A team of scientists based in the US has developed a non-invasive headset device designed to track changes in blood flow and assess a patient’s stroke risk. The device could make it easier to detect early signs of stroke, offering patients and physicians a direct, cost-effective approach to stroke prevention.

The challenge of stroke risk assessment

Stroke remains the leading cause of death and long-term disability, affecting 15 million people worldwide every year. In the United States, someone dies from a stroke roughly every 3 min. Those who survive are often left physically and cognitively impaired.

About 80% of strokes occur when a blood clot blocks an artery that carries blood to the brain (ischaemic stroke). In other cases, a blood vessel can rupture and bleed into the brain (haemorrhagic stroke). In both types of stroke, deprived of oxygen from the loss of blood flow, millions of brain cells rapidly die every minute, causing devastating disability and even death.

As debilitating as stroke is, current methods for assessing stroke risk remain limited. Physicians typically use a questionnaire that assesses factors such as demographics, blood test results and pre-existing medical conditions to estimate a patient’s risk. While non-invasive techniques exist to detect changes after the onset of a stroke, by the time a stroke is suspected and patients are rushed to the emergency room, critical damage may have already been done.

Consequently, there remains an acute need for tools that can proactively monitor and quantify stroke risk before an event occurs.

Blood flow dynamics as proxies for stroke risk

Seeking to bridge this gap, in a study published in Biomedical Optics Express, a research team, led by Charles Liu of the Keck School of Medicine at the University of Southern California and Changhuei Yang of California Institute of Technology, developed a headset device to monitor changes in the brain’s blood flow and volume while a patient holds their breath.

“Stroke is essentially a brain attack. The stroke world has been trying to draw a parallel between a heart attack and a brain attack,” explains Liu. “When you have a heart disease, under normal circumstances – like sitting on the couch or walking to the kitchen – your heart may seem fine. But if you start walking uphill, you might experience chest pain. For heart diseases, we have the cardiac stress test. During this test, a doctor puts you on a treadmill and monitors your heart with EKG leads. For stroke, we do not have a scalable and practical equivalent to a cardiac stress test.”

Indeed, breath holding temporarily stresses the brain, similar to the way that walking uphill or running on a treadmill would stress the heart in a cardiac stress test. During breath holding, blood volume and blood flow increase in response to lower oxygen and higher carbon dioxide levels. In turn, blood vessels dilate to mitigate the pressure of this increase in blood flow. In patients with higher stroke risk, less flexible blood vessels would impede dilation, causing distinct changes in blood flow dynamics.

Researchers have long had access to various imaging techniques to measure blood dynamics in the brain. However, these methods are often expensive, invasive and impractical for routine screening. To circumvent these limitations, the team built a device comprising a laser diode and a camera that can be placed on the head with no external optical elements, making it lightweight, portable, and cost-effective.

The device transmits infrared light through the skull and brain. A camera positioned elsewhere on the head captures the transmitted light through the skull. By tracking how much the light intensity decreases as it travels through the skull and into the camera, the device can measure changes in blood volume.

When a coherent light source such as a laser scatters off a moving sample (i.e., flowing blood), it creates a type of granular interference pattern, known as a speckle pattern. These patterns fluctuate as blood moves through the brain – the faster the blood flow, the quicker the fluctuations. This technique, called speckle contrast optical spectroscopy (SCOS), enables the researchers to non-invasively measure the blood flow rate in the brain.

The researchers tested the device on 50 participants, divided into low- and high-risk groups based on a standard stroke-risk calculator. During a breath-holding exercise, they found significant differences in blood dynamic changes between people with high stroke risk and those at lower risk.

Specifically, the high-risk group exhibited a faster blood flow rate but a lower volume of blood in response to the brain’s oxygen demands, suggesting restricted blood flow through the stiff vessels. Overall, these findings establish physiological links between stroke risk and blood dynamics measurements, highlighting the technology’s potential for stroke diagnosis and prevention.

The future of stroke prevention

The team plans to expand these studies to a broader population to reinforce the validity of the results. “Our goal is to further develop this concept to ensure it remains portable, compact, and easy to operate without requiring specialized technicians. We believe the design is scalable, aligning well with our vision of accessibility, allowing diverse and underrepresented communities to benefit from this technology,” says co-lead author Simon Mahler, a postdoctoral scholar in the Yang lab at Caltech.

The researchers also aim to integrate machine learning into data analysis and conduct clinical trials in a hospital setting, testing their approach’s effectiveness in stroke prevention. They are also excited about the applications of their device in other neurological conditions, including brain injuries, seizures, and headaches.

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In computing, quantum mechanics is a double-edged sword. While computers that use quantum bits, or qubits, can perform certain operations much faster than their classical counterparts, these qubits only maintain their quantum nature – their superpositions and entanglement – for a limited time. Beyond this so-called coherence time, interactions with the environment, or noise, lead to loss of information and errors. Worse, because quantum states cannot be copied – a consequence of quantum mechanics known as the no-cloning theorem – or directly observed without collapsing the state, correcting these errors requires more sophisticated strategies than the simple duplications used in classical computing.

One such strategy is known as an approximate quantum error correction (AQEC) code. Unlike exact QEC codes, which aim for perfect error correction, AQEC codes help quantum computers return to almost, though not exactly, their intended state. “When we can allow mild degrees of approximation, the code can be much more efficient,” explains Zi-Wen Liu, a theoretical physicist who studies quantum information and computation at China’s Tsinghua University. “This is a very worthwhile trade-off.”

The problem is that the performance and characteristics of AQEC codes are poorly understood. For instance, AQEC conventionally entails the expectation that errors will become negligible as system size increases. This can in fact be achieved simply by appending a series of redundant qubits to the logical state for random local noise; the likelihood of the logical information being affected would, in that case, be vanishingly small. However, this approach is ultimately unhelpful. This raises the questions: What separates good (that is, non-trivial) codes from bad ones? Is this dividing line universal?

Establishing a new boundary

So far, scientists have not found a general way of differentiating trivial and non-trivial AQEC codes. However, this blurry boundary motivated Liu, Daniel Gottesman of the University of Maryland, US; Jinmin Yi of Canada’s Perimeter Institute for Theoretical Physics; and Weicheng Ye at the University of British Columbia, Canada, to develop a framework for doing so.

To this end, the team established a crucial parameter called subsystem variance. This parameter describes the fluctuation of subsystems of states within the code space, and, as the team discovered, links the effectiveness of AQEC codes to a property known as quantum circuit complexity.

Circuit complexity, an important concept in both computer science and physics, represents the optimal cost of a computational process. This cost can be assessed in many ways, with the most intuitive metrics being the minimum time or the “size” of computation required to prepare a quantum state using local gate operations. For instance, how long does it take to link up the individual qubits to create the desired quantum states or transformations needed to complete a computational task?

The researchers found that if the subsystem variance falls below a certain threshold, any code within this regime is considered a nontrivial AQEC code and subject to a lower bound of circuit complexity. This finding is highly general and does not depend on the specific structures of the system. Hence, by establishing this boundary, the researchers gained a more unified framework for evaluating and using AQEC codes, allowing them to explore broader error correction schemes essential for building reliable quantum computers.

A quantum leap

But that wasn’t all. The researchers also discovered that their new AQEC theory carries implications beyond quantum computing. Notably, they found that the dividing line between trivial and non-trivial AQEC codes also arises as a universal “threshold” in other physical scenarios – suggesting that this boundary is not arbitrary but rooted in elementary laws of nature.

One such scenario is the study of topological order in condensed matter physics. Topologically ordered systems are described by entanglement conditions and their associated code properties. These conditions include long-range entanglement, which is a circuit complexity condition, and topological entanglement entropy, which quantifies the extent of long-range entanglement. The new framework clarifies the connection between these entanglement conditions and topological quantum order, allowing researchers to better understand these exotic phases of matter.

A more surprising connection, though, concerns one of the deepest questions in modern physics: how do we reconcile quantum mechanics with Einstein’s general theory of relativity? While quantum mechanics governs the behavior of particles at the smallest scales, general relativity accounts for gravity and space-time on a cosmic scale. These two pillars of modern physics have some incompatible intersections, creating challenges when applying quantum mechanics to strongly gravitational systems.

In the 1990s, a mathematical framework called the anti-de Sitter/conformal field theory correspondence (AdS/CFT) emerged as a way of using CFT to study quantum gravity even though it does not incorporate gravity. As it turns out, the way quantum information is encoded in CFT has conceptual ties to QEC. Indeed, these ties have driven recent advances in our understanding of quantum gravity.

By studying CFT systems at low energies and identifying connections between code properties and intrinsic CFT features, the researchers discovered that the CFT codes that pass their AQEC threshold might be useful for probing certain symmetries in quantum gravity. New insights from AQEC codes could even lead to new approaches to spacetime and gravity, helping to bridge the divide between quantum mechanics and general relativity.

Some big questions remain unanswered, though. One of these concerns the line between trivial and non-trivial codes. For instance, what happens to codes that live close to the boundary? The researchers plan to investigate scenarios where AQEC codes could outperform exact codes, and to explore ways to make the implications for quantum gravity more rigorous. They hope their study will inspire further explorations of AQEC’s applications to other interesting physical systems.

The research is described in Nature Physics.

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