Neir Eshel Wins 2016 Science & SciLifeLab Prize for Young Scientists

Using optogenetics in mice, Eshel explored how dopamine neurons compare predictions to reality. | Jeremiah Cohen

Neir Eshel, a neuropsychiatry researcher, has been honored for his work uncovering new insights about how dopamine neurons are wired to help humans navigate the consequences of their choices and was named the 2016 Grand Prize winner of the Science & SciLifeLab Prize for Young Scientists.

The prize recognizes promising early-career scientists who conduct groundbreaking life-science research and is supported by Science for Life Laboratory, a coordinated effort among four universities in Sweden and the journal Science.

Eshel and colleagues at Harvard University expanded on the basic finding that humans learn by comparing predictions to reality, then update those predictions accordingly, using a simple mechanism evolved by the brain. Dopamine neurons are key players in this process, with a unique ability to calculate prediction error, or the difference between actual and predicted reward.

Eshel, now a psychiatry resident at Stanford University, is excited about the possible implications for his patients, particularly those affected by addiction, a disease he sees every day in the emergency rooms and hospital wards. "Addiction has been conceptualized as a disorder in which the dopamine prediction error system is hijacked, so that drugs of abuse always appear better than expected," said Eshel. "Now that we know more about how dopamine neurons calculate prediction error, we can better target our therapies."

The Science & SciLifeLab Prize for Young Scientists is an annual prize aimed at rewarding young scientists at an early stage of their careers, and includes a grand-prize award of $30,000. The categories for this annual award are cell and molecular biology, ecology and environment, genomics and proteomics, and translational medicine.

Despite extensive studies exploring the effect of prediction error on behavior, little is known about how dopamine neurons actually calculate this error. Interested in how nerve cells processed predictions related to decision making, Eshel combined optogenetics (a technique that harnesses light to control cells like neurons) with classical neuron recording techniques, carefully-designed behavioral tasks and computational analysis to investigate further.

"Many researchers have used optogenetics alone to explore how behavior changes when a subset of neurons is activated or inhibited. While these experiments are important, the results can be hard to interpret, because it is unclear if the manipulated neurons ever respond this way during natural behaviors," said Eshel. "To truly understand how circuits function during behavior, a combination of methods is needed."

He zeroed in on the small portion of the brain in mice called the ventral tegmental area (VTA), where dopamine is produced. This same region of the brain also contains inhibitory GABA neurons, which signal how much reward to expect from an event. Intrigued by the possibility that dopamine neurons could use the GABA expectation signal to calculate prediction error, his team selectively infused VTA GABA neurons with a light-sensitive protein to control their activity. The scientists recorded GABA and dopamine neuron activity as the animals performed easy learning tasks.

Following VTA GABA neuron stimulation, the mice's dopamine neurons responded to unanticipated rewards as if they were anticipated. Conversely, when the VTA GABA neurons were inhibited, dopamine neurons responded to expected rewards as if they were unexpected. "If we activated the VTA GABA neurons simultaneously on both sides of the brain, we even changed the animals' behavior," Eshel noted. After training mice to await a certain size of reward, the researchers artificially increased the expectation level by stimulating VTA GABA neurons during the anticipation period, maintaining the same reward amount. Following several trials where expectation exceeded reality, the disappointed rodents stopped anticipating a reward.

Eshel's work revealed that individual dopamine neurons responded in a remarkably similar fashion, and were essentially indistinguishable from one another. "This uniformity greatly simplifies information coding, allowing prediction errors to be broadcasted robustly and coherently throughout the brain — ensuring that the rest of the brain can decipher the message loud and clear," said Eshel.

Neir Eshel | Geoff Chesman

Importantly, Eshel also found that dopamine neurons take a simple mathematical approach to prediction errors — by subtracting expectation from actual reward. "Subtraction like this is rare to find in the brain, but it's ideally suited for this purpose, allowing for consistent and precise calculations," said Eshel.

In Eshel's grand-prize winning essay, "Trial and error" which will appear in the 2 December issue of Science, he highlights how his team's research methods allowed them to pinpoint the origin of prediction signals. While these GABA neurons were previously difficult to analyze, they may now be studied in greater detail, using Eshel's approach.

"As always, we are delighted by the high level of accomplishment that we see in all the Science & SciLifeLab Prize applicants. This year stands out for the ability shown by the winners to apply current technologies in new ways, to answer fundamental questions and provide potentially life-saving insights," said Barbara Jasny, deputy editor of Science.

"SciLifeLab is very happy to note that there were more applications for the prize in 2016 compared to previous years and also that the geographical spread was bigger," said Lena Claesson-Welsh, co-director of SciLifeLab. "The prize is clearly becoming established as a very prestigious award for young scientists. We look forward very much to hosting the winners of 2016 and welcome them to Sweden to receive the awards for their great achievements."

The 2016 runners-up are:

David Seekell: For his essay on the topic of ecology and environment, " Passing the point of no return." Seekell is an environmental scientist based in Sweden. He holds a Bachelor of Science in Natural Resources from the University of Vermont, and a Ph.D. in Environmental Sciences from the University of Virginia. In his Ph.D. research, Seekell developed statistics to provide early warning that an ecosystem is passing a tipping point, and is about to undergo a transformation. He is currently an Assistant Professor of Ecology in the Department of Ecology and Environmental Science at Umeå University. In 2015 he became a Wallenberg Academy Fellow, and in 2016, he received a Science for Solutions Award from the American Geophysical Union.

Sam Behjati: For his essay on the topic of genomics and proteomics, " Retracing embryonic fate." Originally from Germany, Behjati studied medicine at Oxford University and pursued post-graduate clinical training in London, UK. Funded by the Wellcome Trust, he joined the Cancer Genome Project of the Sanger Institute (UK) for doctoral research. Using a "mutational postcode" that cells acquire as they divide, Behjati has shown that it is feasible to reconstruct early developmental processes in the adult mouse. Building on ideas developed during his Ph.D., he now aims to define the embryonic origin and fate of childhood cancer cells.

Canan Dagdeviren: For her essay on the topic of translational medicine, " The future of bionic dynamos." Dagdeviren was born in 1985 in Istanbul, Turkey. As a Fulbright Doctoral Fellow, she received her Ph.D. degree in Material Science and Engineering at the University of Illinois at Urbana-Champaign. Dagdeviren developed an energy harvester that converts mechanical energy from internal organ movements into electric energy to power medical devices. It is soft and flexible, and conforms to the heart as well as other soft tissues. This technology could extend the battery life of implanted electronics or eliminate the need of battery replacement, sparing patients from repeated operations and the risk of surgical complications. Beginning in January 2017, she will assume the role of Assistant Professor at the MIT Media Lab, where she will direct the Conformable Decoders research group.

SciLifeLab, Science for Life Laboratory, is a Swedish national center for molecular biosciences, with the mission to develop, use and provide advanced technologies for applications in health and environmental research. The center was established in 2010 and became a national resource in 2013, making technologies and expertise available to researchers in all of Sweden and beyond.