These neurons have food in the brain | MIT News


A slice of sticky pizza. A pile of crispy fries. Ice cream dripping from a cone on a hot summer day. According to a new study by neuroscientists at MIT, when you look at any of these foods, a specialized part of your visual cortex lights up.

This newly discovered population of food-sensing neurons is located in the ventral visual stream, alongside populations that respond specifically to faces, bodies, places, and words. The unexpected finding may reflect the special importance of food in human culture, the researchers say.

“Food is central to human social interactions and cultural practices. It’s not just about sustenance,” says Nancy Kanwisher, Walter A. Rosenblith Professor of Cognitive Neuroscience and fellow at MIT’s McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines. . “Food is central to so many elements of our cultural identity, our religious practices and our social interactions, and many other things humans do.”

The findings, based on an analysis of a large public database of human brain responses to a set of 10,000 images, raise many additional questions about how and why this neuronal population develops. In future studies, researchers hope to explore how people’s responses to certain foods might differ depending on their likes and dislikes, or their familiarity with certain types of food.

MIT post-doctoral fellow Meenakshi Khosla is the lead author of the paper, along with MIT researcher N. Apurva Ratan Murty. The study appears today in the journal Current biology.

Visual categories

More than 20 years ago, while studying ventral visual flow, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that respond selectively to faces. Later, she and other scientists discovered other regions that respond selectively to places, bodies, or words. Most of these areas were discovered when researchers specifically set out to search for them. However, this assumption-based approach can limit what you end up finding, says Kanwisher.

“There might be other things that we might not think to look for,” she says. “And even when we find something, how do we know that it’s actually part of the basic dominant structure of that pathway, and not something we found just because we were looking for it?”

In an attempt to uncover the fundamental structure of ventral visual flow, Kanwisher and Khosla set out to analyze a large, publicly available dataset of whole-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects while they viewed thousands of images.

“We wanted to see when we apply a data-driven and hypothesis-free strategy, what kinds of selectivities appear and if they are consistent with what had been discovered before. ‘hadn’t been hypothesized before, or remained hidden due to the lower spatial resolution of the fMRI data,’ explains Khosla.

To do this, the researchers applied a mathematical method that allows them to discover populations of neurons that cannot be identified from traditional fMRI data. An fMRI image is made up of many voxels – three-dimensional units that represent a cube of brain tissue. Each voxel contains hundreds of thousands of neurons, and if some of these neurons belong to smaller populations that respond to one type of visual input, their responses may be drowned out by other populations within the same voxel.

The new analytical method, which Kanwisher’s lab has previously used on fMRI data from the auditory cortex, can unravel the responses of neuronal populations in each voxel of the fMRI data.

Using this approach, the researchers found four populations that matched previously identified groups that respond to faces, places, bodies, and words. “It tells us that this method works, and it tells us that the things we’ve found before aren’t just obscure properties of this pathway, but major, dominant properties,” Kanwisher says.

Curiously, a fifth population also emerged, and this one appeared to be selective for food images.

“We were initially quite intrigued by this because food is not a visually homogeneous category,” Khosla explains. “Things like apples, corn, and pasta look so similar to each other, but we found one population that reacted the same way to all of these diverse foods.”

The food-specific population, which the researchers call the ventral food component (VFC), appears to be distributed across two groups of neurons, located on either side of the FFA. The fact that food-specific populations are split among other category-specific populations may help explain why they haven’t been seen before, the researchers say.

“We believe that food selectivity was more difficult to characterize previously because food-selective populations are mixed with other nearby populations that have distinct responses to other stimulus attributes. The low spatial resolution of fMRI tells us prevents seeing this selectivity because the responses of different neuronal populations are mixed in a voxel,” says Khosla.

“The technique the researchers used to identify category-responsive cells or areas is impressive, and it recovered known category-responsive systems, making the food category results the most impressive,” says Paul Rozin. , a psychology professor at the University of Pennsylvania, who was not involved in the study. “I can’t imagine a way for the brain to reliably identify food diversity based on sensory characteristics. That makes it all the more fascinating and likely to teach us something really new.

Food vs non-food

The researchers also used the data to train a computer model of HRV, based on previous models Murty had developed for face and place recognition areas of the brain. This allowed researchers to conduct additional experiments and predict HRV responses. In one experiment, they fed the model matching images of food and non-food items that looked very similar – for example, a banana and a yellow crescent moon.

“These paired stimuli have very similar visual properties, but the main attribute by which they differ is edible versus inedible,” Khosla explains. “We could feed these arbitrary stimuli through the predictive model and see if it would respond even more to food than non-food, without having to collect the fMRI data.”

They could also use the computer model to analyze much larger datasets consisting of millions of images. These simulations confirmed that the VFC is very selective for food images.

From their analysis of human fMRI data, the researchers found that in some subjects, HCV responded slightly more to processed foods such as pizza than to unprocessed foods such as apples. In the future, they hope to explore how factors such as familiarity and a love or dislike of a particular food might affect individuals’ responses to that food.

They also hope to study when and how this region specializes in early childhood, and with which other parts of the brain it communicates. Another question is whether this selective food population will be seen in other animals such as monkeys, which do not attach the cultural significance to food that humans do.

The research was funded by the National Institutes of Health, the National Eye Institute, and the National Science Foundation through the MIT Center for Brains, Minds, and Machines.


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