Computers Math

Light-emitting diodes (LEDs) have revolutionized the displays industry. LEDs use electric current to produce visible light without the excess heat found in traditional light bulbs, a glow called electroluminescence. This breakthrough led to the eye-popping, high-definition viewing experience we’ve come to expect from our screens. Now, a group of physicists and chemists have developed a new type of LED that utilizes spintronics without needing a magnetic field, magnetic materials or cryogenic temperatures; a “quantum leap” that could take displays to the next level.
“The companies that make LEDs or TV and computer displays don’t want to deal with magnetic fields and magnetic materials. It’s heavy and expensive to do it,” said Valy Vardeny, distinguished professor of physics and astronomy at the University of Utah. “Here, chiral molecules are self-assembled into standing arrays, like soldiers, that actively spin polarize the injected electrons, which subsequently lead to circularly polarized light emission. With no magnetic field, expensive ferromagnets and with no need for extremely low temperatures. Those are no-nos for the industry.”
Most opto-electronic devices, such as LEDs, only control charge and light and not the spin of the electrons. The electrons possess tiny magnetic fields that, like the Earth, have magnetic poles on opposite sides. Its spin may be viewed as the orientation of the poles and can be assigned binary information — an “up” spin is a “1,” a “down” is a “0.” In contrast, conventional electronics only transmit information through bursts of electrons along a conductive wire to convey messages in “1s” and “0s.” Spintronic devices, however, could utilize both methods, promising to process exponentially more information than traditional electronics.
One barrier to commercial spintronics is setting the electron spin. Presently, one needs to produce a magnetic field to orient the electron spin direction. Researchers from the University of Utah and the National Renewable Energy Laboratory (NREL) developed technology that acts as an active spin filter made of two layers of material called chiral two-dimension metal-halide perovskites. The first layer blocks electrons having spin in the wrong direction, a layer that the authors call a chiral-induced spin filter. Then when the remaining electrons pass through the second light-emitting perovskite layer, they cause the layer to produce photons that move in unison along a spiral path, rather than a conventional wave pattern, to produce circular polarized electroluminescence.
The study was published in the journal Science on March 12, 2021.
Left-handed, right-handed molecules
The scientists exploited a property called chirality that describes a particular type of geometry. Human hands are a classic example; the right and left hands are arranged as mirrors of one another, but they will never perfectly align, no matter the orientation. Some compounds, such as DNA, sugar and chiral metal-halide perovskites, have their atoms arranged in a chiral symmetry. A “left-handed” oriented chiral system may allow transport of electrons with “up” spins but block electrons with “down” spins, and vice versa.

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“If you try to transport electrons through these compounds, then the electron spin becomes aligned with the chirality of the material,” Vardeny said. Other spin filters do exist, but they either require some kind of magnetic field, or they can only manipulate electrons in a small area. “The beauty of the perovskite material that we used is that it’s two-dimensional — you can prepare many planes of 1 cm2 area that contain one million of a billion (1015) standing molecules with the same chirality.”
Metal-halide perovskite semiconductors are mostly used for solar cells these days, as they are highly efficient at converting sunlight to electricity. Since a solar cell is one of the most demanding applications of any semiconductor, scientists are discovering other uses exist as well, including spin-LEDs.
“We are exploring the fundamental properties of metal-halide perovskites, which has allowed us to discover new applications beyond photovoltaics,” said Joseph Luther, a co-author of the new paper and NREL scientist. “Because metal-halide perovskites, and other related metal halide organic hybrids, are some of the most fascinating semiconductors, they exhibit a host of novel phenomena that can be utilized in transforming energy.”
Although metal-halide perovskites are the first to prove the chiral-hybrid devices are feasible, they are not the only candidates for spin-LEDs. The general formula for the active spin filter is one layer of an organic, chiral material, another layer of an inorganic metal halide, such as lead iodine, another organic layer, inorganic layer and so on.
“That’s beautiful. I’d love that someone will come out with another 2-D organic/inorganic layer material that may do a similar thing. At this stage, it’s very general. I’m sure that with time, someone will find a different two-dimensional chiral material that will be even more efficient,” Vardeny said.
The concept proves that using these two dimensional chiral-hybrid systems gain control over spin without magnets and has “broad implications for applications such as quantum-based optical computing, bioencoding and tomography,” according to Matthew Beard, a senior research fellow and director of Center for Hybrid Organic Inorganic Semiconductors for Energy.
Vardeny and Xin Pan from the Department of Physics & Astronomy at the University of Utah co-authored the study. The other co-authors from NREL are Beard, Young-Hoon Kim, Yaxin Zhai, Haipeng Lu, Chuanxiao Xiao, E. Ashley Gaulding, Steven Harvey and Joseph Berry. All are part of CHOISE collaboration, an Energy Frontier Research Center (EFRC) funded by the Office of Science within DOE.
Funding for the research came from CHOISE. More

In order to exploit the properties of quantum physics technologically, quantum objects and their interaction must be precisely controlled. In many cases, this is done using light. Researchers at the University of Innsbruck and the Institute of Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences have now developed a method to individually address quantum emitters using tailored light pulses. “Not only is it important to individually control and read the state of the emitters,” says Oriol Romero-Isart, “but also to do so while leaving the system as undisturbed as possible.” Together with Juan Jose Garcia-Ripoll (IQOQI visiting fellow) from the Instituto de Fisica Fundamental in Madrid, Romero-Isart’s research group has now investigated how specifically engineered pulses can be used to focus light on a single quantum emitter.
Self-compressing light pulse
“Our proposal is based on chirped light pulses,” explains Silvia Casulleras, first author of the research paper. “The frequency of these light pulses is time-dependent.” So, similar to the chirping of birds, the frequency of the signal changes over time. In structures with certain electromagnetic properties — such as waveguides — the frequencies propagate at different speeds. “If you set the initial conditions of the light pulse correctly, the pulse compresses itself at a certain distance,” explains Patrick Maurer from the Innsbruck team. “Another important part of our work was to show that the pulse enables the control of individual quantum emitters.” This approach can be used as a kind of remote control to address, for example, individual superconducting quantum bits in a waveguide or atoms near a photonic crystal.
Wide range of applications
In their work, now published in Physical Review Letters, the scientists show that this method works not only with light or electromagnetic pulses, but also with other waves such as lattice oscillations (phonons) or magnetic excitations (magnons). The research group led by the Innsbruck experimental physicist Gerhard Kirchmair, wants to implement the concept for superconducting qubits in the laboratory in close collaboration with the team of theorists.
The research was financially supported by the European Union.

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Researchers at UCL have solved a major piece of the puzzle that makes up the ancient Greek astronomical calculator known as the Antikythera Mechanism, a hand-powered mechanical device that was used to predict astronomical events.
Known to many as the world’s first analogue computer, the Antikythera Mechanism is the most complex piece of engineering to have survived from the ancient world. The 2,000-year-old device was used to predict the positions of the Sun, Moon and the planets as well as lunar and solar eclipses.
Published in Scientific Reports, the paper from the multidisciplinary UCL Antikythera Research Team reveals a new display of the ancient Greek order of the Universe (Cosmos), within a complex gearing system at the front of the Mechanism.
Lead author Professor Tony Freeth (UCL Mechanical Engineering) explained: “Ours is the first model that conforms to all the physical evidence and matches the descriptions in the scientific inscriptions engraved on the Mechanism itself.
“The Sun, Moon and planets are displayed in an impressive tour de force of ancient Greek brilliance.”
The Antikythera Mechanism has generated both fascination and intense controversy since its discovery in a Roman-era shipwreck in 1901 by Greek sponge divers near the small Mediterranean island of Antikythera.

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The astronomical calculator is a bronze device that consists of a complex combination of 30 surviving bronze gears used to predict astronomical events, including eclipses, phases of the moon, positions of the planets and even dates of the Olympics.
Whilst great progress has been made over the last century to understand how it worked, studies in 2005 using 3D X-rays and surface imaging enabled researchers to show how the Mechanism predicted eclipses and calculated the variable motion of the Moon.
However, until now, a full understanding of the gearing system at the front of the device has eluded the best efforts of researchers. Only about a third of the Mechanism has survived, and is split into 82 fragments — creating a daunting challenge for the UCL team.
The biggest surviving fragment, known as Fragment A, displays features of bearings, pillars and a block. Another, known as Fragment D, features an unexplained disk, 63-tooth gear and plate.
Previous research had used X-ray data from 2005 to reveal thousands of text characters hidden inside the fragments, unread for nearly 2,000 years. Inscriptions on the back cover include a description of the cosmos display, with the planets moving on rings and indicated by marker beads. It was this display that the team worked to reconstruct.

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Two critical numbers in the X-rays of the front cover, of 462 years and 442 years, accurately represent cycles of Venus and Saturn respectively. When observed from Earth, the planets’ cycles sometimes reverse their motions against the stars. Experts must track these variable cycles over long time-periods in order to predict their positions.
“The classic astronomy of the first millennium BC originated in Babylon, but nothing in this astronomy suggested how the ancient Greeks found the highly accurate 462-year cycle for Venus and 442-year cycle for Saturn,” explained PhD candidate and UCL Antikythera Research Team member Aris Dacanalis.
Using an ancient Greek mathematical method described by the philosopher Parmenides, the UCL team not only explained how the cycles for Venus and Saturn were derived but also managed to recover the cycles of all the other planets, where the evidence was missing.
PhD candidate and team member David Higgon explained: “After considerable struggle, we managed to match the evidence in Fragments A and D to a mechanism for Venus, which exactly models its 462-year planetary period relation, with the 63-tooth gear playing a crucial role.”
Professor Freeth added: “The team then created innovative mechanisms for all of the planets that would calculate the new advanced astronomical cycles and minimize the number of gears in the whole system, so that they would fit into the tight spaces available.”
“This is a key theoretical advance on how the Cosmos was constructed in the Mechanism,” added co-author, Dr Adam Wojcik (UCL Mechanical Engineering). “Now we must prove its feasibility by making it with ancient techniques. A particular challenge will be the system of nested tubes that carried the astronomical outputs.” More

In the field of robotics, metals offer advantages like strength, durability, and electrical conductivity. But, they are heavy and rigid — properties that are undesirable in soft and flexible systems for wearable computing and human-machine interfaces.
Hydrogels, on the other hand, are lightweight, stretchable, and biocompatible, making them excellent materials for contact lenses and tissue engineering scaffolding. They are, however, poor at conducting electricity, which is needed for digital circuits and bioelectronics applications.
Researchers in Carnegie Mellon University’s Soft Machines Lab have developed a unique silver-hydrogel composite that has high electrical conductivity and is capable of delivering direct current while maintaining soft compliance and deformability. The findings were published in Nature Electronics.
The team suspended micrometer-sized silver flakes in a polyacrylamide-alginate hydrogel matrix. After going through a partial dehydration process, the flakes formed percolating networks that were electrically conductive and robust to mechanical deformations. By manipulating this dehydration and hydration process, the flakes can be made to stick together or break apart, forming reversible electrical connections.
Previous attempts to combine metals and hydrogels revealed a trade-off between improved electrical conductivity and lowered compliance and deformability. Majidi and his team sought to tackle this challenge, building on their expertise in developing stretchable, conductive elastomers with liquid metal.
“With its high electrical conductivity and high compliance or ‘squishiness,’ this new composite can have many applications in bioelectronics and beyond,” explained Carmel Majidi, professor of mechanical engineering. “Examples include a sticker for the brain that has sensors for signal processing, a wearable energy generation device to power electronics, and stretchable displays.”
The silver-hydrogel composite can be printed by standard methods like stencil lithography, similar to screen printing. The researchers used this technique to develop skin-mounted electrodes for neuromuscular electrical stimulation. According to Majidi, the composite could cover a large area of the human body, “like a second layer of nervous tissue over your skin.”
Future applications could include treating muscular disorders and motor disabilities, such as assisting someone with tremors from Parkinson’s disease or difficulty grasping something with their fingers after a stroke.

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A new study demonstrates that machine-learning strategies can be applied to routinely collected physiological data, such as heart rate and blood pressure, to provide clues about pain levels in people with sickle cell disease. Mark Panaggio of Johns Hopkins University Applied Physics Laboratory and colleagues present these findings in the open-access journal PLOS Computational Biology.
Pain is subjective, and monitoring pain can be intrusive and time-consuming. Pain medication can help, but accurate knowledge of a patient’s pain is necessary to balance relief against risk of addiction or other unwanted effects. Machine-learning strategies have shown promise in predicting pain from objective physiological measurements, such as muscle activity or facial expressions, but few studies have applied machine learning to routinely collected data.
Now, Panaggio and colleagues have developed and applied machine-learning models to data from people with sickle cell disease who were hospitalized due to debilitating pain. These statistical models classify whether a patient’s pain was low, moderate, or high at each point during their stay based on routinely collected measurements of their blood pressure, heart rate, temperature, respiratory rate, and oxygen levels.
The researchers found that these vital signs indeed gave clues into the patients’ reported pain levels. By taking physiological data into account, their models outperformed baseline models in estimating subjective pain levels, detecting changes in pain, and identifying atypical pain levels. Pain predictions were most accurate when they accounted for changes in patients’ vital signs over time.
“Studies like ours show the potential that data-driven models based on machine learning have to enhance our ability to monitor patients in less invasive ways and ultimately, be able to provide more timely and targeted treatments,” Panaggio says.
Looking ahead, the researchers hope to leverage more comprehensive data sources and real-time monitoring tools, such as fitness trackers, to build better models for inferring and forecasting pain.

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What do you do after solving the answer to life, the universe, and everything? If you’re mathematicians Drew Sutherland and Andy Booker, you go for the harder problem.
In 2019, Booker, at the University of Bristol, and Sutherland, principal research scientist at MIT, were the first to find the answer to 42. The number has pop culture significance as the fictional answer to “the ultimate question of life, the universe, and everything,” as Douglas Adams famously penned in his novel “The Hitchhiker’s Guide to the Galaxy.” The question that begets 42, at least in the novel, is frustratingly, hilariously unknown.
In mathematics, entirely by coincidence, there exists a polynomial equation for which the answer, 42, had similarly eluded mathematicians for decades. The equation x3+y3+z3=k is known as the sum of cubes problem. While seemingly straightforward, the equation becomes exponentially difficult to solve when framed as a “Diophantine equation” — a problem that stipulates that, for any value of k, the values for x, y, and z must each be whole numbers.
When the sum of cubes equation is framed in this way, for certain values of k, the integer solutions for x, y, and z can grow to enormous numbers. The number space that mathematicians must search across for these numbers is larger still, requiring intricate and massive computations.
Over the years, mathematicians had managed through various means to solve the equation, either finding a solution or determining that a solution must not exist, for every value of k between 1 and 100 — except for 42.
In September 2019, Booker and Sutherland, harnessing the combined power of half a million home computers around the world, for the first time found a solution to 42. The widely reported breakthrough spurred the team to tackle an even harder, and in some ways more universal problem: finding the next solution for 3.

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Booker and Sutherland have now published the solutions for 42 and 3, along with several other numbers greater than 100, this week in the Proceedings of the National Academy of Sciences.
Picking up the gauntlet
The first two solutions for the equation x3+y3+z3 = 3 might be obvious to any high school algebra student, where x, y, and z can be either 1, 1, and 1, or 4, 4, and -5. Finding a third solution, however, has stumped expert number theorists for decades, and in 1953 the puzzle prompted pioneering mathematician Louis Mordell to ask the question: Is it even possible to know whether other solutions for 3 exist?
“This was sort of like Mordell throwing down the gauntlet,” says Sutherland. “The interest in solving this question is not so much for the particular solution, but to better understand how hard these equations are to solve. It’s a benchmark against which we can measure ourselves.”
As decades went by with no new solutions for 3, many began to believe there were none to be found. But soon after finding the answer to 42, Booker and Sutherland’s method, in a surprisingly short time, turned up the next solution for 3:5699368212219623807203 + (−569936821113563493509)3 + (−472715493453327032)3 = 3
The discovery was a direct answer to Mordell’s question: Yes, it is possible to find the next solution to 3, and what’s more, here is that solution. And perhaps more universally, the solution, involving gigantic, 21-digit numbers that were not possible to sift out until now, suggests that there are more solutions out there, for 3, and other values of k.

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“There had been some serious doubt in the mathematical and computational communities, because [Mordell’s question] is very hard to test,” Sutherland says. “The numbers get so big so fast. You’re never going to find more than the first few solutions. But what I can say is, having found this one solution, I’m convinced there are infinitely many more out there.”
A solution’s twist
To find the solutions for both 42 and 3, the team started with an existing algorithm, or a twisting of the sum of cubes equation into a form they believed would be more manageable to solve:
k − z3 = x3 + y3 = (x + y)(x2 − xy + y2)
This approach was first proposed by mathematician Roger Heath-Brown, who conjectured that there should be infinitely many solutions for every suitable k. The team further modified the algorithm by representing x+y as a single parameter, d. They then reduced the equation by dividing both sides by d and keeping only the remainder — an operation in mathematics termed “modulo d” — leaving a simplified representation of the problem.
“You can now think of k as a cube root of z, modulo d,” Sutherland explains. “So imagine working in a system of arithmetic where you only care about the remainder modulo d, and we’re trying to compute a cube root of k.”
With this sleeker version of the equation, the researchers would only need to look for values of d and z that would guarantee finding the ultimate solutions to x, y, and z, for k=3. But still, the space of numbers that they would have to search through would be infinitely large.
So, the researchers optimized the algorithm by using mathematical “sieving” techniques to dramatically cut down the space of possible solutions for d.
“This involves some fairly advanced number theory, using the structure of what we know about number fields to avoid looking in places we don’t need to look,” Sutherland says.
A global task
The team also developed ways to efficiently split the algorithm’s search into hundreds of thousands of parallel processing streams. If the algorithm were run on just one computer, it would have taken hundreds of years to find a solution to k=3. By dividing the job into millions of smaller tasks, each independently run on a separate computer, the team could further speed up their search.
In September 2019, the researchers put their plan in play through Charity Engine, a project that can be downloaded as a free app by any personal computer, and which is designed to harness any spare home computing power to collectively solve hard mathematical problems. At the time, Charity Engine’s grid comprised over 400,000 computers around the world, and Booker and Sutherland were able to run their algorithm on the network as a test of Charity Engine’s new software platform.
“For each computer in the network, they are told, ‘your job is to look for d’s whose prime factor falls within this range, subject to some other conditions,'” Sutherland says. “And we had to figure out how to divide the job up into roughly 4 million tasks that would each take about three hours for a computer to complete.”
Very quickly, the global grid returned the very first solution to k=42, and just two weeks later, the researchers confirmed they had found the third solution for k=3 — a milestone that they marked, in part, by printing the equation on t-shirts.
The fact that a third solution to k=3 exists suggests that Heath-Brown’s original conjecture was right and that there are infinitely more solutions beyond this newest one. Heath-Brown also predicts the space between solutions will grow exponentially, along with their searches. For instance, rather than the third solution’s 21-digit values, the fourth solution for x, y, and z will likely involve numbers with a mind-boggling 28 digits.
“The amount of work you have to do for each new solution grows by a factor of more than 10 million, so the next solution for 3 will need 10 million times 400,000 computers to find, and there’s no guarantee that’s even enough,” Sutherland says. “I don’t know if we’ll ever know the fourth solution. But I do believe it’s out there.” More

How do you turn “dumb” headphones into smart ones? Rutgers engineers have invented a cheap and easy way by transforming headphones into sensors that can be plugged into smartphones, identify their users, monitor their heart rates and perform other services.
Their invention, called HeadFi, is based on a small plug-in headphone adapter that turns a regular headphone into a sensing device. Unlike smart headphones, regular headphones lack sensors. HeadFi would allow users to avoid having to buy a new pair of smart headphones with embedded sensors to enjoy sensing features.
“HeadFi could turn hundreds of millions of existing, regular headphones worldwide into intelligent ones with a simple upgrade,” said Xiaoran Fan, a HeadFi primary inventor. He is a recent Rutgers doctoral graduate who completed the research during his final year at the university and now works at Samsung Artificial Intelligence Center.
A peer-reviewed Rutgers-led paper on the invention, which results in “earable intelligence,” will be formally published in October at MobiCom 2021, the top international conference on mobile computing and mobile and wireless networking.
Headphones are among the most popular wearable devices worldwide and they continue to become more intelligent as new functions appear, such as touch-based gesture control, the paper notes. Such functions usually rely on auxiliary sensors, such as accelerometers, gyroscopes and microphones that are available on many smart headphones.
HeadFi turns the two drivers already inside all headphones into a versatile sensor, and it works by connecting headphones to a pairing device, such as a smartphone. It does not require adding auxiliary sensors and avoids changes to headphone hardware or the need to customize headphones, both of which may increase their weight and bulk. By plugging into HeadFi, a converted headphone can perform sensing tasks and play music at the same time.
The engineers conducted experiments with 53 volunteers using 54 pairs of headphones with estimated prices ranging from $2.99 to $15,000. HeadFi can achieve 97.2 percent to 99.5 percent accuracy on user identification, 96.8 percent to 99.2 percent on heart rate monitoring and 97.7 percent to 99.3 percent on gesture recognition.

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A University at Buffalo researcher’s recent work on dyslexia has unexpectedly produced a startling discovery which clearly demonstrates how the cooperative areas of the brain responsible for reading skill are also at work during apparently unrelated activities, such as multiplication.
Though the division between literacy and math is commonly reflected in the division between the arts and sciences, the findings suggest that reading, writing and arithmetic, the foundational skills informally identified as the three Rs, might actually overlap in ways not previously imagined, let alone experimentally validated.
“These findings floored me,” said Christopher McNorgan, PhD, the paper’s author and an assistant professor in UB’s Department of Psychology. “They elevate the value and importance of literacy by showing how reading proficiency reaches across domains, guiding how we approach other tasks and solve other problems.
“Reading is everything, and saying so is more than an inspirational slogan. It’s now a definitive research conclusion.”
And it’s a conclusion that was not originally part of McNorgan’s design. He planned to exclusively explore if it was possible to identify children with dyslexia on the basis of how the brain was wired for reading.
“It seemed plausible given the work I had recently finished, which identified a biomarker for ADHD,” said McNorgan, an expert in neuroimaging and computational modeling.

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Like that previous study, a novel deep learning approach that makes multiple simultaneous classifications is at the core of McNorgan’s current paper, which appears in the journal Frontiers in Computational Neuroscience.
Deep learning networks are ideal for uncovering conditional, non-linear relationships.
Where linear relationships involve one variable directly influencing another, a non-linear relationship can be slippery because changes in one area do not necessarily proportionally influence another area. But what’s challenging for traditional methods is easily handled through deep learning.
McNorgan identified dyslexia with 94% accuracy when he finished with his first data set, consisting of functional connectivity from 14 good readers and 14 poor readers engaged in a language task.
But he needed another data set to determine if his findings could be generalized. So McNorgan chose a math study, which relied on a mental multiplication task, and measured functional connectivity from the fMRI information in that second data set.

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Functional connectivity, unlike what the name might imply, is a dynamic description of how the brain is virtually wired from moment to moment. Don’t think in terms of the physical wires used in a network, but instead of how those wires are used throughout the day. When you’re working, your laptop is sending a document to your printer. Later in the day, your laptop might be streaming a movie to your television. How those wires are used depends on whether you’re working or relaxing. Functional connectivity changes according to the immediate task.
The brain dynamically rewires itself according to the task all the time. Imagine reading a list of restaurant specials while standing only a few steps away from the menu board nailed to the wall. The visual cortex is working whenever you’re looking at something, but because you’re reading, the visual cortex works with, or is wired to, at least for the moment, the auditory cortex.
Pointing to one of the items on the board, you accidentally knock it from the wall. When you reach out to catch it, your brain wiring changes. You’re no longer reading, but trying to catch a falling object, and your visual cortex now works with the pre-motor cortex to guide your hand.
Different tasks, different wiring; or, as McNorgan explains, different functional networks.
In the two data sets McNorgan used, participants were engaged in different tasks: language and math. Yet in each case, the connectivity fingerprint was the same, and he was able to identify dyslexia with 94% accuracy whether testing against the reading group or the math group.
It was a whim, he said, to see how well his model distinguished good readers from poor readers — or from participants who weren’t reading at all. Seeing the accuracy, and the similarity, changed the direction of the paper McNorgan intended.
Yes, he could identify dyslexia. But it became obvious that the brain’s wiring for reading was also present for math.
Different task. Same functional networks.
“The brain should be dynamically wiring itself in a way that’s specifically relevant to doing math because of the multiplication problem in the second data set, but there’s clear evidence of the dynamic configuration of the reading network showing up in the math task,” McNorgan says.
He says it’s the sort of finding that strengthens the already strong case for supporting literacy.
“These results show that the way our brain is wired for reading is actually influencing how the brain functions for math,” he said. “That says your reading skill is going to affect how you tackle problems in other domains, and helps us better understand children with learning difficulties in both reading and math.”
As the line between cognitive domains becomes more blurred, McNorgan wonders what other domains the reading network is actually guiding.
“I’ve looked at two domains which couldn’t be farther afield,” he said. “If the brain is showing that its wiring for reading is showing up in mental multiplication, what else might it be contributing toward?”
That’s an open question, for now, according to McNorgan.
“What I do know because of this research is that an educational emphasis on reading means much more than improving reading skill,” he said. “These findings suggest that learning how to read shapes so much more.” More