During the COVID-19 pandemic, governments around the world implemented restrictive measures — such as stay-at-home orders and school closures — to mitigate the spread of the respiratory illness. It’s been well-documented that this disruption of daily routines and social activities on the mental health of adolescents.

Adolescence, the period of transition between childhood and adulthood, is marked by dramatic changes in emotional, behavioral and social development. It’s also a time when a sense of self-identity, self-confidence and self-control are developed. The pandemic reduced social interaction for teenagers and led to documented reports of anxiety, depression and stress, especially for girls.

New research from the 91̽��, published Sept. 9 in the , found the pandemic also resulted in unusually accelerated brain maturation in adolescents. This maturation was more pronounced in girls. When measured in terms of the number of years of accelerated brain development, the mean acceleration was 4.2 years in females and 1.4 years in males.

“We think of the COVID-19 pandemic as a health crisis,” said , senior author and co-director of the 91̽�� (I-LABS), “but we know that it produced other profound changes in our lives, especially for teenagers.”

Brain maturation is measured by the thickness of the cerebral cortex, the outer layer of tissue in the brain. The cerebral cortex naturally thins with age, even in teens. Chronic stress and adversity are known to accelerate cortical thinning, which is associated with an increased risk for the development of neuropsychiatric and behavioral disorders. Many of these disorders, such as anxiety and depression, often emerge during adolescence — with females at a higher risk.

The 91̽��research began in 2018 as a longitudinal study of 160 teens between 9 and 17 years, with the original objective of evaluating changes in brain structure during typical adolescence. The cohort was slated to return in 2020, but the pandemic delayed the repeat tests until 2021. By then, the original intent to study typical teen development was no longer viable.

“Once the pandemic was underway, we started to think about which brain measures would allow us to estimate what the pandemic lockdown had done to the brain,” said , lead author and research scientist at I-LABS. “What did it mean for our teens to be at home rather than in their social groups — not at school, not playing sports, not hanging out?”

Using the original 2018 data, researchers created a model of expected cortical thinning during the teen years. They then re-examined the brains of the adolescents, over 80% of whom returned for the second set of measurements. The teens’ brains showed a general effect of accelerated thinning across adolescence, but this was much more pronounced in females. The cortical thinning effects in females were seen all over the brain, in all lobes and both hemispheres. In males, the effects were only seen in the visual cortex.

The greater impact on female brains as opposed to male brains could be due to differences in the importance of social interaction for girls versus boys, Kuhl said. She added that female teenagers often rely more heavily on the relationships with other girls, prioritizing the ability to gather, talk to each other and share feelings. Boys tend to gather for physical activity.

“Teenagers really are walking a tightrope, trying to get their lives together,” Kuhl said. “They’re under tremendous pressure. Then a global pandemic strikes and their normal channels of stress release are gone. Those release outlets aren’t there anymore, but the social criticisms and pressures remain because of social media. What the pandemic really seems to have done is to isolate girls. All teenagers got isolated, but girls suffered more. It affected their brains much more dramatically.”

The cerebral cortex is unlikely to get thicker again, Kuhl said, but the potential for recovery might take the form of slower thinning over time, after the return of normal social interactions and outlets. Further research will be needed to see if this is the case.

“It is possible that there might be some recovery,” Kuhl said. “On the other hand, it’s also possible to imagine that brain maturation will remain accelerated in these teens.”

In older populations, measures of cognitive brain function, such as processing speed and the ability to complete typical tasks, correlate with how much the cerebral cortex has thinned. That kind of data is not yet available for teenagers, Kuhl said, but it could be where future research is headed.

“The pandemic provided a test case for the fragility of teenagers’ brains,” Kuhl said. “Our research introduces a new set of questions about what it means to speed up the aging process in the brain. All the best research raises profound new questions, and I think that’s what we’ve done here.”

, a 91̽��research associate professor of psychology and data science fellow at the eScience Institute, is a co-author. The research was funded by a grant from the Bezos Family Foundation.

For more information, contact Kuhl at pkkuhl@uw.edu.

Three new faculty books from the 91̽�� cover the recipes and culture of the world’s largest Syrian refugee camp, traditional ecological knowledge of Indigenous peoples and data science for neuroimaging researchers.

91̽��News spoke with the authors to learn more.

Documenting history and rituals of Syrian cuisine

When was invited to Zaatari, the world’s largest Syrian refugee camp, she noticed that stories of the camp rarely included women’s voices. As she learned more about their lives, she had the idea to create a cookbook to counter the effects of domicide – the deliberate destruction of housing and basic infrastructure – and carve a space for the women to share their cultural knowledge with the world.

Published by Goose Lane Editions, “,” brings to life stories and traditions that have been passed down from generation to generation. Fisher wrote the book in collaboration with over 2,000 refugees. All royalties return to the people of Zaatari, which is located near Jordan’s border with Syria.

“The book was a way to increase global awareness about war and refugees, and to show how important food and other aspects of the culture are in human survival and in telling the human story,” said Fisher, a 91̽��professor in the Information School and an adjunct professor of communication.

The women in the camp were excited when Fisher approached them with the idea – even though many of them had never seen a cookbook.

“Part of why our book is so fascinating is that it focuses on tacit knowledge and the social nature of cooking,” Fisher said. “You learn to cook by cooking alongside somebody else.”

With over 130 recipes, some of which have never been written down before, the book documents the history and rituals of Syrian cuisine and how they have been adapted to life in a refugee camp. It also chronicles camp culture.

“We cannot lose our connections with humanity,” Fisher said. “Just because someone is a refugee living in a camp halfway around the world, doesn’t mean that their lives don’t have value. They are important within the global world that we live in and are all part of the history of humanity. All of these things need to be preserved and supported.”

Because they are war refugees, the people involved in the project were all credited with aliases. The photographs of the women were also taken from behind to protect their identities and as part of Islamic practice.

“The Zaatari book is just a powerful example of the 91̽��community-engaged research, of working with a refugee community and agencies inside a high security closed refugee camp,” Fisher said. “It was just incredible what we were able to do.”

For more information, contact Fisher at fisher@uw.edu.��

Collection highlights Indigenous environmental knowledge

In “,” presenters from the discuss best practices for traditional ecological knowledge, or TEK, which refers to evolving knowledge acquired by Indigenous peoples through direct contact with the environment.

(enrolled Haliwa Saponi/descendant Eastern Band Cherokee), who is an associate professor and chair of Social and Historical studies in the School of Interdisciplinary Arts and Sciences at the 91̽�� Tacoma, edited the book. She brought together speakers from the Indigenous Speakers Series and multigenerational Indigenous peoples to share how TEK aids in environmental justice efforts and why it should be adapted into Western sciences.

Launched by Montgomery in 2015, the Indigenous Speaker Series is a multi-purpose platform that promotes community partnerships, amplifies the voices of Indigenous people and dialogues about Indigenous people’s cultural and traditional lived experiences.

“Part of the Indigenous Speaker Series is about bringing in multigenerational voices to talk about all sorts of topics that relate to sustainability, because sustainability isn’t just about ecosystems or STEM initiatives,” Montgomery said. “It’s also about culture, identity, all those sorts of things. This project is about me really being passionate about decolonizing and indigenizing the narrative.”

As the founder and director of the Indigenous Speaker Series and “a humble, forever student,” Montgomery wanted to give back to the community by helping people share their stories.

“Culturally, I’m taught that my wealth is determined by how many people can say I contributed when asked,” Montgomery said. “Did I give back? How many people did I uplift as I made it on the journey? Being an editor, it sounds like a position of unique power. But to me, it was a humbling opportunity to reach out to people and to say, ‘I believe in your voice. Let me create a platform so you can share it.’”

Storytelling is about empowerment and justice, Montgomery said. Published by University Press of Colorado, the book is a multi-tribal collection and a space for people from all walks of life to share interdisciplinary knowledge through their stories.

“The reason why it’s important for me to always uplift the voices and the storytelling of people is that I want people to feel comfortable in their identity and the walk that they walk,” Montgomery said. “If you save spaces to tell their story, erasure doesn’t happen.”

For more information, contact Montgomery at montgm2@uw.edu.��

A new guide on data science for researchers

“,” recently published by Princeton University Press, serves as a guide to broadly relevant data science skills with specific application to neuroimaging research.

Written by , research associate professor of psychology at the 91̽��and data science fellow at the 91̽��eScience Institute, and , the book fills the need for an authoritative resource on data science for neuroimaging researchers.

“We’re both neuroimaging researchers and both of us painstakingly acquired data science skills by learning from mentors and peers and teaching ourselves,” Rokem said. “What we wanted to do was make that process a lot easier, especially for early-career researchers in our field.”

In 2016, Rokem and Yarkoni established a summer school focused on data science and neuroimaging. They’ve received funding from the National Institutes of Mental Health since 2017 to run the course, which is now called . Over the years, they identified gaps in existing training and worked to fill them.

In June, The Organization for Human Brain Mapping (OHBM) awarded Rokem the , which is given to an OHBM member who has made significant contribution to education and training in the field of neuroimaging. Rokem was recognized for the work that led to the book, among other accomplishments.

Formal training programs don’t typically cover topics like data management and programming topics in machine learning, Rokem said. The book provides a source that students, teachers and instructors can use to learn and teach about these skills.

“Neuroimaging and neuroscience research, much like many other fields, is inundated in data,” Rokem said. “The instruments that we use to make neuroimaging measurements and the datasets that we have available to us are all becoming larger, more complicated.”

Researchers who are mentoring students don’t always have experience with the current magnitude of available datasets. “Data Science for Neuroimaging: An Introduction” helps bridge the gap.

There is also a growing concern about reproducibility in the neuroimaging field, Rokem said.

“One of the ways to mitigate concerns about reproducibility is to automate everything, track the progress of the research and then make the research openly available in a way that others can inspect what we’re doing,” Rokem said. “This is part of a larger movement around open science and reproducible research that the eScience Institute has been advancing here at the UW. Part of what we write about in the book is, what are the tools and techniques for making research accessible to and reproducible by others?”

The book, which allows users to run code examples and experiment with them hands-on, is also openly.

For more information, contact Rokem at arokem@uw.edu.

A single brain is unfathomably complex. So brain researchers, whether they’re looking at datasets built from or from , are now dealing with so much information that they must also come up with new methods to comprehend it. Developing new analysis tools has become as important as using them to understand brain health and development.

A team including researchers at the 91̽�� recently used new software to compare MRIs from 300 babies and discovered that myelin, a part of the brain’s so-called white matter, develops much slower after birth. The researchers Aug. 7 in the Proceedings of the National Academy of Sciences.

91̽��News spoke with senior author , a 91̽��research associate professor in the psychology department and a data science fellow in the eScience Institute, about the paper and his research approach.

What topics do you research and how?

Ariel Rokem: My group works in neuroinformatics, which focuses on building methods and software for analyzing neuroscience data. We focus specifically on MRI measurements in human brains. A brain is made out of a big network of connections between different areas. Within our brains, we have these big bundles of connections called white matter that contain lots of axons, which are the long, branching parts of neurons that let them talk to each other across pretty large distances. So we use MRI to find these bundles in every person in a study and then make sense of the tissue within these bundles. From that, we can find differences between people who have certain diseases and those who don’t, or differences in development or cognitive abilities.

How does this approach differ from how brain research was practiced historically?

AR: For many years, researchers would take test subjects over to their local hospital or MRI center and collect some data. And people still do this. In fact, we have one of these scanners at the new , which I am part of. But more recent approaches involve collecting much larger amounts of data. For example, it would be hard for anyone here in the department at the 91̽��to collect data from more than 1,000 individuals. But a few years ago, the National Institutes of Health funded what’s called the to do exactly that — get a sample of 1,200 healthy, grown-up people, and collect pretty large amounts of data on each of them. In neuroinformatics we take those kinds of datasets and develop the tools to study them.

What discoveries have these methods led to within brain science?

AR: Our recent paper is a good example. Our team used a large, openly available dataset from the , which collects data from newborn infants in the first few days of life. We were looking at how white matter develops in these scans of more than 300 babies. My collaborator and lead author , at the Philipps University of Marburg, had previously taken software for finding white matter bundles in adults and adapted it to work on babies’ brains. In this study, we scaled her approach up using cloud computing. We were looking at how myelin, a fatty sheath that insulates axons, grows in white matter.

We know from other studies that abnormal myelin development is associated with many developmental and mental health disorders, from . But before this study we still didn’t know how birth changes the course of myelin development.

We had several hypotheses that we wanted to test. One is, well, that it doesn’t matter when exactly you were born; it just matters how much time passed from conception to when you’re scanned. Another was that it matters only how long after conception you were born, and it didn’t matter how long after birth you were scanned. And we had a third hypothesis that says both of these things matter: how long the baby spent gestating in the mother’s womb, and how much time passed from birth until the time of the scan. So we were comparing scans from babies who were born at different gestational ages, ranging from very early premature birth, up to babies who were born a couple of weeks after the full term of 40 weeks. Because we had this large dataset to work with, we could really chart how babies’ brains change in the first few days and weeks of life.

We found that the data supports that both the gestational age at birth and the gestational age at time of scan mattered, but there’s an inflection point right at birth. Right then, development of these bundles that we were looking at slows down dramatically. It’s a basic fact, but we didn’t know this until now, and we found it by examining publicly available data. This has implications for our basic understanding of early-life brain development, and implications for the ways that we might mitigate the adverse effects of premature birth. Perhaps, for instance, creating a “womb-like” environment after birth could offset this slowed development and give the brains of premature babies more time to develop.

What are you looking to investigate with these methods moving forward?

AR: We’re starting to ask questions about brain connections related to autism spectrum disorder and to schizophrenia. We’re also now part of the UW’s , or the Adult Changes in Thought Study. It’s been around for , following a large cohort of people in the Seattle area as they are aging. In the recent round of that study, we’ve added MRI measurements. We’re developing methods to make inferences about white matter bundles in people who are aging.

Additional co-authors on this paper are , a former 91̽��post-baccalaureate student in the psychology department; , a 91̽��doctoral student in the psychology department; , a former 91̽��postdoctoral researcher in the psychology department; and at Philipps University of Marburg; and and at Stanford University. This research was funded by the National Institute of Mental Health and the National Eye Institute.

For more information, contact Rokem at arokem@uw.edu.

, professor in the Paul G. Allen School of Computer Science & Engineering, has been awarded a of $2 million for two years from the National Science Foundation as part of the NSF’s program.

The manufacturing life cycle starts with discovering new molecules and materials, often through computer simulations, and identifying promising candidates that can later be tested in laboratories. The grant, starting in September, will support development of new data science approaches to accelerate the engineering life cycle of design, characterization, manufacturing and operation.

Balazinska is principal investigator; co-principal investigators are chemical engineering professor , research associate professor ; and , senior data scientist with the UW’s eScience Institute.

For more information, contact Balazinska at magda@cs.washington.edu.

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, professor in the College of Education, has been awarded a of $1,400,000 across four years from the for a study to identify “malleable” reading factors — such as awareness and letter sounds — among elementary students with intellectual disabilities, with the long-term aim of developing effective literacy interventions.

Hudson is principal investigator; co-principal investigators, also in education, are assistant professor and associate professor . The National Center for Special Education Research is one of four research centers of . The grant was awarded in August.

Listen to a spring 2019 with Hudson about the effectiveness of interventions designed to help young readers on the autism spectrum.

For more information, contact Hudson at rhudson@uw.edu

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, clinical assistant professor of in the Department of Global Health — which bridges the 91̽��schools of public health and medicine — has received a $200,000 grant from the Social Media and Adolescent Health Research Team at the University of Wisconsin-Madison. She will develop and pilot a program that uses social media to prevent depression in young pregnant women or women who have recently given birth.
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91̽��Notebook is a section of the 91̽��News site dedicated to telling stories of the good work done by faculty and staff at the 91̽��. Read all posts here.

Each night, high-definition cameras mounted to telescopes collect terabytes of data about objects in the sky. Each day, scientists sequence the genomes of people, animals, plants and microbes for biomedical and evolutionary research. Each year, the Large Hadron Collider of data on particle collisions.

Science has become a big-data endeavor. But scientists are not universally adept in “data science” — the computing and statistical skillsets needed to handle, sort, analyze and draw conclusions from big data. The shortage of know-how in data science can hamper research, medicine and .

Now a team from the 91̽��, New York University and the University of California, Berkeley has developed an interactive workshop in data science for researchers at multiple stages of their careers. The course format, called “hack week,” blends elements from both traditional lecture-style pedagogy with participant-driven projects. The most recent was a neuroscience-themed event held in July on the 91̽��campus. As the team reports in a published Aug. 20 in the , participants rated the hack weeks as opportunities to learn about new concepts, foster new connections, share data openly, and develop skills and work on problems that will positively affect their day-to-day research lives.

“The idea behind hack week was to bring together people who were interested in data science and give them a place to meet, talk and exchange ideas,” said lead and corresponding author , associate director of the UW’s astronomy-focused . “But instead of a traditional format with experts lecturing nonexperts, this would allow participants to mingle more and teach one another.”

Huppenkothen was involved in the inaugural hack week event, “Astro Data Hack Week,” held at the 91̽��in 2014. That event brought together big-data researchers in astrophysics and cosmology. Since then, the team has held four additional events, three “” events for neuroscience and two “” events for the geosciences.

All hack week events have the same basic design and organizing principles. They usually commence with some structured periods for instruction, and then shift toward time for participant-driven, open-ended projects, as well as peer networking and free discussion. The projects can resemble a , but with greater emphasis on collaboration and learning rather than specific outcomes. Hack week participants tackle their projects in smaller groups, with organizers circulating to observe and provide feedback or encouragement.

The projects range from experiments that the participants brought from their home institutions to ideas that come up during the course. One project from the inaugural Astro Hack Week, for example, eventually became Stingray, a software project to provide algorithms to analyze time-series data in astronomy. At last month’s Neurohackademy, a new two-week version of Neuro Hack Week, one team worked on developing common ways to analyze different types of MRI scans.

The events’ open-ended structure places greater responsibility on the organizers of each hack week.

“A hack week takes a different kind of preparation, because you don’t have the security of ‘falling back’ on the structure of traditional talks and lectures,” said co-author Anthony Arendt, a research scientist with the 91̽�� who has organized Geo Hack Week. “You have to set up ways to encourage participants at all levels of ability and comfort — creating a welcoming space for everyone to pitch ideas.”

Most hack weeks organized by the team cap the number of participants at 60. Organizers also strive to select participants to maximize diversity — including scientists of different abilities, backgrounds and at different stages of their careers. Participants also agree to abide by a code of conduct that emphasizes respect and positive interactions.

In surveys conducted after eight hack weeks, participants ranked the events positively as spaces to learn, teach, network and foster relationships. More than three-quarters ranked the hack weeks as successful learning experiences, while two-thirds reported teaching skills to someone else. This feedback was constant across different backgrounds, showing that the unique format of hack weeks helps all participants feel included, said Huppenkothen.

“Now we want other scientific communities to learn about our experiences and see how they might start organizing their own events,” said Huppenkothen. “We also want feedback from other communities — both good and bad — and to widen the dialogue about data science and skill development.”

Their includes supplementary materials detailing the hack week experiences and advice for other groups interested in starting their own workshops.

Participants gave hack weeks high scores for promoting open-science principles — in which researchers publicly post and share their datasets, code and methods. Open science principles are critical to addressing challenges that researchers face in making their research more reproducible, said co-author , a data scientist with the 91̽�� and co-organizer of the recent Neurohackademy, along with at the University of Texas at Austin.

“One of our goals with the hack week format is to elevate the quality of science being done,” said Rokem. “The best way to do that is to try out ideas and share what you’ve learned.”

Additional co-authors are David Hogg with the NYU Center for Data Science; Karthik Ram at the Berkeley Institute for Data Science at the University of California, Berkeley; and Jake VanderPlas at the 91̽��eScience Institute. The research was funded by the National Institutes of Health; the 91̽��; New York University; the University of California, Berkeley; the Charles and Lisa Simonyi Fund for Arts and Sciences; and the Washington Research Foundation.

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For more information, contact Huppenkothen at dhuppenk@uw.edu and Rokem at arokem@gmail.com.

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.