Training speech networks to treat aphasia
About 80,000 people develop aphasia each year in the United States alone. Nearly all of these individuals have difficulty speaking. For example, some patients (nonfluent aphasics) have trouble producing sounds clearly, making it frustrating for them to speak and difficult for them to be understood. Other patients (fluent aphasics) may select the wrong sound in a word or mix up the order of the sounds. In the latter case, “kitchen” can become “chicken.” Blumstein’s idea is to use guided speech to help people who have suffered stroke-related brain damage to rebuild their neural speech infrastructure.
Blumstein has been studying aphasia and the neural basis of language her whole career. She uses brain imaging, acoustic analysis, and other lab-based techniques to study how the brain maps sound to meaning and meaning to sound.
What Blumstein and other scientists believe is that the brain organizes words into networks, linked both by similarity of meaning and similarity of sound. To say “pear,” a speaker will also activate other competing words like “apple” (which competes in meaning) and “bear”(which competes in sound). Despite this competition, normal speakers are able to select the correct word.
In a study published in the Journal of Cognitive Neuroscience in 2010, for example, she and her co-authors used functional magnetic resonance imaging to track neural activation patterns in the brains of 18 healthy volunteers as they spoke English words that had similar sounding “competitors” (“cape” and “gape” differ subtly in the first consonant by voicing, i.e. the timing of the onset of vocal cord vibration). Volunteers also spoke words without similar sounding competitors (“cake” has no voiced competitor in English; gake is not a word). What the researchers found is that neural activation within a network of brain regions was modulated differently when subjects said words that had competitors versus words that did not.
One way this competition-mediated difference is apparent in speech production is that words with competitors are produced differently from words that do not have competitors. For example, the voicing of the “t” in “tot” (with a voiced competitor ‘dot’) is produced with more voicing than the “t” in “top” (there is no ‘dop’ in English). Through acoustic analysis of the speech of people with aphasia, Blumstein has shown that this difference persists, suggesting that their word networks are still largely intact.
