The nature of scientific progress is often summarized by the Isaac Newton quotation, “If I have seen further it is by standing on the shoulders of giants.” Each new study draws on dozens of earlier ones, forming a chain of knowledge stretching back to Newton and the scientific giants his work referenced.
Scientific publishing and referencing has become more formal since Newton’s time, with databases of citations allowing for sophisticated quantitative analyses of that flow of information between researchers.
The Institute for Scientific Information and the Web of Science Group provide a yearly snapshot of this flow, publishing a list of the researchers who are in the top 1 percent of their respective fields when it comes to the number of times their work has been cited.
Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering, and Jason Burdick, Robert D. Bent Professor in the department of Bioengineering, are among the 6,389 researchers named to the 2020 list.
Bassett is a pioneer in the field of network neuroscience, which incorporates elements of mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits. Burdick is an expert in tissue engineering and the design of biomaterials for regenerative medicine; by precisely tailoring the microenvironment within these materials, they can influence stem cell differentiation or trigger the release of therapeutics.
Parkes will use the BBRF’s support to continue his research examining the link between the symptoms of mental illness and the brain. In particular, he seeks to uncover how individual patterns of abnormal neurodevelopment link to, and predict, the emergence of psychosis symptoms through childhood and adolescence using longitudinal data. In turn, Parkes’ work will discover prognostic biomarkers for the psychosis spectrum that will help inform clinical outcome tracking.
“I am honored to have been selected for a Young Investigator Grant from the BBRF this year,” Parkes says. “This award will support me to conduct research that I believe will make real inroads into understanding the pathways that link abnormalities in neurodevelopment to the symptoms of psychosis. I feel grateful for the opportunity to complete my postdoctoral training at Penn. Penn has connected me with wonderful people who I’m sure will be lifelong mentors, colleagues, and peers.”
The BBRF Young Investigator Grants are valued at more than $10.3 million and are awarded annually to 150 of the world’s most promising young scientists to support the work of early career investigators with innovative ideas for groundbreaking neurobiological research seeking to identify causes, improve treatments, and develop prevention strategies for psychiatric disorders.
Read more about the BBRF 2020 Young Investigators here.
In a ‘Wired’ feature, Bassett helps explain the growing field of network neuroscience and how the form and function of the brain are connected.
Early attempts to understand how the brain works included the pseudoscience of phrenology, which theorized that various mental functions could be determined through the shape of the skull. While those theories have long been debunked, modern neuroscience has shown a kernel of truth to them: those functions are highly localized to different regions of the brain.
Now, Danielle Bassett, Professor of J. Peter Skirkanich Professor of Bioengineering and Electrical and Systems Engineering, is pioneering a new subfield that goes even deeper into the connection between the brain’s form and function: network neuroscience.
In a recent feature article in Wired, Bassett explains the concepts behind this new subfield. While prior understanding has long relied on the idea that certain areas of the brain control certain functions, Bassett and other network neuroscientists are using advances in imaging and machine learning to reveal the role the connections between those areas play.
For Bassett, one of the first indicators that these connections mattered more than previously realized was the shape of the neurons themselves.
Speaking with Wired’s Grace Huckins, Bassett says:
“Neurons are not spherical — neurons have a cell body, and then they have this long tail that allows them to connect to many other cells. You can even look at the morphology of the neuron and say, ‘Oh, well, connectivity has to matter. Otherwise, it wouldn’t look like this.’”
Read more about Bassett and the field of network neuroscience in Wired.
Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering, has been called the “doyenne of network neuroscience.” The burgeoning field applies insights from the field of network science, which studies how the structure of networks relate to their performance, to the billions of neuronal connections that make up the brain.
Much of Basset’s research draws on mathematical and engineering principles to better understand how mental traits arise, but also applies them more broadly to other challenges in neuroscience.
The researchers used machine learning techniques to create a new classification system for neurodegenerative diseases, one which may redraw the boundaries between them and help explain clinical differences in patients who received the same diagnoses.
BioWorld’s Anette Breindl spoke with Bassett about the team’s findings.
Now, investigators have developed a new approach to classifying neurodegenerative disorders that used the overall patterns of protein aggregation, rather than specific proteins, to define six clusters of patients that crossed traditional diagnostic categories.
“We find that perhaps the way that clinicians have been diagnosing these disorders… is not necessarily the way these disorders work,” Danielle Bassett told BioWorld. “The way we’ve been trying to carve nature at joints is not the way that nature has joints. The joints are elsewhere.”
Continue reading Breindl’s article, “For neurodegeneration, a different way to slice the pie,” at BioWorld.
Among the key faculty involved in this new center is J. Peter Skirkanich Professor of Bioengineering Danielle Bassett. Bassett’s Complex Systems Lab studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Bassett, along with Assistant Professor of Psychiatry Desmond Oathes, will work to:
understand how TMS [i.e. transcranial magnetic stimulation] might improve working memory in healthy adults and those with ADHD by combining network control theory (a set of concepts and principles employed in engineering), magnetic stimulation of the brain, and functional brain imaging.
An interdisciplinary research team has found statistical evidence of women being under-cited in academic literature. They are now studying similar effects along racial lines.
By Izzy Lopez
Scientific papers are the backbone of a research community and the citation of those papers sparks conversation in a given field. This cycle of publication and citation leads to new knowledge, but what happens when implicit discrimination in a field leads to papers by minority scholars being cited less often than their counterparts? A new team of researchers has come together to ask this question and dig into the numbers of gender and racial bias in neuroscience.
The team members include physicist and neuroscientist Danielle Bassett, J. Peter Skirkanich Professor of Bioengineering at the University of Pennsylvania, with secondary appointments in the Departments of Neurology and Psychiatry in Penn’s Perelman School of Medicine, statistician Jordan Dworkin, then a graduate student in Penn Medicine’s Department of Biostatistics, Epidemiology and Bioinformatics, and ethicist Perry Zurn, an Assistant Professor of Philosophy at American University.
Their study on gender bias, which recently appeared in Nature Neuroscience, reports on the extent and drivers of gender imbalance in neuroscience reference lists. The team has also published a perspective paper in Neuron that makes practical recommendations for improving awareness of this issue and correcting for biases.
They are now working on a second study, led by Maxwell Bertolero, a postdoctoral researcher in Bassett’s lab, that considers the extent and drivers of racial imbalances in neuroscience reference lists.
Together, Bassett, Dworkin and Zurn are using their combined research strengths to uncover the under-citation of women or otherwise minority-led papers in neuroscience and to assess its significance. This research is fundamental in highlighting a true gap in representation in research paper citations, which can have detrimental effects for women and other minorities leading science. In addition, they provide actionable steps to address the problem and build a more equitable future.
Your research team is a distinctive one. How did you come together for a study about gender discrimination in neuroscience citations?
Jordan Dworkin: It was a fortunate coincidence. In the run-up to a big neuroscience conference, I started seeing discussions on Twitter about gender-based discrimination in neuroscience. There were stories being shared of women’s papers being overlooked and reviewers seeing reference lists that were almost entirely made up of men. It was illuminating, especially because some people in the discussion were hesitant to take those experiences at face value. This skepticism, and occasional combativeness, seemed to stem from the view that citations are an untouchable, scientific bastion where researchers’ decisions are fully objective. The tension between that view and scholars’ lived experiences encouraged me to explore the existing literature on this issue.
As it turns out, there is really strong literature on issues of diversity and citation in science. Some disciplines have done field-specific investigations, such as the foundational studies in political science, international relations, and economics, but there wasn’t yet any research in neuroscience. Since biomedical sciences often have different approaches to citation, it seemed that it would be worth doing a deeper neuroscience-specific investigation to give quantitative backing to the issue of gender bias in neuroscience research.
Danielle Bassett: When Jordan and I started working together on this project, I knew it was important. To do it right, we needed to present the information in a way that made it actionable, with clear recommendations about how each of us as scientists can help address the issue. We also needed to add someone to the team with expertise in gender theory and research ethics. We especially wanted to make sure we were discussing gender bias in a way that was informed by recent advances in gender studies. That’s when we brought Perry in.
Perry Zurn: I’m a philosopher by training, with a focus on ethics and politics. Citations are both an ethical and a political issue. Citations reflect whose questions and whose contributions are recognized as important in the scholarly conversation. As such, citations can either bring in marginalized voices, voices that have been historically excluded from a conversation, or they can simply replicate that exclusion. My own field of philosophy has just as much of a problem with gender and racial diversity as STEM fields, something Dani and I have been talking about for a long time. This work seemed like a natural point of collaboration.
Describe this study and what it means for promoting gender diversity in neuroscience.
Bassett: For years now, various scholars and activists in science have drawn attention to issues of gender and racial inequities in the field. Most of these conversations, however, have placed responsibility for change in the hands of people in power, such as journal editors, grant reviewers, department chairs, presidents of scientific societies, etc. But many of the imbalances people notice, whether in conversation with peers or through studies like ours, are perpetuated by researchers at all levels. Given that every research project is built on prior research, and therefore every paper has a reference list of citations, every researcher can make a difference. Who we choose to cite matters.
Dworkin: To understand the role of gender in citation practices, we looked at the authors and reference lists of articles published in five top neuroscience journals since 1995. We accounted for self-citations, and various potentially relevant characteristics of papers, and we found that women-led papers are under-cited relative to what would be expected if gender was not a consideration in citation behavior. Importantly, we also found that the under-citation of women-led papers is driven largely by the citation behavior of men-led teams. We also found that this trend is getting worse over time, because the field is getting more diverse while citation rates are generally staying the same.
For a very simple example, if there were 10 women and 90 men neuroscientists in 1980, then citing 10% women would be roughly proportional. But with a diversifying field, say there are now 200 women and 200 men neuroscientists and citations are still 10% women. Sure, the percentage of women cited didn’t go down, but that percentage is now vastly lower than the true percentage in the field. That’s a dramatic example, but it shows you that if we’re going to call for equality in scientific citation, the number of women-led papers on a given reference list should reflect, or even exceed, the number of available and relevant women-led papers in a field, and our work found that it does not.
Bassett: This under-citation of women scientists is a key issue because the gaps in the amount of engagement that women’s work receives could have detrimental downstream effects on conference invitations, grant and fellowship awards, tenure and promotion, inclusion in syllabi, and even student evaluations. As a result, understanding and eliminating gender bias in citation practices is vital for addressing gender imbalances in a field.
Why are citations important to gender representation in neuroscience?
Dworkin: Unlike hiring and grant funding, citations are something every researcher participates in. For example, as a graduate student I did not have any role on a faculty search committee, or any power in an academic society to decide on conference speakers, but I still have reference lists in all my papers. Citations are a unique area where all researchers play a direct role, where each person has a chance to reflect on their own practices and use those practices to create change in their field. Their ubiquity means that citations function as a conversation within a field, and their presence or absence can signal whose work is valued and whose is not. On a more concrete level, citations are often used as metrics for a variety of important, potentially career-defining, decisions.
Bassett: There are a lot of underrepresented scholars who have fantastic ideas and write really interesting papers but they’re not being acknowledged — and cited — in the way they deserve. And there are great role models for all the young women who are thinking about going into science, but unless the older women scientists are being cited, the younger ones will never see them. Without serious changes in the field, and a deep commitment to gender and racial diversity, many young women and minority scientists won’t stick with it, they won’t be hired, they won’t be promoted, and they won’t be put in the textbooks.
Zurn: Exactly. I think it’s important not only to think about who we’re citing as leading scientists, but also what sorts of people we’re representing as scientists at all. If you are looking at neuroscience as a field and you see predominantly white cisgender men in the research labs and the reference lists, then you begin to think that is what a neuroscientist looks like. But this homogeneity is neither representative of an increasingly diverse field like neuroscience, nor supportive of continuing efforts to diversify STEM in general. We need to expand what a scientist looks like and citations are one way to do that.
Danielle Bassett also has appointments in Penn Engineering’s Department of Electrical and Systems Engineering and Penn Arts & Sciences Department of Physics and Astronomy.
Jordan Dworkin is now an Assistant Professor of Clinical Biostatistics in the Department of Psychiatry at Columbia University.
Kristin Linn, Assistant Professor of Biostatics, Russell Shinohara, Associate Professor of Biostatistics, and Erin Teich, a postdoctoral researcher in Bassett’s lab, also contributed to the study published in Nature Neuroscience. It was supported by the National Institute of Neurological Disorders and Stroke through grants R01 NS085211 and R01 NS060910, the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation through CAREER Award PHY-1554488.
New research finds that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share large amounts of information efficiently.
By Erica K. Brockmeier
To an English scholar or avid reader, the Shakespeare Canon represents some of the greatest literary works of the English language. To a network scientist, Shakespeare’s 37 plays and the 884,421 words they contain also represent a massively complex communication network. Network scientists, who employ math, physics, and computer science to study vast and interconnected systems, are tasked with using statistically rigorous approaches to understand how complex networks, like all of Shakespeare, convey information to the human brain.
New research published in Nature Physics uses tools from network science to explain how complex communication networks can efficiently convey large amounts of information to the human brain. Conducted by postdoc Christopher Lynn, graduate students Ari Kahn and Lia Papadopoulos, and professor Danielle S. Bassett, the study found that different types of networks, including those found in works of literature, musical pieces, and social connections, have a similar underlying structure that allows them to share information rapidly and efficiently.
Technically speaking, a network is simply a statistical and graphical representation of connections, known as edges, between different endpoints, called nodes. In pieces of literature, for example, a node can be a word, and an edge can connect words when they appear next to each other (“my” — “kingdom” — “for” — “a” — “horse”) or when they convey similar ideas or concepts (“yellow” — “orange” — “red”).
The advantage of using network science to study things like languages, says Lynn, is that once relationships are defined on a small scale, researchers can use those connections to make inferences about a network’s structure on a much larger scale. “Once you define the nodes and edges, you can zoom out and start to ask about what the structure of this whole object looks like and why it has that specific structure,” says Lynn.
Building on the group’s recent study that models how the brain processes complex information, the researchers developed a new analytical framework for determining how much information a network conveys and how efficient it is in conveying that information. “In order to calculate the efficiency of the communication, you need a model of how humans receive the information,” he says.
Researchers develop a new model for how the brain processes complex information: by striking a balance between accuracy and simplicity while making mistakes along the way.
By Erica K. Brockmeier
The human brain is a highly advanced information processor composed of more than 86 billion neurons. Humans are adept at recognizing patterns from complex networks, such as languages, without any formal instruction. Previously, cognitive scientists tried to explain this ability by depicting the brain as a highly optimized computer, but there is now discussion among neuroscientists that this model might not accurately reflect how the brain works.
Now, Penn researchers have developed a different model for how the brain interprets patterns from complex networks. Published in Nature Communications, this new model shows that the ability to detect patterns stems in part from the brain’s goal to represent things in the simplest way possible. Their model depicts the brain as constantly balancing accuracy with simplicity when making decisions. The work was conducted by physics Ph.D. student Christopher Lynn, neuroscience Ph.D. student Ari Kahn, and Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering.
This new model is built upon the idea that people make mistakes while trying to make sense of patterns, and these errors are essential to get a glimpse of the bigger picture. “If you look at a pointillist painting up close, you can correctly identify every dot. If you step back 20 feet, the details get fuzzy, but you’ll gain a better sense of the overall structure,” says Lynn.
To test their hypothesis, the researchers ran a set of experiments similar to a previous study by Kahn. That study found that when participants were shown repeating elements in a sequence, such as A-B-C-B, etc., they were automatically sensitive to certain patterns without being explicitly aware that the patterns existed. “If you experience a sequence of information, such as listening to speech, you can pick up on certain statistics between elements without being aware of what those statistics are,” says Kahn.
To understand how the brain automatically understands such complex associations within sequences, 360 study participants were shown a computer screen with five gray squares corresponding to five keys on a keyboard. As two of the five squares changed from gray to red, the participants had to strike the computer keys that corresponded to the changing squares. For the participants, the pattern of color-changing squares was random, but the sequences were actually generated using two kinds of networks.
The researchers found that the structure of the network impacted how quickly the participants could respond to the stimuli, an indication of their expectations of the underlying patterns. Responses were quicker when participants were shown sequences that were generated using a modular network compared to sequences coming from a lattice network.
Lydon-Staley started out studying English and Psychology in his undergraduate education, going on to pursue a Ph.D. from Penn State University in Human Development and Family Studies. What brought him to Bassett’s lab was his interest in using cognitive neuroscience to understand the brain patterns and behaviors behind substance abuse and addiction. There, Lydon-Staley examined networks of nicotine withdrawal behaviors, how those behaviors impact each other, and what information they might hold about how to help smokers in their quit attempts. “David’s breadth of interest is only rivalled by his expansive expertise and bottomless enthusiasm,” says Bassett. “I feel incredibly lucky to have had the chance to work with him.”
In his new role at Annenberg, Lydon-Staley will launch the Addiction, Health, and Adolescence Lab, or “AHA!” for short. “My recent work examines engagement with new media during the course of daily life, and how the information sought and encountered relates to both curiosity and substance use,” he says. Lydon-Staley’s new lab will use methods like experience-sampling and functional Magnetic Resonance Imaging to understand brain and behavior, while drawing on theories and tools from communication, psychology, cognitive neuroscience, network science, and more.
Even though Lydon-Staley will be working out of a new school at Penn, he still has plans to continue collaborating with the Bassett Lab. One ongoing project he has with the lab involves studying how curiosity works in everyday life, and another looks at moment-to-moment patterns of cigarette withdrawal in daily smokers. “Working in the Bassett Lab gave me the confidence and ability to stretch my wings, chase ideas across traditional disciplinary lines, learn new skills, and collaborate with creative and capable scientists every day,” says Lydon-Staley. Those are opportunities he hopes to keep chasing and fostering in his new position.
Beyond continuing his prior research from a communication-based angle, Lydon-Staley is also excited to develop new classes in the Annenberg School. “Annenberg is a very special place. It is an active school, with frequent seminars and many vibrant research centers,” he says. Informed and inspired by the breadth of research from Annenberg scholars, Lydon-Staley hopes that he can create classes that focus on the psychology of time and timing in everyday life—topics that he spends a lot of time thinking about himself.
Above all, Lydon-Staley is excited by the opportunity to stay at Penn and continue the kind of versatile and multi-faceted studies that have been the bedrock of his research so far. He hopes to continue expanding his previous work with not only the Engineering School, but the School of Medicine and the Graduate School of Education as well. “The opportunities for interdisciplinary collaboration at Penn are unrivaled, and I am constantly in awe of the quality of students here.”
Election to the AIMBE College of Fellows is among the highest professional distinctions accorded to a medical and biological engineer. College membership honors those who have made outstanding contributions to “engineering and medicine research, practice, or education” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of medical and biological engineering, or developing/implementing innovative approaches to bioengineering education.”
Bassett was nominated, reviewed, and elected by peers and members of the College of Fellows for “significant contributions to the application of neural network theory for understanding both physio and patho-physiological brain function.”
As a result of health concerns, AIMBE’s annual meeting and induction ceremony scheduled for March 29–30, 2020, was cancelled. Under special procedures, Bassett was remotely inducted along with 156 colleagues who make up the AIMBE College of Fellows Class of 2020.