Picture this scene: A father reads a storybook to his young daughter, the girl seated in his lap and apparently fascinated by the illustrations of a bunny.

Picture the same scene, neuroscientist version: Brain scans show activity in the father’s primary visual cortex, extending to a specialized word recognition area, as he reads the text and glances at the drawings; in the child’s brain, only the primary visual cortex and regions involved in recognizing the illustrations are activated.

The brain is constantly taking in countless pieces of information, processing different types in distinct areas of the brain. Evidence suggests that the word-recognition section is active once children begin learning to read.

But when does that region start going to work, exactly? And what makes it spring into action?

A literacy study launching this summer with dozens of local preschoolers not only will try to answer that, but also, lead researcher hopes, continue in years to come as both a research project and a service to the community. The reading instruction study, a two-week “camp” targeting children entering kindergarten in the fall, aims to teach early literacy skills and measure brain activity before and after instruction.

“We know how literacy works in the literate brain, but what changes in the brain’s circuitry from beginning to fluent readers?” said Yeatman, an assistant professor of speech and hearing sciences who conducts research at the 91̽��Institute for Learning & Brain Sciences.

The foundation of literacy is the alphabetic principle: letters represent sounds, which in turn come together to form words. Very young children first see a letter as, essentially, a squiggle. It’s not until they learn to associate those squiggles with the sounds of language, and combine those letters into words that have meaning, that they are reading.

The brain processes words in a small region at its base, close to a wider swath at the back of the head called the primary visual cortex, where all sight-related information is received. By ages 8 to 12 — generally considered a middle to fluent reading stage — magnetoencephalography (MEG) images of the brain indicate heightened neural activity in the word-recognition area. That same area, in a baby or preschooler brain, does not register activity in MEG images.

But at some point, between ogling pictures and reading chapter books, neurons in the word-recognition area start firing. Yeatman and his team want to try to determine when, and what instructional methods might ignite activity.

The study is recruiting 40 children who will start kindergarten in the fall and, at the time of the study, have limited knowledge of the alphabet (the better to evaluate the effects of various literacy lessons). The children will spend three hours a day, every day, for two weeks in what amounts to free “reading camp”: about five children per instructor, receiving evidence-based, game-oriented lessons in identifying and writing letters, reading sight words and the like. (Washington’s call for those skills and more.)

Yeatman will use the — a safe, noninvasive process — to capture images of each child’s brain at the beginning and end of the camp, and in a follow-up session the next summer. As children encounter literacy lessons in kindergarten, some may be reading by the end of the year, while others may be struggling with combining letters into sounds. The reading camp study may be able to help shed light on those early differences, Yeatman said.

In 2018, Yeatman published a that focused on the brain circuitry of school-age children with dyslexia, and the neural development that occurred when children underwent an intensive reading intervention.

The dyslexia study and, perhaps even more so, the reading camp study, function on two levels, Yeatman said. There’s the fundamental scientific purpose, which is to study how the brain develops the ability to read. But there’s also the potential for community impact, as children receive free instruction in essential skills. Yeatman hopes to expand reading camp to reach greater numbers of children, especially those who might not have access to preschool, in future summers.

“After every summer of doing this, there will be more research questions to ask. Why are some children struggling at the end of kindergarten? What’s changing in brain’s reading circuitry? What’s the impact of one component of reading instruction? How can we personalize instruction to a child’s unique patter of brain development” Yeatman said. “This is also a way to tie research to services that are helpful for the community, which is a good thing.”

The reading camp study is funded by the National Institutes of Health.

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For more information on the study, contact the Brain Development and Education Lab at bdelab@u.washington.edu or 206-685-9365.

The early years are when the brain develops the most, forming neural connections that pave the way for how a child — and the eventual adult — will express feelings, embark on a task, and learn new skills and concepts. Scientists have even theorized that the anatomical structure of neural connections forms the basis for how children identify letters and recognize words. In other words, the brain’s architecture may predetermine who will have trouble with reading, including children with dyslexia.

But teaching can change that, a new 91̽�� study finds.

Using MRI measurements of the brain’s neural connections, or “white matter,” 91̽��researchers have shown that, in struggling readers, the neural circuitry strengthened — and their reading performance improved — after just eight weeks of a specialized tutoring program. The , published June 8 in Nature Communications, is the first to measure white matter during an intensive educational intervention and link children’s learning with their brains’ flexibility.

“The process of educating a child is physically changing the brain,” said , an assistant professor in both the 91̽��Department of Speech and Hearing Sciences and the Institute for Learning & Brain Sciences (I-LABS). “We were able to detect changes in brain connections within just a few weeks of beginning the intervention program. It’s underappreciated that teachers are brain engineers who help kids build new brain circuits for important academic skills like reading.”

The study focused on three areas of white matter — regions rich with neuronal connections — that link regions of the brain involved in language and vision.

“We tend to think of these connections as being fixed,” said co-author , a 91̽��postdoctoral researcher. “In reality, different experiences can shape the brain in dramatic ways throughout development.”

After eight weeks of intensive instruction among study participants who struggled with reading, two of those three areas showed evidence of structural changes — a greater density of white matter and more organized “wiring.” That plasticity points to changes brought about by the environment, indicating that these regions are not inherently inflexible structures. They reorganize in response to experiences children have in the classroom.

, a learning disorder that affects the ability to read and spell words, is the most common language-related learning disability. While estimates vary, between 10 to 20 percent of the population has some form of dyslexia. There is no quick and simple cure, and without intervention, children with dyslexia tend to struggle in school as the need for literacy skills increases over time. ­

Yeatman, who launched the at I-LABS, conducted the study during the summers of 2016 and 2017, when a total of 24 children, ages 7 to 12, participated in a reading intervention program offered by Lindamood-Bell Learning Centers. The company did not fund the study but provided the tutoring services for free to study participants. The participants’ parents had reported that their child either struggled with reading or had been diagnosed with dyslexia.

Over eight weeks, the children received one-on-one instruction for four hours a day, five days a week. They took a series of reading tests before and after the tutoring program and underwent four MRI scans and behavioral evaluation sessions at the beginning, middle and end of the eight-week period. A control group of 19 children with a mixture of reading skill levels participated in the MRI and behavioral sessions but did not receive the reading intervention.

The researchers used measurements to determine the density of three areas of white matter — areas that contain nerve fibers and connect different specialized processing circuits to each other. Specifically, they looked at the rate at which water diffuses within the white matter: A decline in the rate of diffusion indicates that additional tissue has formed, which allows information to be transmitted faster and easier.

The analysis focused on the left arcuate fasciculus, which connects regions where language and sounds are processed; the left inferior longitudinal fasciculus, where visual inputs, such as letters on a page, are transmitted throughout the brain; and the posterior callosal connections, which link the two hemispheres of the brain.

Subjects in the control group showed no changes in diffusion rates or structure between MRI measurements. But for subjects who took part in the tutoring program, reading skills improved by an average of one full grade level. In the majority of these subjects, diffusion rates decreased in the arcuate and inferior longitudinal fasciculus. For the few children who showed no significant decline in diffusion by MRI, Yeatman said there could be compounding differences in individual capacities for brain plasticity, age of the participants (younger brains may be more susceptible to change than slightly older ones) or other factors.

The callosal connections showed no changes between treatment and control groups, results that support past research suggesting that this structure, though relevant for reading acquisition, may already be mature and stable by age 7, Yeatman said.

Just what kind of tissue was created among reading program participants is likely to be the subject of future study, the authors said. For example, the measurements might be picking up on increases in the number or size of certain types of cells that help nourish and maintain the white matter, or on added insulation for existing neural connections, Huber said. The challenge with MRI data, Yeatman pointed out, is that they reflect an indirect measurement — not a hands-on examination of the brain.

But the structure of this experiment underscores the importance of the findings, he added: Children participated in a tightly controlled, short-term educational intervention, with measurable, identifiable growth in brain tissue from start to finish.

“Much of what we know about brain plasticity comes from research done in animals,” Yeatman said. “The beauty of educational interventions is that they provide a means to study fundamental questions about the link between childhood experiences, brain plasticity and learning, all while giving kids extra help in reading.”

Yeatman believes the findings can extend to schools. Teachers have the potential to develop their students’ brains, regardless of whether they have the resources to provide individualized instruction for each student in their class.

“While many parents and teachers might worry that dyslexia is permanent, reflecting intrinsic deficits in the brain, these findings demonstrate that targeted, intensive reading programs not only lead to substantial improvements in reading skills, but also change the underlying wiring of the brain’s reading circuitry,” Yeatman said.

Other authors on the paper were , a graduate student at I-LABS, and , a data science fellow at the 91̽��eScience Institute. The study was funded by a grant from the National Science Foundation.

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For more information, contact Yeatman at 206-685-3934 or jyeatman@uw.edu.

Grant number: 1551330��

Over the past few years, scientists have faced a problem: They often cannot reproduce the results of experiments done by themselves or their peers.

This “” plagues fields from medicine to physics, and likely has many causes. But one is undoubtedly the difficulty of sharing the vast amounts of data collected and analyses performed in so-called “big data” studies.  The volume and complexity of the information also can make these scientific endeavors unwieldy when it comes time for researchers to share their data and findings with peers and the public.

Researchers at the 91̽�� have developed a set of tools to make one critical area of big data research — that of our central nervous system — easier to share. In a published online March 5 in , the 91̽��team describes an open-access browser they developed to display, analyze and share neurological data collected through a type of magnetic resonance imaging study known as diffusion-weighted MRI.

“There has been a lot of talk among researchers about the replication crisis,” said lead author . “But we wanted a tool — ready, widely available and easy to use — that would actually help fight the replication crisis.”

Yeatman — who is an assistant professor in the 91̽��Department of Speech & Hearing Sciences and the Institute for Learning & Brain Sciences () — is describing . This web browser-based tool, freely available online, is a platform for uploading, visualizing, analyzing and sharing diffusion MRI data in a format that is publicly accessible, improving transparency and data-sharing methods for neurological studies. In addition, since it runs in the web browser, AFQ-Browser is portable — requiring no additional software package or equipment beyond a computer and an internet connection.

“One major barrier to data transparency in neuroscience is that so much data collection, storage and analysis occurs on local computers with special software packages,” said senior author , a senior data scientist in the 91̽��. “But using AFQ-Browser, we eliminate those requirements and make uploading, sharing and analyzing diffusion-weighted MRI data a simple, straightforward process.”

Diffusion-weighted MRI measures the movement of fluid in the brain and spinal cord, revealing the structure and function of white-matter tracts. These are the connections of the central nervous system, tissue that are made up primarily of axons that transmit long-range signals between neural circuits. Diffusion MRI research on brain connectivity has fundamentally changed the way neuroscientists understand human brain function: The state, organization and layout of white matter tracts are at the core of cognitive functions such as memory, learning and other capabilities. Data collected using diffusion-weighted MRI can be used to diagnose complex neurological conditions such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS). Researchers also use diffusion-weighted MRI data to study the neurological underpinnings of conditions such as dyslexia and learning disabilities.

“This is a widely-used technique in neuroscience research, and it is particularly amenable to the benefits that can be gleaned from big data, so it became a logical starting point for developing browser-based, open-access tools for the field,” said Yeatman.

The AFQ-Browser — the AFQ stands for Automated Fiber-tract Quantification — can receive diffusion-weighted MRI data and perform tract analysis for each individual subject. The analyses occur via a remote server, again eliminating technical and financial barriers for researchers. The AFQ-Browser also contains interactive tools to display data for multiple subjects — allowing a researcher to easily visualize how white matter tracts might be similar or different among subjects, identify trends in the data and generate hypotheses for future experiments.

Researchers also can insert additional code to analyze the data, as well as save, upload and share data instantly with fellow researchers.

“We wanted this tool to be as generalizable as possible, regardless of research goals,” said Rokem. “In addition, the format is easy for scientists from a variety of backgrounds to use and understand — so that neuroscientists, statisticians and other researchers can collaborate, view data and share methods toward greater reproducibility.”

Embedded demo of AFQ-Browser

The idea for the AFQ-Browser came out of a 91̽��course on data visualization, and the researchers worked with several graduate students to develop and perfect the browser. They tested it on existing diffusion-weighted MRI datasets, including research subjects with and . In the future, they hope that the AFQ-Browser can be improved to do automated analyses — and possibly even diagnoses — based on diffusion-weighted MRI data.

“AFQ-Browser is really just the start of what could be a number of tools for sharing neuroscience data and experiments,” said Yeatman. “Our goal here is greater reproducibility and transparency, and a more robust scientific process.”

Co-authors on the paper are 91̽��physics doctoral student Adam Richie-Halford, 91̽��chemical engineering doctoral student Josh Smith, and Anisha Keshavan, a 91̽��postdoctoral researcher in I-LABS, the Institute for Neuroengineering, and the eScience Institute. The research was funded by the Gordon and Betty Moore Foundation, the Alfred P. Sloan Foundation and the U.S. Department of Energy.

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For more information, contact Yeatman at jyeatman@uw.edu or Rokem at arokem@gmail.com.

A couple of years ago a scientist looking at dozens of MRI scans of human brains noticed something surprising. A large, fiber pathway that seemed to be part of the network of connections that process visual information showed up on the scans, but the researcher couldn’t find it mentioned in any of the modern-day anatomy textbooks he had.

“It was this massive bundle of fibers, visible in every brain I examined,” said , a research scientist at the 91̽��’s . “It seemed unlikely that I was the first to have noticed this structure; however, as far as I could tell, it was absent from the literature and from all major neuroanatomy textbooks.”

With colleagues at Stanford University, where he was a graduate student at the time, Yeatman started some detective work to figure out the identity of that large, mysterious fiber bundle.

In the , published Nov. 17 by the Proceedings of the National Academy of Sciences, the team describes the history and controversy of the elusive brain pathway, explains how modern MRI techniques rediscovered it, and gives analytical tools researchers can use to identify the brain structure — now known as the vertical occipital fasciculus.

The “aha moment” in identifying the pathway came while Yeatman and Kevin Weiner, a Stanford postdoctoral researcher, were poring over the yellowed pages of 19th-century brain atlases in the basement of the Stanford Medical Library.

“Kevin found an atlas, written by Carl Wernicke near the turn of the (20^th) century, that depicted the vertical occipital fasciculus,” Yeatman said. “The last time that atlas had been checked out was 1912, meaning we were the first to view these images in the last century.”

From there, Yeatman and Weiner, who share lead authorship on the paper, did more library research revealing these possibilities for why the pathway was forgotten:

A scientific disagreement – In an 1881 neuroanatomy atlas, Wernicke, a well-known anatomist who in 1874 discovered “Wernicke’s area,” which is essential for language, wrote about a fiber pathway in a monkey brain he was examining. He called it “senkrechte Occiptalbündel” (translated as vertical occipital bundle). But its vertical orientation contradicted the belief of one of the most renowned neuroanatomists of the era, Theodor Meynert, who asserted that brain connections could only travel in between the front and the back of the brain, not up and down.

Haphazard naming methods – The 1880s and 1890s were a fertile time in the neuroanatomy world, but scientists lacked a shared process for naming the brain structures they found. Looking at drawings of the brain from this time period, Yeatman and coauthors saw that the fiber pathway that they were looking for appeared in brain atlases but was called different things, including “Wernicke’s perpendicular fasciculus,” “perpendicular occipital fasciculus of Wernicke,” and “stratum profundum convexitatis.”

“When we started, it was just for our own knowledge and curiosity,” said , who’s also the director of public information at the Institute for Applied Neuroscience, a nonprofit based in Palo Alto, California.

“But, after a while, we realized that there was an important story to tell that contained a series of missing links that have been buried for so long within this puzzle of historical conversation among many who are considered the founders of the entire neuroscience field.”

The researchers used a type of MRI measure called diffusion-weighted imaging to measure the size of the pathway and see where in the brain it went. Across brain scans taken from 37 subjects, they found that the vertical occipital fasciculus begins in the occipital lobe — the part of the brain’s visual processing system located at the back of the head.

From there, the fibers spread out like a sheet, connecting brain regions that are important for seeing objects with other brain regions that coordinate which objects to focus attention upon.

“We believe that signals carried by the VOF play a role in many perceptual processes, from recognizing a friend’s face to rapidly reading a page of text,” said Yeatman, who is now studying brain mechanisms involved in learning to read.

In the paper, the researchers also provide an algorithm that others can use on their own data to find the pathway and measure its properties.

“To support reproducible research, our lab makes a strong effort to share software and data,” said , senior author of the paper and a psychology professor at Stanford. “We believe this is a powerful way to ensure that our findings can be both checked and used in labs around the world.”

The researchers also hope that the algorithm will enable other researchers to study the pathway, possibly leading to a better understanding of its role in human cognition and in patient populations.

In addition to Yeatman, Weiner and Wandell, other co-authors are Franco Pestilli, Ariel Rokem and Aviv Mezer. This work was funded by grants to Wandell from the National Institutes of Health and the National Science Foundation.

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For more information, contact Jason Yeatman at jdyeatman@gmail.com or Kevin Weiner at kweiner@stanford.edu