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University of Maryland Researchers Record Brainwaves to Measure ‘cybersickness’



In a first-of-its-kind study, the UMD team used electroencephalography (EEG) to better understand and work toward solutions for VR-induced discomfort

If a virtual world has ever left you feeling nauseous or disorientated, you’re familiar with cybersickness, and you’re hardly alone. The intensity of virtual reality (VR)-whether that’s standing on the edge of a waterfall in Yosemite or engaging in tank combat with your friends-creates a stomach-churning challenge for 30-80% of users.

In a first-of-its kind study, researchers at the University of Maryland recorded VR users’ brain activity using electroencephalography (EEG) to better understand and work toward solutions to prevent cybersickness. The research was conducted by Eric Krokos, who received his Ph.D. in computer science in 2018, and Amitabh Varshney, a professor of computer science and dean of UMD’s College of Computer, Mathematical, and Natural Sciences.

Their study, “Quantifying VR cybersickness using EEG,” was recently published in the journal Virtual Reality.

The term cybersickness derives from motion sickness, but instead of physical movement, it’s the perception of movement in a virtual environment that triggers physical symptoms such as nausea and disorientation. While there are several theories about why it occurs, the lack of a systematic, quantified way of studying cybersickness has hampered progress that could help make VR accessible to a broader population.

Krokos and Varshney are among the first to use EEG-which records brain activity through sensors on the scalp-to measure and quantify cybersickness for VR users. They were able to establish a correlation between the recorded brain activity and self-reported symptoms of their participants. The work provides a new benchmark-helping cognitive psychologists, game developers and physicians as they seek to learn more about cybersickness and how to alleviate it.

“Establishing a strong correlation between cybersickness and EEG-measured brain activity is the first step toward interactively characterizing and mitigating cybersickness, and improving the VR experience for all,” Varshney said.

EEG headsets have been widely used to measure motion sickness, yet prior research on cybersickness has relied on users to accurately recall their symptoms through questionnaires filled out after users have removed their headsets and left the immersive environment.

The UMD researchers said that such methods provide only qualitative data, making it difficult to assess in real time which movements or attributes of the virtual environment are affecting users.

Another complication is that not all people suffer from the same physical symptoms when experiencing cybersickness, and cybersickness may not be the only cause of these symptoms.

Without the existence of a reliable tool to measure and interactively quantify cybersickness, understanding and mitigating it remains a challenge, said Varshney, a leading researcher in immersive technologies and co-director of the Maryland Blended Reality Center.

For the UMD study, participants were fitted with both a VR headset and an EEG recording device, then experienced a minute-long virtual fly-through of a futuristic spaceport. The simulation included quick drops and gyrating turns designed to evoke a moderate degree of cybersickness.

Participants also self-reported their level of discomfort in real time with a joystick. This helped the researchers identify which segments of the fly-through intensified users’ symptoms.


Written by Maria Herd

“Quantifying VR Cybersickness using EEG,” was published in May 2021 in the academic journal Springer Virtual Reality.

This work was supported by the National Science Foundation (Grant Nos. 14-29404 and 15-64212), the state of Maryland’s MPower Initiative and the NVIDIA CUDA Center of Excellence program. The content of this article does not necessarily reflect the views of these organizations.




African American Breast Cancer Patients Less Likely to Receive Genetic Counseling, Testing



Researchers at Washington University School of Medicine in St. Louis have surveyed cancer doctors to identify differences in physician attitudes and beliefs that may contribute to a gap in referrals to genetic counseling and testing between Black women and white women with breast cancer.

Researchers at Washington University School of Medicine in St. Louis have surveyed cancer doctors to identify differences in physician attitudes and beliefs that may contribute to a gap in referrals to genetic counseling and testing between Black women and white women with breast cancer.

The breast cancer mortality rate is 41% higher for Black women than white women. Part of the reason for that difference may be that white women are almost five times more likely than Black women to be referred for genetic counseling and testing, suggesting racial disparities in how some doctors refer patients for those services.

Genetic counseling and testing can identify those at high risk for developing breast cancer. It also can be used to personalize cancer prevention for individual patients, and it can guide treatment in those who have hereditary breast cancer caused by gene mutations. Hereditary forms of breast cancer — which account for 5% to 10% of breast cancer cases — affect Black and white women at about the same rates.

The new findings, published Oct. 18 in the Journal of Clinical Oncology, revealed that physicians believe Black women experience more barriers to genetic counseling and testing. The doctors’ self-reported practices with regard to counseling and testing for Black women also indicated that many believed Black women would be less likely to comply with recommendations for genetic counseling and testing.

“For breast cancer patients with genetic mutations, the treatment is different; the surgical options are different; the screening and surveillance going forward is very different — so it’s important to identify those patients through genetic counseling and testing services,” said first author Foluso O. Ademuyiwa, MD, an associate professor of medical oncology. “We wanted to learn why Black women are not being referred for this type of care as frequently. We hope these findings might help change that trajectory. We hope that Black women won’t continue to be less likely to receive information and referrals that may help save their lives and even the lives of some of their family members.”

The researchers surveyed 277 cancer doctors around the country to learn why referrals are made so much less frequently for Black women. Of the doctors surveyed, 67% were white, less than 4% were Black, almost 59% were female and almost 62% practiced at academic medical centers. Although fewer than 2% of doctors surveyed said they were less likely to refer a Black patient than a white patient, other research has found that Black patients are being referred for genetic counseling and testing less than 60% of the time that such testing is recommended by National Comprehensive Cancer Network guidelines. That compares with a referral rate of 93% for white patients.

Ademuyiwa and her colleagues asked doctors whether they believed Black patients were more likely than white patients to refuse genetic counseling and testing. Almost 26% said yes. Another 46% of respondents cited cost as a barrier for Black patients and a potential reason not to refer. Almost 59% said that their Black patients were less likely to trust their doctors’ diagnoses and referrals than white patients were.

“The survey indicated that 14% of physicians felt their patients, in general — regardless of race — probably would not follow through with genetic testing and counseling recommendations,” said Ademuyiwa, who treats patients at Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital. “But more than twice as many, 31%, thought their Black patients would be less likely than white patients to comply with their recommendations for genetic counseling and testing. We feel there is some bias here, and we want to understand how we as physicians can do better in closing this gap.”

Women with mutations in the BRCA1 and BRCA2 genes have a 55% to 85% chance of developing breast cancer during their lifetimes, but several studies have shown that doctors do not refer many of their patients for counseling and testing that could identify those mutations and help guide their treatment.

One issue, Ademuyiwa said, is time. In 30 minutes with a new patient, some doctors may prefer to focus on upcoming surgery or chemotherapy rather than discuss the pros and cons of genetic counseling and testing.

“In a prior study of 250 Black women with breast cancer in the St. Louis region, we found that among women who were eligible for genetic testing based on the National Comprehensive Cancer Network guidelines, only 60% had any testing done,” said Laura Jean Bierut, MD, the Alumni Endowed Professor of Psychiatry and the study’s senior author. “Why were 40% of these women not referred? It’s important to learn why so many patients may not get access to these services.”

Data also suggest Black patients are more comfortable working with providers of the same race, but only about 3% of U.S. oncologists are Black. Based on the results of the new survey, Ademuyiwa and her colleagues are launching a pilot study at Siteman Cancer Center. Facilitators will be matched with patients of the same race and will perform in-person genetic screens of such patients. The facilitators then will share information from the screenings — including family histories and other pertinent details — with treating physicians to see whether this strategy might increase rates of referrals to genetic counseling and testing.

“We want doctors to check themselves, to take stock of what they have been doing and to take a little more time to make sure they are referring patients eligible for genetic counseling and testing,” Ademuyiwa said. “Correctly referring women, regardless of their skin color, is very important and can improve survival for breast cancer patients of all colors in a very real way.”


Ademuyiwa FO, Salyer P, Tao Y, Luo J, Hensing WL, Afolalu A, Peterson LL, Weilbaecher K, Housten AJ, Baumann AA, Desai M, Jones S, Linnenbringer E, Plichta J, Bierut LJ. Genetic counseling and testing in African American patients with breast cancer. Journal of Clinical Oncology, Oct. 18, 2021.

This work was supported by the American Society of Clinical Oncology’s Research Survey Pool.

Washington University School of Medicine’s 1,700 faculty physicians also are the medical staff of Barnes-Jewish and St. Louis Children’s hospitals. The School of Medicine is a leader in medical research, teaching and patient care, consistently ranking among the top medical schools in the nation by U.S. News & World Report. Through its affiliations with Barnes-Jewish and St. Louis Children’s hospitals, the School of Medicine is linked to BJC HealthCare.

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Is Fresh Breast Milk Better for Preemies Than Pumped and Stored?



Carrie-Ellen Briere studies the cells in human milk, a passion inspired by years of working as a clinical nurse with sick and premature babies in the neonatal intensive care unit (NICU).

Carrie-Ellen Briere studies the cells in human milk, a passion inspired by years of working as a clinical nurse with sick and premature babies in the neonatal intensive care unit (NICU).

The University of Massachusetts Amherst assistant professor of nursing is researching whether fresh breast milk can be shown to provide more benefits to NICU babies than breast milk pumped from the mother that has been refrigerated or frozen. She’s also advancing research that suggests in animal models that breast cells can act like stem cells, turning into functioning cells in such organs as the liver and brain.

To pursue her lab research mentored by a team of veteran UMass Amherst professors, Briere has been awarded a five-year, $730,000 career development grant from the National Institutes of Health’s (NIH) National Institute of Child Health and Human Development.

“A typical newborn breastfeeds directly at the breast, so they’re getting milk right as nature intended,” Briere says. “When babies are in the NICU, moms usually have to pump milk that we refrigerate or freeze because not all the babies who are born early or are sick can eat it right away so we save it for later use. Many preterm infants also have extra nutrients added into milk, so their milk is often prepared in bulk at a set time of day, instead of individually at feedings with fresh milk.

“We know that human milk is protective and beneficial for infants both in the NICU and afterwards, but we’re not quite sure how storing impacts different components of the milk we’re giving to babies and whether it may be taking away some of its beneficial properties.”

Briere is being mentored by an interdisciplinary UMass Amherst team, including breast milk and breast cancer researchers Kathleen Arcaro and D. Joseph Jerry, both professors of veterinary and animal sciences; and nutrition scientist David Sela, associate professor of food science and director of the Fergus M. Clydesdale Center for Foods for Health and Wellness. (Briere is also working with Sela in his NIH-funded investigation into how nitrogen in human milk is used by beneficial microbes in the infant gut.) In addition to the UMass Amherst researchers, Dr. Laura Madore, an assistant professor of pediatrics at UMass Medical School-Baystate and attending neonatologist at Baystate Children’s Hospital, is another member of Briere’s guiding team.

Briere will conduct experiments using fresh, refrigerated and frozen human milk and compare their impacts on lab-based intestinal cells in which an infection has been introduced. “We want to prioritize using fresh milk in the NICU but there are a lot of barriers to that,” she says. “Having actual data showing that having fresh milk is best would help change clinical practice to make sure our sickest little babies are getting the best milk possible, especially early in their life when they’re most vulnerable.”

One way to make that happen, she says, is by coordinating family visit times with feeding times.

In related research, Briere will design new studies in animal models that examine how specific breast milk cells end up moving from the gut and transforming into functional cells in other organs. This could have significant implications for preterm babies whose organs are underdeveloped, she notes.

“Studies have shown that cells in milk, when ingested in animal models, don’t just get swallowed and hang out in the gut,” Briere says. “They actually are finding milk cells in various organs throughout the mouse pups. Somehow the milk cells are traveling through the digestive system and landing in some specific organs as actual functioning cells.”

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Scientists Show How AI May Spot Unseen Signs of Heart Failure



A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.

Credit: Courtesy of Glicksberg and Nadkarni labs, Mount Sinai, N.Y., N.Y.

A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.

“We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data,” said Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai, and a senior author of the study published in the Journal of the American College of Cardiology: Cardiovascular Imaging. “Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable quicker diagnosis of heart failure.”

The study was led by Akhil Vaid, MD, a postdoctoral scholar who works in both the Glicksberg lab and one led by Girish N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital Medicine (D3M), and a senior author of the study.

Affecting about 6.2 million Americans, heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs. For years doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient may be experiencing heart failure. While helpful, echocardiograms can be labor-intensive procedures that are only offered at select hospitals.

However, recent breakthroughs in artificial intelligence suggest that electrocardiograms–a widely used electrical recording device–could be a fast and readily available alternative in these cases. For instance, many studies have shown how a “deep-learning” algorithm can detect weakness in the heart’s left ventricle, which pushes freshly oxygenated blood out to the rest of the body. In this study, the researchers described the development of an algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs.

“Although appealing, traditionally it has been challenging for physicians to use ECGs to diagnose heart failure. This is partly because there is no established diagnostic criteria for these assessments and because some changes in ECG readouts are simply too subtle for the human eye to detect,” said Dr. Nadkarni. “This study represents an exciting step forward in finding information hidden within the ECG data which can lead to better screening and treatment paradigms using a relatively simple and widely available test.”

Typically, an electrocardiogram involves a two-step process. Wire leads are taped to different parts of a patient’s chest and within minutes a specially designed, portable machine prints out a series of squiggly lines, or waveforms, representing the heart’s electrical activity. These machines can be found in most hospitals and ambulances throughout the United States and require minimal training to operate.

For this study, the researchers programmed a computer to read patient electrocardiograms along with data extracted from written reports summarizing the results of corresponding echocardiograms taken from the same patients. In this situation, the written reports acted as a standard set of data for the computer to compare with the electrocardiogram data and learn how to spot weaker hearts.

Natural language processing programs helped the computer extract data from the written reports. Meanwhile, special neural networks capable of discovering patterns in images were incorporated to help the algorithm learn to recognize pumping strengths.

“We wanted to push the state of the art by developing AI capable of understanding the entire heart easily and inexpensively,” said Dr. Vaid.

The computer then read more than 700,000 electrocardiograms and echocardiogram reports obtained from 150,000 Mount Sinai Health System patients from 2003 to 2020. Data from four hospitals was used to train the computer, whereas data from a fifth one was used to test how the algorithm would perform in a different experimental setting.

“A potential advantage of this study is that it involved one of the largest collections of ECGs from one of the most diverse patient populations in the world,” said Dr. Nadkarni.

Initial results suggested that the algorithm was effective at predicting which patients would have either healthy or very weak left ventricles. Here strength was defined by left ventricle ejection fraction, an estimate of how much fluid the ventricle pumps out with each beat as observed on echocardiograms. Healthy hearts have an ejection fraction of 50 percent or greater while weak hearts have ones that are equal to or below 40 percent.

The algorithm was 94 percent accurate at predicting which patients had a healthy ejection fraction and 87 percent accurate at predicting those who had an ejection fraction that was below 40 percent.

However the algorithm was not as effective at predicting which patients would have slightly weakened hearts. In this case, the program was 73 percent accurate at predicting the patients who had an ejection fraction that was between 40 and 50 percent.

Further results suggested that the algorithm also learned to detect right valve weaknesses from the electrocardiograms. In this case, weakness was defined by more descriptive terms extracted from the echocardiogram reports. Here the algorithm was 84 percent accurate at predicting which patients had weak right valves.

“Our results suggested that this algorithm may eventually help doctors correctly diagnose failure on either side of the heart,” Dr. Vaid said.

Finally, additional analysis suggested that the algorithm may be effective at detecting heart weakness in all patients, regardless of race and gender.

“Our results suggest that this algorithm could be a useful tool for helping clinical practitioners combat heart failure suffered by a variety of patients,” added Dr. Glicksberg. “We are in the process of carefully designing prospective trials to test out its effectiveness in a more real-world setting.”

This study was supported by the National Institutes of Health (TR001433).


Vaid, A., et al., Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram, Journal of the American College of Cardiology: Cardiovascular Imaging, October 13, 2021, DOI: 10.1016/j.jcmg.2021.08.004.

About the Mount Sinai Health System

The Mount Sinai Health System is New York City’s largest academic medical system, encompassing eight hospitals, a leading medical school, and a vast network of ambulatory practices throughout the greater New York region. Mount Sinai advances medicine and health through unrivaled education and translational research and discovery to deliver care that is the safest, highest-quality, most accessible and equitable, and the best value of any health system in the nation. The Health System includes approximately 7,300 primary and specialty care physicians; 13 joint-venture ambulatory surgery centers; more than 415 ambulatory practices throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. The Mount Sinai Hospital is ranked on U.S. News & World Report’s “Honor Roll” of the top 20 U.S. hospitals and is top in the nation by specialty: No. 1 in Geriatrics and top 20 in Cardiology/Heart Surgery, Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. Mount Sinai Kravis Children’s Hospital is ranked in U.S. News & World Report’s “Best Children’s Hospitals” among the country’s best in four out of 10 pediatric specialties. The Icahn School of Medicine is one of three medical schools that have earned distinction by multiple indicators: ranked in the top 20 by U.S. News & World Report’s “Best Medical Schools,” aligned with a U.S. News & World Report “Honor Roll” Hospital, and No. 14 in the nation for National Institutes of Health funding. Newsweek’s “The World’s Best Smart Hospitals” ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.

For more information, visit or find Mount Sinai on Facebook, Twitter and YouTube.


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