How do you introduce yourself, scientifically? 

My name is Dana Boebinger, and I’m an auditory cognitive neuroscientist. I study how the brain understands sound; I specifically study humans, and how the brain understands the kinds of sounds that are particularly relevant for humans, like speech and music. 

What are the implications or broader impacts of your work? 

I do basic science, which aims to advance fundamental knowledge about the world. As a cognitive neuroscientist, I want to understand how the brain gives rise to cognition (thinking, perceiving). My research focuses on a part of the brain that seems to respond selectively to music, meaning that it responds more to music than any other type of sound. We are still trying to understand some pretty basic things about that brain region, like what aspect of music it’s responding to (rhythm, melody, etc.), and whether it is the same in everyone or changes based on musical training.

What does your data look like? 

I use functional magnetic resonance imaging (fMRI) to study how people’s brains respond to various sounds. I put people in an MRI scanner and record what their brain is doing while they’re listening to different sounds (Figure 1). The MRI signal shows how much blood flow there is at every location in a person’s brain – the amount of blood flow relates to the amount of neural activity. The data that I get from the MRI scanner are a series of 3D images that show this signal intensity for every voxel (or volume pixel, 3D pixel) in the image. This ends up being a single number at every point in space and at every point in time during the duration of an experiment.

Figure 1: Functional magnetic resonance imaging (fMRI) machine. Source: Work from EconomicsUZH. Licensed under CC BY-SA 4.0.

Can you explain some of the steps that go into collecting your data? What does data collection typically look like for you? 

First, I have to design the experiment. This typically involves coming up with a scientific question and then designing different types of sounds to help us answer that question. Those sounds can sometimes be recordings we’ve made of various sounds, sometimes I have to take recordings and manipulate them in certain ways, and sometimes I even synthesize completely new sounds. Then I write some computer code that enables us to present those sounds at the correct time and sound level while participants are in an MRI scanner. 

To collect data, we first need to recruit volunteers (through word of mouth, various email lists, etc.) We take volunteers into the MRI facility and explain how the experiment works. Then we help them get comfortable inside the MRI scanner – it’s safe! – and run the scanner to record their brain activity while they listen to sounds. 

What happens after you collect your data? How do you get from raw data to the final images that people might see in the news? 

After I collect the data, I retreat to my computer to analyze it. There are a lot of software packages to help with some basic first steps to make the data easier to analyze, such as aligning all of the different images from different time points, or isolating just the part of the image that is the brain, and not the skull or nose or eyes. We then do additional statistical analyses to determine how much every voxel in a person’s brain responds to the various stimuli which we’ve presented, and to ask additional questions about those brain responses.

When you see a news article about fMRI research, you may see a picture of a brain with what looks like colorful ‘blobs’ on it (Figure 2). Those ‘blobs’ show the peaks of brain activity that correspond to a given task/condition in an experiment – for example, which voxels respond the most to music vs. sound that is not music.

Figure 2: Example data generated from fMRI research. Source: Functional MRI in the investigation of blast-related traumatic brain injury. Licensed under CC-PD-Mark.

What makes a neuroscientist different from other scientists? 

Well, neuroscientists study the brain. It’s an extremely multi- and inter-disciplinary field, so neuroscientists can have backgrounds in many different fields. My background is in psychology (where people mainly study behavior), but every day I work with people who have training in math, physics, computer science, philosophy, linguistics, and more. All these people are interested in the brain and how it works. ‘Neuroscientist’ is an umbrella term; some neuroscientists study the biology of the brain (cellular and molecular neuroscience), and others, cognitive neuroscientists like me, study the relationship between the brain and the mind. 

Is there a common misconception about your field? 

A lot of times there are inaccurate interpretations of what neuroscience data are actually showing, and this leads to ‘neuro-myths’. For example, the idea that people are right- or left-brained. Everyone uses both sides of the brain, and it has nothing to do with your personality. Or the idea that we only use 10% of our brains, which is also not correct. Your whole brain is always active. It keeps your heart beating and you breathing and standing upright and seeing and hearing the world around you. This myth may come from the fact that you see these images with brains with small rainbow ‘blobs’ of activity on them, and it makes it look like there is only activity in those areas. But those are just the areas that are most active, not the only areas that are active. 

Is there anything you would tell people interested in becoming a neuroscientist?

If you’re interested in studying the brain, no matter whether you are interested in molecular neuroscience or psychology, learn computer programming and learn as much math as possible (especially linear algebra). I majored in psychology, and when I was an undergraduate my curriculum didn’t involve any computer coding or any math beyond standard statistics. When I got to graduate school, I had a rude awakening when I realized the importance of math and programming skills. I spend pretty much all day every day reading and writing code to put experiments together, make stimuli, and analyze data. 


To learn more about Dana’s work: 

This interview with Dana Boebinger, Graduate Student in the Speech and Hearing Bioscience and Technology Program at Harvard University, was conducted and edited for space and clarity by Malinda J. McPherson in July 2020. 

Malinda J. McPherson is a PhD candidate in the Harvard University/MIT Program in Speech and Hearing Bioscience and Technology.

Cover image: Open science paper entitled Resting state networks and consciousness; licensed under CC-BY-3.0.

This piece is part of our special edition on the day-to-day lives of researchers working in many different fields of science. Are you interested in learning about a different type of scientist? Check out the rest of the special edition!

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