast collections of text, they learn patterns in how words relate to one another and can produce responses that often feel strikingly human. For scientists studying the brain, that capability raises a provocative question: What can these systems reveal about how people think?
Benjamin Hayden, professor of neurosurgery at Baylor College of Medicine and adjunct professor of electrical and computer engineering and linguistics at Rice, believes the comparison could offer a powerful new window into human cognition. Though artificial and biological intelligence operate very differently, the fact that both systems produce language makes LLMs a useful tool for investigating how the brain organizes meaning.
“LLMs are basically computers that generate language,” Hayden says. “Remarkably, when you look inside of them, you see a lot of processes that look a lot like what the brain is doing as well.”
For neuroscientists, that resemblance provides something rare: a model system for studying language. Investigating how people process words and ideas inside the brain is notoriously difficult, requiring complex experiments and limited opportunities to record neural activity during real-world communication. Artificial models, by contrast, can be examined in detail.
“As a neuroscientist, you always want model organisms that are easier to study than humans,” Hayden says. “For neuroscience of language and neuroscience of concepts, LLMs are basically a model organism.”
The work, however, demands collaboration across disciplines. Computer scientists design and interpret the models, linguists analyze how language is structured and used, and neuroscientists compare those findings to patterns in the brain. In service of bringing these perspectives together, Hayden and Rice associate professor of linguistics and cognitive sciences Suzanne Kemmer recently convened researchers to explore the emerging connections between AI and brain science.
“We invited a range of speakers with different disciplinary backgrounds but who are already all facing in the same direction,” Hayden says.
Kemmer sees the moment as a turning point in the study of language and cognition. “Combined with recent technological developments in brain research, some pioneered here in Texas, we are truly at a new jumping-off point,” she says.
The implications extend beyond theory. Because language reflects subtle shifts in cognition and emotion, researchers believe AI systems capable of analyzing language could eventually help monitor brain health or detect neurological conditions earlier.
“LLMs can analyze language use and may be able, if programmed the right way, to give a measure like blood pressure,” Hayden says.
For now, the science is still developing. But by placing artificial language systems alongside the human brain, researchers are beginning to illuminate how both learn, process and produce meaning.
What causes our brains to lose focus?
By Kat Cosley Trigg
From phone notifications to crowded screens, modern life bombards us with visual distractions. But for Rice assistant professor of psychological sciences Kirsten Adam, the question isn’t willpower — it’s what the brain is doing when attention falters and how it recovers.
“At any given moment, there’s far more information in the world than our brains can process,” Adam says. “Attention is what determines what reaches our awareness and what doesn’t.”
Adam is studying how irrelevant visual information interferes with our ability to stay on task — and why some distractions slow us down more than others. Her research examines how attention is captured, how it lingers and how the brain regains control.
In Adam’s lab, participants complete visual search tasks while researchers record brain activity using electroencephalography. The method captures attention shifts in real time, revealing the precise moment focus is pulled away and the effort required to refocus.
By pairing neural data with subtle behavioral changes — especially moments when people slow down — Adam is testing competing theories of distraction. One suggests attention must be disengaged from irrelevant information before returning to the task. Another proposes that distractions compete directly with relevant information for limited mental resources.
Though grounded in basic science, the work has practical stakes. Lapses in attention contribute to errors in high-risk settings such as medical imaging, airport security screening and driving. A clearer understanding of attention’s limits could inform the design of technologies that support decision-making instead of overwhelming it.
“We’re not trying to make attention limitless,” Adam says. “We’re trying to understand how it actually works, so we can stop designing environments and expectations that fight against it.”
As artificial intelligence-assisted tools become more common, understanding how attention truly functions may help ensure technology works with the brain — not against it.
How do protein clumps damage the brain?
By Marcy de Luna
For decades, the sticky protein plaques associated with Parkinson’s disease have been treated as the brain’s harmless debris: toxic, but essentially inert. But new Rice research suggests they play a far more damaging role, actively draining cells of energy when they accumulate in the brain.
In a study published in Advanced Science, Pernilla Wittung-Stafshede and her collaborators found that amyloid clumps made of alpha-synuclein — a protein closely linked to Parkinson’s disease and other neurodegenerative disorders — can break down adenosine triphosphate, or ATP, the molecule that powers nearly every cellular process. That means the plaques may function less like waste and more like tiny destructive machines.
“We were astonished to see that amyloids, long thought to be inert waste, can actively cleave ATP,” says Wittung-Stafshede, the Charles W. Duncan Jr.-Welch Chair in Chemistry and a Cancer Prevention and Research Institute of Texas Scholar. “The protein folds around ATP and essentially transforms the plaque into a molecular machine.”
The team first created uniform alpha-synuclein clumps in the lab, then successfully attempted to accelerate chemical reactions. When the researchers moved to ATP, they found the clumps broke it apart, releasing energy in a process that resembled enzyme activity.
If confirmed in living cells, the discovery could reshape how scientists understand neurodegenerative disease — and how they fight it, Wittung-Stafshede says.
To understand why, the team turned to cryo-electron microscopy, working with collaborators in Switzerland. The images showed that when ATP binds to the clump, a floppy region of the protein folds over it like a lid, creating a small pocket lined with positive charges that help drive the reaction.
“That folding over, or forming a lid, is what transforms a passive aggregate into a reactive enzymelike structure,” Wittung-Stafshede says.
When researchers removed those positive charges one by one, the clumps still formed but lost their ability to break down ATP, confirming that the structure itself is key. The findings suggest these plaques may worsen disease by starving cells of energy and interfering with the systems meant to clear them away.
If confirmed in living cells, the discovery could reshape how scientists understand neurodegenerative disease — and how they fight it, Wittung-Stafshede says.
“We want to learn how to stop neurodegenerative diseases at the source, directly detoxifying damaging species, instead of just treating symptoms as we do today.”
Can an algorithm identify brain cells at risk of Alzheimer’s?
By Silvia Cernea Clark
Alzheimer’s disease presents scientists with a frustrating paradox. Genetic studies have long pointed to one type of brain cell as a likely driver of the disease, while examinations of patients’ brains tell another story. Reconciling those two pictures has become a central challenge in understanding how dementia begins.
A new computational tool developed by computer science researchers at Rice, in collaboration with researchers at Boston University, may help close that gap. The algorithm, called the Single-cell Expression Integration System for Mapping genetically Implicated Cell types, or seismic, is designed to identify which specific cell types are genetically linked to complex diseases. When the researchers applied it to Alzheimer’s data, the tool highlighted neurons involved in memory formation — the very cells that deteriorate as the disease progresses.
The finding matters because Alzheimer’s research has often focused on microglia, the brain’s immune cells. Genetic evidence has repeatedly tied those infection-fighting cells to the disease, yet brain tissue from patients tends to show the most dramatic loss in memory-related neurons.
In testing, the system outperformed existing tools, detecting disease-relevant cellular signals that had previously been difficult to isolate.
“As we age, some brain cells naturally slow down, but in dementia — a memory-loss disease — specific brain cells actually die and can’t be replaced,” says Qiliang Lai ’25, a Rice doctoral student and first author of the study. “The fact that it is memory-making brain cells dying and not infection-fighting brain cells raises this confusing puzzle where DNA evidence and brain evidence don’t match up.”
To investigate the discrepancy, the researchers built seismic to combine two powerful types of biological data. Genome-wide association studies scan human DNA to identify genetic variations associated with disease, while single-cell RNA sequencing measures which genes are active in individual cells across the body. By integrating the two, the algorithm can link genetic risk to specific cell types with greater precision than earlier methods.
“We built our seismic algorithm to analyze genetic information and match it precisely to specific types of brain cells,” Lai says. “This enables us to create a more detailed picture of which cell types are affected by which genetic programs.”
In testing, the system outperformed existing tools, detecting disease-relevant cellular signals that had previously been difficult to isolate. The work suggests a way forward not only for Alzheimer’s but for many complex diseases. As vast genetic datasets continue to grow, the hope is that tools like seismic will help researchers pinpoint the earliest cellular vulnerabilities of diseases like Alzheimer’s — and, eventually, learn how to stop them before memory begins to fade.
From the Spring 2026 issue of Rice Magazine