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Using Computational Models to Solve Problems in Neurology

Pat Lawlor, a third-year Medical Scientist Training program student, uses computational and statistical tools to answer questions about how the brain works.  

This fall, Pat Lawlor, a third-year Medical Scientist Training Program (MSTP) student, transitioned from the classroom to the lab on his path toward earning both an MD and a PhD. Having completed two years of medical school, over the next three to four years he will conduct research with Konrad Kording, PhD, associate professor in physical medicine and rehabilitation, before returning to his last two years of clinical instruction.

As an undergraduate physics major, Lawlor knew he loved science and research, but he still wanted a career in medicine.

“I think medicine informs your research questions,” he said. “It keeps you in touch with the important questions in the field, what kinds of problems are solvable, what questions will actually make a difference, and what solutions will actually be implemented. The science keeps you focused on why things happen in medicine and keeps you from falling into thinking that this is the way things are and forgetting that change can happen.”

Lawlor has noted a distinction in how students learn in each part of the MSTP program, adding that the two styles complement each other.

“In medical school you have to be at class at 8 a.m., the faculty members teach the information you need to know for the test, and you have very little choice in what you do, except how you study,” he said. “In graduate school, they say, ‘This is the question we are interested in, go do it.’ So you really go from being completely constrained to being unconstrained.”

Lawlor’s research is in the field of neuroscience. He uses computational and statistical tools to solve problems. His main research project seeks to answer questions about how the brain chooses where to look and how this is represented in the brain. Three main factors are thought to influence where we look: a) salience – how different a visual target is from its surroundings, b) relevance – how a visual target relates to the goal at hand, and c) gist – whether the context of a visual search makes sense.

“I really love the science we’re doing. I spend time in the lab recording neural activity from animal models while they do certain tasks, and then spend a lot of time analyzing and modeling that data” Lawlor said. “By analyzing our data, we can study what individual neurons do during a visual search and ultimately learn how information is processed in the visual system.”

While doing research takes up most of his time, Lawlor has found that the structure of the day of a graduate student has allowed him to spend more time in extracurricular activities. He has stepped into the role as the MSTP student body president this year and mentors high school students through the Promoting Intercity Youth in Science and Medicine (PRISM) program.

While he thinks the transition into the last two years of medical school will be difficult, he stays active in the medical community by shadowing clinicians.

“I also feel morally obligated to help people in the short term, and sometimes science is a little bit farther away from that,” he explained. “I feel people who have the means to help others have the obligation to do so. By pursuing medicine I feel like I fulfill that hope that I have for myself in a way that I couldn’t if I just did science.”

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