Using a groundbreaking technique that records electrical activity directly from the brain, MIT neuroscientists have made a significant discovery in how our brains process language. While functional magnetic resonance imaging (fMRI) has previously identified key language regions, its limited resolution couldn’t decipher the specific roles of individual neurons. This new research reveals distinct clusters of neurons, each attuned to different lengths of linguistic context, ranging from single words to sequences of up to six.
“This is the first time we see clear heterogeneity within the language network,” explains Evelina Fedorenko, an associate professor of neuroscience at MIT and the study’s senior author. “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.”
Published in Nature Human Behavior, the study, led by MIT postdoc Tamar Regev and Harvard University graduate student Colton Casto, leverages the precision of intracranial electroencephalography (iEEG). This technique, used on patients undergoing neurological surgery, provides unparalleled detail by measuring electrical activity from electrodes implanted directly in the brain.
“It can take a few years to get enough data for a task because these patients are relatively rare, and in a given patient electrodes are implanted in idiosyncratic locations based on clinical needs, so it takes a while to assemble a dataset with sufficient coverage of some target part of the cortex. But these data, of course, are the best kind of data we can get from human brains: You know exactly where you are spatially and you have very fine-grained temporal information,” Fedorenko elaborates.
Analyzing data from 22 patients, the researchers observed distinct patterns of neural activity. Some neuron populations fired with each word, while others showed activity building up over multiple words before subsiding. This led them to categorize the neurons into three groups, each with “temporal windows” of one, four, or six words.
“It really looks like these neural populations integrate information across different timescales along the sentence,” Regev states.
This discovery suggests a division of labor within the language network. Neurons with shorter temporal windows might focus on individual word meanings, while those with longer windows could be responsible for deciphering the more complex meanings arising from word combinations and sentence structure.
“We already know that in the language network, there is sensitivity to how words go together and to the meanings of individual words,” Regev adds. “So that could potentially map to what we’re finding, where the longest timescale is sensitive to things like syntax or relationships between words, and maybe the shortest timescale is more sensitive to features of single words or parts of them.”
This groundbreaking research, funded by institutions including the U.S. National Institutes of Health and the Simons Center for the Social Brain, opens new avenues for understanding how our brains construct meaning from language, potentially paving the way for advancements in areas like language disorders and artificial intelligence.
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