summary: Images of food stimulate a newly discovered population of food-responsive neurons in the ventral visual stream. Researchers believe that this neural population may have an evolutionary reason that may reflect the importance of food in human culture.
Source: MIT
A sticky slice of pizza. A stack of crispy french fries. Ice cream dripping down a cone on a hot summer day. According to a new study by MIT neuroscientists, a particular part of your visual cortex lights up when you look at any of these foods.
This newly discovered population of food-responsive neurons is located in the ventral visual stream, with populations that respond specifically to faces, bodies, places and words. Researchers say the unexpected discovery may reflect the special importance of food in human culture.
“Food is central to human social interactions and cultural practices. It is not just sustenance,” said Nancy Kanwisher, Walter A. Rosenblith Professor of Cognitive Neuroscience and member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds and Machines Says. “Food is central to our cultural identity, religious practice, and social interaction, and to many other things that humans do.”
The findings, based on analysis of a large public database of human brain responses to a set of 10,000 images, raise several additional questions about how and why neural populations evolve. In future studies, the researchers hope to find that people’s reactions to certain foods may vary based on their likes and dislikes, or their familiarity with certain types of food.
MIT postdoc Meenakshi Khosla is the paper’s lead author along with MIT research scientist N Apoorva Ratan Murthy. The study appears today in the journal current biology,
scene categories
20 years ago, while studying the ventral visual stream, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that selectively respond to faces. Later, he and other scientists discovered other regions that selectively respond to places, bodies, or words. Most of those areas were discovered when researchers specifically set out in search of them. However, the hypothesis-driven approach can limit what you’re looking for, Kanwisher says.
“There may be other things we can’t think of to see,” she says. “And even when we do find something, how do we know that it’s actually part of the original impressive structure of that passage, and not something we found because we were looking for it?”
To attempt to uncover the fundamental structure of the abdominal visual stream, Kanvishar and Khosla decided to analyze a large, publicly available dataset of whole-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects because He viewed thousands of images.
“We wanted to see what types of selectivity emerge when we apply a data-driven, hypothesis-free strategy, and whether they are consistent with what was previously found. The second goal was to see if we could identify those novel selections.” which have either not been previously hypothesized, or which are hidden due to the low spatial resolution of the fMRI data,” says Khosla.
To do this, the researchers applied a mathematical method that allows them to discover neural populations that cannot be identified with conventional fMRI data. An fMRI image is made up of several voxels – three-dimensional units that represent a cube of brain tissue.
Each voxel contains hundreds of thousands of neurons, and if some of those neurons belong to small 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 tease out the responses of neural populations within 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 found earlier are not just obscure properties of that pathway, but the dominant, dominant properties,” Knavisher says.
Interestingly, a fifth population also emerged, and it appeared to be selective for images of food.
“At first we were quite surprised because the food is not of uniform category,” says Khosla. “Things like apples and corn and pasta all seem to contradict each other, yet we found a single population that reacts similarly to all these diverse foods.”
The food-specific population, which researchers call the ventral food component (VFC), appears to be spread over two groups of neurons located on either side of the FFA. The fact that food-specific populations are dispersed among other range-specific populations may help explain why they have not been observed before, the researchers say.
“We think that food selectivity was difficult to characterize at first because populations selective for food are intertwined with other nearby populations that have different responses to other stimulus characteristics. The low spatial resolution of fMRI allows us to Seeing this selectivity precludes because the responses of different neural populations get mixed into a single tone,” Khosla says.
Paul Rogin, professor of psychology at the University of Pennsylvania, says, “The technique the researchers used to identify category-sensitive cells or regions is impressive, and it made the food category findings the most impressive of all known category- Retrieved sensitive systems.” who was not involved in the study.
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“I can’t imagine a way for the brain to reliably identify a variety of foods based on sensory characteristics. It makes it more appealing, and we have the possibility to actually try something new.”
food vs non food
The researchers used the data to train a computational model of the VFC, based on previous models Murthy had developed for the face and location recognition regions of the brain. This allowed the researchers to run additional experiments and predict the responses of the VFC. In one experiment, they fed models matched images of edible and non-food items that looked very similar – for example, a banana and a yellow crescent.
“Those matched stimuli have very similar visual properties, but the main feature in which they differ is edible versus inedible,” says Khosla. “We can feed those arbitrary stimuli through predictive models and see if it will still respond more to food than non-food, without collecting fMRI data.”
They can also use computational models to analyze very large datasets consisting of millions of images. Those simulations helped confirm that the VFC is highly selective for images of food.
From their analysis of human fMRI data, the researchers found that in some subjects, VFCs 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 likes or dislikes of a particular food may influence individuals’ responses to that food.
They also hope to study when and how this area becomes specialized during childhood, and the way it communicates with other parts of the brain. Another question is whether this food-selective population would be seen in other animals, such as monkeys, that do not seem to add cultural significance to the diet of humans.
Financing: 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.
About this neuroscience research news
Author: Anne Trafton
Source: MIT
contact: Anne Trafton – MIT
image: Image credits Jose-Louis Olivares, MIT. is given to
Basic Research: will appear in the conclusion current biology
(This story has not been edited by seemayo staff and is published from a rss feed)