MIT neuroscientists discover neurons with distinct language processing timescales

MIT neuroscientists have identified several brain regions responsible for processing language using functional magnetic resonance imaging (fMRI).

However, discovering the specific functions of neurons in those regions has proven difficult because fMRI, which measures changes in blood flow, doesn’t have a high resolution to reveal what small populations of neurons are doing.

Now, using a more precise technique that involves recording electrical activity directly from the brain, MIT neuroscientists have identified different clusters of neurons that appear to process different amounts of linguistic context.

These “temporal windows” range from just one word up to about six words.

Reflect different functions

The researchers say the temporal windows may reflect different functions for each population.

Populations with shorter windows may analyze the meanings of individual words, while those with longer windows may interpret more complex meanings created when words are strung together.

“This is the first time we see clear heterogeneity within the language network,” said Evelina Fedorenko, an associate professor of neuroscience at MIT.

“Across dozens of fMRI experiments, these brain areas all seem to do the same thing, but it’s a large, distributed network, so there’s got to be some structure there. This is the first clear demonstration that there is structure, but the different neural populations are spatially interleaved so we can’t see these distinctions with fMRI.”

Fedorenko, also a member of MIT’s McGovern Institute for Brain Research, is the study’s senior author, which appeared in Nature Human Behavior .

MIT postdoc Tamar Regev and Harvard University graduate student Colton Casto are the paper’s lead authors.

Temporal windows

Functional MRI, which has helped scientists learn a great deal about the roles of different parts of the brain, works by measuring changes in blood flow in the brain. These measurements act as a proxy of neural activity during a particular task.

However, each “voxel,” or three-dimensional chunk, of an fMRI image represents hundreds of thousands to millions of neurons and sums up activity across about two seconds, so it can’t reveal fine-grained detail about what those neurons are doing.

One way to get more detailed information about neural function is to record electrical activity using electrodes implanted in the brain. These data are hard to come by because this procedure is done only in patients who are already undergoing surgery for a neurological condition such as severe epilepsy.

In a 2016 study, Fedorenko reported using this approach to study the language processing regions of six people.

Electrical activity was recorded while the participants read four different types of language stimuli: complete sentences, lists of words, lists of non-words, and “jabberwocky” sentences — sentences that have grammatical structure but are made of nonsense words.

Those data showed that activity gradually built up throughout several words in some neural populations in language processing regions when the participants read sentences.

However, this did not happen when they read lists of words, lists of nonwords, and Jabberwocky sentences.

In the new study, Regev and Casto revisited those data and analyzed the temporal response profiles in greater detail.

Electrical activity

In their original dataset, the scientists had recordings of electrical activity from 177 language-responsive electrodes across the six patients.

Conservative estimates suggest that each electrode represents an average activity from about 200,000 neurons.

They also obtained new data from a second set of 16 patients, which included recordings from another 362 language-responsive electrodes.

When the researchers analyzed these data, they found that in some of the neural populations, activity fluctuated up and down with each word. In others, however, activity built up over multiple words before falling again, and yet others showed a steady buildup of neural activity over longer spans of words.

The researchers found that neural populations from language processing areas could be divided into three clusters by comparing their data with predictions made by a computational model designed to process stimuli with different temporal windows. These clusters represent temporal windows of either one, four, or six words.

“It really looks like these neural populations integrate information across different timescales along the sentence,” Regev says.