For the first time, neuroscientists at the Massachusetts Institute of Technology have identified a population of neurons in a person’s brain that light up when we hear singing but not other types of music.
These neurons, which are located in the auditory cortex, appear to respond to a specific combination of voice and music, but not to ordinary speech or instrumental music. Researchers say exactly what they are doing is unknown, and more work will be needed to find out.
“The work provides evidence of a subtle segregation of functions in the auditory cortex in a way that coincides with an intuitive difference in music,” says Sam Norman-Heiner, a former Massachusetts Institute of Technology graduate who is now an associate professor of neurology at Rochester University Medical Center.
The work is based on a 2015 study in which the same research team used functional magnetic resonance imaging (fMRI) to identify a population of neurons in the auditory cortex of the brain that responds to music. In the new work, the researchers used records of electrical activity made on the surface of the brain, which gave them much more accurate information than fMRI.
“There’s one population of neurons that responds to singing, and then there’s another population of neurons that responds extensively to a lot of music. On the scale of fMRI, they are so close that it is impossible to disassemble them, but with intracranial records we get additional separation, and this is what we believe has allowed us to separate them, ”says Norman-Einer.
Norman-Einer is the lead author of a study that appears in the journal today Modern biology. Josh McDermott, an associate professor in the Department of Brain and Cognitive Sciences, and Nancy Canwicher, a professor of cognitive neuroscience at Walter A. Rosenblitt, both members of the McGovern Brain Research Institute and the Center for Brain, Mind and Machinery (CBMM) MIT.
In their 2015 study, researchers used fMRI to scan participants ’brains when they listened to a collection of 165 sounds, including different types of speech and music, as well as everyday sounds such as finger tapping or dog barking. For this study, the researchers developed a new method for analyzing fMRI data that allowed them to identify six neuronal populations with different response patterns, including a music-selective population and another population that selectively responded to speech.
In a new study, researchers hoped to obtain higher-resolution data using a technique known as electrocorticography (ECoG), which allows you to record electrical activity using electrodes located inside the skull. This gives a much more accurate picture of electrical activity in the brain compared to fMRI, which measures blood flow to the brain as an operator of neuronal activity.
“With most techniques in human cognitive neurology, you can’t see neural representations,” Convisher says. “Most of the data we can gather can tell us that there’s a part of the brain here that does something, but it’s pretty limited. We want to know what is represented there. “
Electrocorticography is usually not performed in humans because it is an invasive procedure, but it is often used to monitor patients with epilepsy who are about to undergo surgery to treat seizures. Patients are monitored for several days so doctors can determine where their seizures are coming from before surgery. During this time, if patients agree, they can participate in studies that include measuring their brain activity while performing certain tasks. For this study, the MIT team was able to collect data from 15 participants over several years.
For these participants, the researchers reproduced the same set of 165 sounds they had used in the previous fMRI study. The location of each patient’s electrodes was determined by their surgeons, so some did not pick up any response to auditory input, but many did. Using a new statistical analysis they developed, the researchers were able to draw conclusions about the types of neuronal populations that created the data recorded by each electrode.
“When we applied this method to this data set, we found this neural pattern of response that only responded to singing,” Norman-Heiner says. “It was a conclusion we didn’t really expect, so it really justifies the whole point of the approach, which is to discover potentially new things that you may not be thinking of looking for.”
This song-special population of neurons had very weak responses to speech or instrumental music, and is thus different from the populations with music and speech choices found in their 2015 study.
Music in the brain
In the second part of their study, the scientists developed a mathematical method to combine the data of intracranial records with fMRI data from their 2015 study. Because fMRI can cover a much larger part of the brain, this allowed them to more accurately determine the location of neuronal populations that respond to singing.
“This way of combining ECG and fMRI is a significant methodological progress,” McDermott says. “Over the last 10 or 15 years, many people have done ECGs, but this has always been limited to the problem of rare recordings. Sam is really the first person to figure out how to combine the improved separation of electrode records with fMRI data to get a better localization of overall reactions. ”
The song-specific access point they found is located at the top of the temporal lobe, next to regions that are selective for language and music. Such a location suggests that the population associated with the songs may respond to functions such as perceived height or the interaction between words and perceived height before sending information to other parts of the brain for further processing, the researchers say.
Now researchers are hoping to learn more about what aspects of singing are driven by the reactions of these neurons. They are also working with Massachusetts Institute of Technology professor Rebecca Sachs ’lab to study whether infants have musical selective areas, hoping to learn more about when and how these areas of the brain develop.
The study was funded by the National Institutes of Health, the U.S. Army Research Office, the National Science Foundation, the NSF Science and Technology Center for Brains, Minds and Machines, the Fondazione Neurone and the Howard Hughes Medical Institute.