Jenny Jiang Wins CZI Grant to Investigate the Potential Trigger for Neurodegenerative Diseases

Jenny Jiang, Ph.D.

TDP-43 may be one of the most dangerous proteins in the human body, implicated in neurodegenerative conditions like ALS and Alzheimer’s disease. But the protein remains mysterious: how TDP-43 interacts with the immune system, for instance, is still unclear. 

Now, Ning Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, has been selected for the Collaborative Pairs Pilot Project Awards, sponsored by the Chan Zuckerberg Initiative (CZI), to investigate the relationship between TDP-43 and the immune system. 

Launched in 2018, the Collaborative Pairs Pilot Project Awards support pairs of investigators to explore “innovative, interdisciplinary approaches to address critical challenges in the fields of neurodegenerative disease and fundamental neuroscience.” Professor Jiang will partner with Pietro Fratta, MRC Senior Clinical Fellow and MNDA Lady Edith Wolfson Fellow at the University College London Queen Square Institute of Neurology.

The TDP-43 protein is associated with neurodegenerative diseases affecting the central nervous system, including ALS and Alzehimer’s disease. While the loss of neurons and muscle degeneration cause the progressive symptoms, the diseases themselves may be a previously unidentified trigger for abnormal immune system activity. 

One possible link is the intracellular mislocalization of TDP-43 (known as TDP-43 proteinopathy), when the protein winds up in the wrong location, which the Jiang and Fratta Labs will investigate. Successfully proving this link could result in potentially game-changing new therapies for these neurodegenerative diseases. 

The Jiang Lab at Penn Engineering specializes in systems immunology, using high-throughput sequencing and single-cell and quantitative analysis to understand how the immune system develops and ages, as well as the molecular signatures of immune related diseases. Jiang joined Penn Bioengineering in 2021. 

Since arriving on campus, Jiang has teamed with the recently formed Penn Anti-Cancer Engineering Center (PACE), which seeks to understand the forces that determine how cancer grows and spreads, and Engineers in the Center for Precision Engineering (CPE4H), which focuses on innovations in diagnostics and delivery in the development of customizable biomaterials and implantable devices for individualized care. 

Jiang was elected a member of the American Institute for Medical and Biological Engineering (AIMBE) College of Fellows in 2021, and has previously won multiple prestigious awards including the NSF CAREER, a Cancer Research Institute Lloyd J. Old STAR Award, and a CZI Neurodegeneration Challenge Network Ben Barres Early Career Acceleration Award.

Jiang is a leader in high-throughput and high-dimensional analysis of T cells, a type of white blood cell crucial to the functioning of a healthy immune system. A recent study in Nature Immunology described the Jiang Lab’s TetTCR-SeqHD technology, the first approach to provide a multifaceted analysis of antigen-specific T cells in a high-throughput manner.

The CZI Collaborative Pairs Pilot Project Awards will provide $200,000 of funding over 18 months with a chance to advance to the second phase of $3.2 million in funding over a four-year period. 

Read the full list of grantees on the CZI’s Neurodegeneration Challenge Network (NDCN) Projects website here.

Penn Scientists Reflect on One Year of ChatGPT

by Erica Moser

René Vidal, at the podium, introduces the event “ChatGPT turns one: How is generative AI reshaping science?” Bhuvnesh Jain, left at the table, moderated the discussion with Sudeep Bhatia, Konrad Kording, Andrew Zahrt, and Nick Pangakis.

As a neuroscientist surveying the landscape of generative AI—artificial intelligence capable of generating text, images, or other media—Konrad Kording cites two potential directions forward: One is the “weird future” of political use and manipulation, and the other is the “power tool direction,” where people use ChatGPT to get information as they would use a drill to build furniture.

“I’m not sure which of those two directions we’re going but I think a lot of the AI people are working to move us into the power tool direction,” says Kording, a Penn Integrates Knowledge (PIK) University professor with appointments in the Perelman School of Medicine and School of Engineering and Applied Science. Reflecting on how generative AI is shifting the paradigm of science as a discipline, Kording said he thinks “it will push science as a whole into a much more collaborative direction,” though he has concerns about ChatGPT’s blind spots.

Kording joined three University of Pennsylvania researchers from the chemistry, political science, and psychology departments sharing their perspectives in the recent panel “ChatGPT turns one: How is generative AI reshaping science?” PIK Professor René Vidal opened the event, which was hosted by the School of Arts & Sciences’ Data Driven Discovery Initiative (DDDI), and Bhuvnesh Jain, physics and astronomy professor and co-faculty director of DDDI, moderated the discussion.

“Generative AI is moving so rapidly that even if it’s a snapshot, it will be very interesting for all of us to get that snapshot from these wonderful experts,” Jain said. OpenAI launched ChatGPT, a large language model (LLM)-based chatbot, on Nov. 30, 2022, and it rapidly ascended to ubiquity in news reports, faculty discussions, and research papers. Colin Twomey, interim executive director of DDDI, told Penn Today that it’s an open question as to how it will change the landscape of scientific research, and the` idea of the event was to solicit colleagues’ opinions on interesting directions in their fields.

Read the full story in Penn Today.

Konrad Paul Kording is Nathan Francis Mossell University Professor in Bioengineering and Computer and Information Science in Penn Engineering and in Neuroscience in the Perelman School of Medicine.

How the Hippocampus Distinguishes True and False Memories

by Erica Moser

Image: iStock/metamorworks

Let’s say you typically eat eggs for breakfast but were running late and ate cereal. As you crunched on a spoonful of Raisin Bran, other contextual similarities remained: You ate at the same table, at the same time, preparing to go to the same job. When someone asks later what you had for breakfast, you incorrectly remember eating eggs.

This would be a real-world example of a false memory. But what happens in your brain before recalling eggs, compared to what would happen if you correctly recalled cereal?

In a paper published in Proceedings of the National Academy of Sciences, University of Pennsylvania neuroscientists show for the first time that electrical signals in the human hippocampus differ immediately before recollection of true and false memories. They also found that low-frequency activity in the hippocampus decreases as a function of contextual similarity between a falsely recalled word and the target word.

“Whereas prior studies established the role of the hippocampus in event memory, we did not know that electrical signals generated in this region would distinguish the imminent recall of true from false memories,” says psychology professor Michael Jacob Kahana, director of the Computational Memory Lab and the study’s senior author. He says this shows that the hippocampus stores information about an item with the context in which it was presented.

Researchers also found that, relative to correct recalls, the brain exhibited lower theta and high-frequency oscillations and higher alpha/beta oscillations ahead of false memories. The findings came from recording neural activity in epilepsy patients who were already undergoing invasive monitoring to pinpoint the source of their seizures.

Noa Herz, lead author and a postdoctoral fellow in Kahana’s lab at the time of the research, explains that the monitoring was done through intracranial electrodes, the methodology researchers wanted to use for this study. She says that, compared to scalp electrodes, this method “allowed us to more precisely, and directly, measure the neural signals that were generated in deep brain structures, so the activity we are getting is much more localized.”

Read the full story in Penn Today.

Michael Kahana is the Edmund J. and Louise W. Kahn Term Professor of Psychology in the School of Arts & Sciences and director of the Computational Memory Lab at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.

Could Psychedelics Simultaneously Treat Chronic Pain and Depression?

Ahmad Hammo

Ongoing clinical trials have demonstrated that psychedelics like psilocybin and LSD can have rapid and long-lived antidepressant and anti-anxiety effects. A related clinical problem is chronic pain, which is notoriously difficult to treat and often associated with depression and anxiety.

This summer, Ahmad Hammo, a rising third-year student in bioengineering in the School of Engineering and Applied Science, is conducting a pilot study to explore psilocybin’s potential as a therapy for chronic pain and the depression that often accompanies it.

“There’s a strong correlation between chronic pain and depression, so I’m looking at how a psychedelic might be used for treating both of these things simultaneously,” says Hammo, who is originally from Amman, Jordan.

Hammo is working under the guidance of neuroanesthesiologist and neuroscientist Joseph Cichon, an assistant professor in the Perelman School of Medicine. The effort is supported by the Penn Undergraduate Research Mentoring (PURM) program, administered by the Center for Undergraduate Research and Fellowships, which awards undergraduate students $5,000 to spend 10 weeks conducting research alongside Penn faculty.

Hammo’s project focuses on neuropathic pain, pain associated with nerve damage. Like other forms of chronic pain, most experts believe that chronic neuropathic pain is stored in the brain.

“Neuropathic pain can lead to a centralized pain syndrome where the pain is still being processed in the brain,” Cichon says. “It’s as if there’s a loop that keeps playing over and over again, and this chronic form is completely divorced from that initial injury.”

Read the full story in Penn Today.

AI-guided Brain Stimulation Aids Memory in Traumatic Brain Injury

by Erica Moser

Illustration of a human brain
Image: iStock/Ogzu Arslan

Traumatic brain injury (TBI) has disabled 1 to 2% of the population, and one of their most common disabilities is problems with short-term memory. Electrical stimulation has emerged as a viable tool to improve brain function in people with other neurological disorders.

Now, a new study in the journal Brain Stimulation shows that targeted electrical stimulation in patients with traumatic brain injury led to an average 19% boost in recalling words.

Led by University of Pennsylvania psychology professor Michael Jacob Kahana, a team of neuroscientists studied TBI patients with implanted electrodes, analyzed neural data as patients studied words, and used a machine learning algorithm to predict momentary memory lapses. Other lead authors included Wesleyan University psychology professor Youssef Ezzyat and Penn research scientist Paul Wanda.

“The last decade has seen tremendous advances in the use of brain stimulation as a therapy for several neurological and psychiatric disorders including epilepsy, Parkinson’s disease, and depression,” Kahana says. “Memory loss, however, represents a huge burden on society. We lack effective therapies for the 27 million Americans suffering.”

Read the full story in Penn Today.

Michael Kahana is the Edmund J. and Louise W. Kahn Term Professor of Psychology at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.

Why is Machine Learning Trending in Medical Research but not in Our Doctor’s Offices?

by Melissa Pappas

Illustration of a robot in a white room with medical equipment.Machine learning (ML) programs computers to learn the way we do – through the continual assessment of data and identification of patterns based on past outcomes. ML can quickly pick out trends in big datasets, operate with little to no human interaction and improve its predictions over time. Due to these abilities, it is rapidly finding its way into medical research.

People with breast cancer may soon be diagnosed through ML faster than through a biopsy. Those suffering from depression might be able to predict mood changes through smart phone recordings of daily activities such as the time they wake up and amount of time they spend exercising. ML may also help paralyzed people regain autonomy using prosthetics controlled by patterns identified in brain scan data. ML research promises these and many other possibilities to help people lead healthier lives.

But while the number of ML studies grow, the actual use of it in doctors’ offices has not expanded much past simple functions such as converting voice to text for notetaking.

The limitations lie in medical research’s small sample sizes and unique datasets. This small data makes it hard for machines to identify meaningful patterns. The more data, the more accuracy in ML diagnoses and predictions. For many diagnostic uses, massive numbers of subjects in the thousands would be needed, but most studies use smaller numbers in the dozens of subjects.

But there are ways to find significant results from small datasets if you know how to manipulate the numbers. Running statistical tests over and over again with different subsets of your data can indicate significance in a dataset that in reality may be just random outliers.

This tactic, known as P-hacking or feature hacking in ML, leads to the creation of predictive models that are too limited to be useful in the real world. What looks good on paper doesn’t translate to a doctor’s ability to diagnose or treat us.

These statistical mistakes, oftentimes done unknowingly, can lead to dangerous conclusions.

To help scientists avoid these mistakes and push ML applications forward, Konrad Kording, Nathan Francis Mossell University Professor with appointments in the Departments of Bioengineering and Computer and Information Science in Penn Engineering and the Department of Neuroscience at Penn’s Perelman School of Medicine, is leading an aspect of a large, NIH-funded program known as CENTER – Creating an Educational Nexus for Training in Experimental Rigor. Kording will lead Penn’s cohort by creating the Community for Rigor which will provide open-access resources on conducting sound science. Members of this inclusive scientific community will be able to engage with ML simulations and discussion-based courses.

“The reason for the lack of ML in real-world scenarios is due to statistical misuse rather than the limitations of the tool itself,” says Kording. “If a study publishes a claim that seems too good to be true, it usually is, and many times we can track that back to their use of statistics.”

Such studies that make their way into peer-reviewed journals contribute to misinformation and mistrust in science and are more common than one might expect.

Read the full story in Penn Engineering Today.

The Potential Futures of Neurotech

Roy Hoshi Hamilton, MD, MS, FAAN, FANA

Brain technology offers all kinds of exciting possibilities — from treating conditions like epilepsy or depression, to simply maximizing brain health. But medical ethicists are concerned about potential dangers and privacy concerns. Roy Hamilton, Professor of Neurology in the Perelman School of Medicine,  Director of the Penn Brain Science, Translation, Innovation, and Modulation (BrainSTIM) Center, and member of the Penn Bioengineering Graduate Group, spoke with WHYY about how brain stimulation is being used.

Listen to “Neurotech and the Growing Battle for Our Brains

Study Reveals New Insights on Brain Development Sequence Through Adolescence

by Eric Horvath

3D illustration of a human brain
Image: Courtesy of Penn Medicine News

Brain development does not occur uniformly across the brain, but follows a newly identified developmental sequence, according to a new Penn Medicine study. Brain regions that support cognitive, social, and emotional functions appear to remain malleable—or capable of changing, adapting, and remodeling—longer than other brain regions, rendering youth sensitive to socioeconomic environments through adolescence. The findings are published in Nature Neuroscience.

Researchers charted how developmental processes unfold across the human brain from the ages of 8 to 23 years old through magnetic resonance imaging (MRI). The findings indicate a new approach to understanding the order in which individual brain regions show reductions in plasticity during development.

Brain plasticity refers to the capacity for neural circuits—connections and pathways in the brain for thought, emotion, and movement—to change or reorganize in response to internal biological signals or the external environment. While it is generally understood that children have higher brain plasticity than adults, this study provides new insights into where and when reductions in plasticity occur in the brain throughout childhood and adolescence.

The findings reveal that reductions in brain plasticity occur earliest in “sensory-motor” regions, such as visual and auditory regions, and occur later in “associative” regions, such as those involved in higher-order thinking (problem solving and social learning). As a result, brain regions that support executive, social, and emotional functions appear to be particularly malleable and responsive to the environment during early adolescence, as plasticity occurs later in development.

“Studying brain development in the living human brain is challenging. A lot of neuroscientists’ understanding about brain plasticity during development actually comes from studies conducted with rodents. But rodent brains do not have many of what we refer to as the association regions of the human brain, so we know less about how these important areas develop,” says corresponding author Theodore D. Satterthwaite, the McLure Associate Professor of Psychiatry in the Perelman School of Medicine, and director of the Penn Lifespan Informatics and Neuroimaging Center (PennLINC).

Read the full story in Penn Medicine News.

N.B.: Theodore Satterthwaite in a member of the Penn Bioengineering Graduate Group.

Novel Tools for the Treatment and Diagnosis of Epilepsy

by Nathi Magubane

A neurologist examines an encephalogram of a patient’s brain.
Throughout his career, Brian Litt has fabricated tools that support international collaboration, produced findings that have led to significant breakthroughs, and mentored the next generation of researchers tackling neurological disorders. (Image: iStock Photo/Alona Siniehina)

When Brian Litt of the Perelman School of Medicine and School of Engineering and Applied Science began treating patients as a neurologist, he found that the therapies and treatments for epilepsy were mostly reliant on traditional pharmacological interventions, which had limited success in changing the course of the disease.

People with epilepsy are often prescribed anti-seizure medications, and, while they are effective for many, about 30% of patients still continue to experience seizures. Litt sought new ways to offer patients better treatment options by investigating a class of devices that electronically stimulate cells in the brain to modulate activity known as neurostimulation devices.

Litt’s research on implantable neurostimulation devices has led to significant breakthroughs in the technology and has broadened scientists’ understanding of the brain. This work started not long after he came to Penn in 2002 with licensing algorithms to help drive a groundbreaking device by NeuroPace, the first closed-loop, responsive neurostimulator to treat epilepsy.

Building on this work, Litt noted in 2011 how the implantable neurostimulation devices being used at the time had rigid wires that didn’t conform to the brain’s surface, and he received support from CURE Epilepsy to accelerate the development of newer, flexible wires to monitor and stimulate the brain.

“CURE is one of the epilepsy community’s most influential funding organizations,” Litt says. “Their support for my lab has been incredibly helpful in enabling the cutting-edge research that we hope will change epilepsy care for our patients.”

Read the full story in Penn Today.

Brian Litt is a Professor in Bioengineering and Neurology.

Flavia Vitale is an Assistant Professor in Neurology with a secondary appointment in Bioengineering.

Jonathan Viventi is an Assistant Professor in Biomedical Engineering at Duke University.

Through the Lens: A Digital Depiction of Dyslexia

by Nathi Magubane

Artist-in-residence and visiting scholar Rebecca Kamen has blended AI and art to produce animated illustrations representing how a dyslexic brain interprets information.

A collage of artwork depicts a series of abstract visualizations of networks.
A work that Penn artist-in-residence Rebecca Kamen produced for the show, “Dyslexic Dictionary” at Arion Press in San Francisco. Here, she reinterprets Ph.D. candidate Dale Zhou’s network visualization. (Image: Cat Fennell)

Communicating thoughts with words is considered a uniquely human evolutionary adaptation known as language processing. Fundamentally, it is an information exchange, a lot like data transfer between devices, but one riddled with discrete layers of complexity, as the ways in which our brains interpret and express ideas differ from person to person.

Learning challenges such as dyslexia are underpinned by these differences in language processing and can be characterized by difficulty learning and decoding information from written text.

Artist-in-residence in Penn’s Department of Physics and Astronomy Rebecca Kamen has explored her personal relationship with dyslexia and information exchange to produce works that reflect elements of both her creative process and understanding of language. Kamen unveiled her latest exhibit at Arion Press Gallery in San Francisco, where nine artists with dyslexia were invited to produce imaginative interpretations of learning and experiencing language.

The artists were presented with several prompts in varying formats, including books, words, poems, quotes, articles, and even a single letter, and tasked with creating a dyslexic dictionary: an exploration of the ways in which their dyslexia empowered them to engage in information exchange in unique ways.

Undiagnosed dyslexia

“[For the exhibit], each artist selected a word representing the way they learn, and mine was ‘lens,’” explains Kamen. “It’s a word that captures how being dyslexic provides me with a unique perspective for viewing and interacting with the world.”

From an early age, Kamen enjoyed learning about the natural sciences and was excited about the process of discovery. She struggled, however, with reading at school, which initially presented an obstacle to achieving her dreams of becoming a teacher. “I had a difficult time getting into college,” says Kamen. “When I graduated high school, the word ‘dyslexia’ didn’t really exist, so I assumed everyone struggled with reading.”

Kamen was diagnosed with dyslexia well into her tenure as a professor. “Most dyslexic people face challenges that may go unnoticed by others,” she says, “but they usually find creative ways to overcome them.”

This perspective on seeing and experiencing the world through the lens of dyslexia not only informed Kamen’s latest work for the exhibition “Dyslexic Dictionary,” but also showcased her background in merging art and science. For decades, Kamen’s work has investigated the intersection of the two, creating distinct ways of exploring new relationships and similarities.

“Artists and scientists are curious creatures always looking for patterns,” explains Kamen. “And that’s because patterns communicate larger insights about the world around us.”

Creativity and curiosity

This idea of curiosity and the patterns its neural representations could generate motivated “Reveal: The Art of Reimagining Scientific Discovery,” Kamen’s previous exhibit, which was inspired by the work of Penn professor Dani Bassett, assistant professor David Lydon-Staley and American University associate professor Perry Zurn on the psychological and historical-philosophical basis of curiosity.

The researchers studied different information-seeking approaches by monitoring how participants explore Wikipedia pages and categorically related these to two ideas rooted in philosophical understandings of learning: a “busybody,” who typically jumps between diverse ideas and collects loosely connected information; and a more purpose-driven “hunter,” who systematically ties in closely related concepts to fill their knowledge gaps.

They used these classifications to inform their computational model, the knowledge network. This uses text and context to determine the degree of relatedness between the Wikipedia pages and their content—represented by dots connected with lines of varying thickness to illustrate the strength of association.

In an adaption of the knowledge network, Kamen was classified as a dancer, an archetype elaborated on in an accompanying review paper by Dale Zhou, a Ph.D. candidate in Bassett’s Complex Systems Lab, who had also collaborated with Kamen on “Reveal.”

“The dancer can be described as an individual that breaks away from the traditional pathways of investigation,” says Zhou. “Someone who takes leaps of creative imagination and in the process, produces new concepts and radically remodels knowledge networks.”

Read the full story in Penn Today.

Rebecca Kamen is a visiting scholar and artist-in-residence in the Department of Physics & Astronomy in Penn’s School of Arts & Sciences.

Dale Zhou is a Ph.D. candidate in Penn’s Neuroscience Graduate Group.

Dani Smith Bassett is J. Peter Skirkanich Professor in Bioengineering with secondary appointments in the Departments of Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry.

David Lydon-Staley is an Assistant Professor in the Annenberg School for Communications and Bioengineering and is an alumnus of the Bassett Lab.