Dartmouth researchers have obtained a 90 percent success rate in predicting human emotions based on brain activity.
The new study, which appears in the journal PLOS Biology, is unique in that researchers studied people within the general population, not just college students. As such, investigators believe the findings one day could help in diagnosing and treating a range of mental and physical health conditions across the population at large.
Many believe the neuroimaging study is a breakthrough in understanding the emotional experience. “It’s an impressive demonstration of imaging our feelings, of decoding our emotions from brain activity,” said lead author Luke Chang, an assistant professor in Psychological and Brain Sciences at Dartmouth.
“Emotions are central to our daily lives and emotional dysregulation is at the heart of many brain- and body-related disorders, but we don’t have a clear understanding of how emotions are processed in the brain. Thus, understanding the neurobiological mechanisms that generate and reduce negative emotional experiences is paramount.”
The quest to understand the “emotional brain” has motivated hundreds of neuroimaging studies in recent years. But for neuroimaging to be useful, sensitive and specific “brain signatures” must be developed that can be applied to individual people to yield information about their emotional experiences, neuropathology, or treatment prognosis.
Thus far, the neuroscience of emotion has yielded many important results but no such indicators for emotional experiences.
In the new study, researchers sought to develop a brain signature that predicts the intensity of negative emotional responses to evocative images; to test the signature in generalizing across individual participants and images; to examine the signature’s specificity related to pain; and to explore the neural circuitry necessary to predict negative emotional experience.
Chang and his colleagues studied 182 participants who were shown negative photos (bodily injuries, acts of aggression, hate groups, car wrecks, human feces) and neutral photos. Thirty additional participants were also subjected to painful heat.
Using brain imaging and machine learning techniques, the researchers identified a neural signature of negative emotion — a single neural activation pattern distributed across the entire brain that accurately predicts how negative a person will feel after viewing unpleasant images.
“This means that brain imaging has the potential to accurately uncover how someone is feeling without knowing anything about them other than their brain activity,” Chang said.
“This has enormous implications for improving our understanding of how emotions are generated and regulated, which have been notoriously difficult to define and measure.
“In addition, these new types of neural measures may prove to be important in identifying when people are having abnormal emotional responses — for example, too much or too little — which might indicate broader issues with health and mental functioning.”
Experts believe the study results are generalizable, or useful for everyone. Unlike most previous research, the new study included a large sample size that reflects the general adult population and not just young college students.
Researchers also used machine learning and statistics to develop a predictive model of emotion and, most importantly, tested participants across multiple psychological states, which allowed researchers to assess the sensitivity and specificity of their brain model.
“We were particularly surprised by how well our pattern performed in predicting the magnitude and type of aversive experience,” Chang said. “As skepticism for neuroimaging grows based on over-sold and -interpreted findings and failures to replicate based on small sizes, many neuroscientists might be surprised by how well our signature performed.”
Chang noted that the emotion brain signature using lots of people performed better at predicting how a person was feeling than their own brain data.
“There is an intuition that feelings are very idiosyncratic and vary across people,” he said “However, because we trained the pattern using so many participants — for example, four to 10 times the standard fMRI experiment — we were able to uncover responses that generalized beyond the training sample to new participants remarkably well.”