In a first, researchers have found that it is possible to assess people’s ability to feel empathy by studying their brain activity while they are resting, rather than while they are engaged in specific tasks, an advance that may help clinicians better examine autism or schizophrenia patients who may not be able to fill out questionnaires, or express emotions.
The study, published in the journal Frontiers in Integrative Neuroscience, assessed the resting brain activity of 58 male and female participants of ages 18 to 35 using functional magnetic resonance imaging, or fMRI — a noninvasive technique for measuring and mapping brain activity through small changes in blood flow.
“Assessing empathy is often the hardest in the populations that need it most. Empathy is a cornerstone of mental health and well-being. It promotes social and cooperative behaviour through our concern for others,” said Marco Iacoboni, study co-author from the University of California Los Angeles (UCLA) in the US. “It also helps us to infer and predict the internal feelings, behaviour and intentions of others,” Iacoboni said. In the study, the participants were told to let their minds wander while keeping their eyes still, by looking at a fixation cross on a black screen.
The participants then completed questionnaires designed to measure empathy which rated how statements such as “I often have tender, concerned feelings for people less fortunate than me” and “I sometimes try to understand my friends better by imagining how things look from their perspective” described them on a five-point scale from “not well” to “very well.”
Using a form of artificial intelligence called machine learning, the scientists attempted to predict the participants’ empathic disposition, characterised as the willingness and ability to understand another’s situation, by analysing the brain scans.
The researchers made the predictions by looking into the resting activity in specific brain networks that earlier studies had demonstrated as important for empathy, the study noted.
“We found that even when not engaged directly in a task that involves empathy, brain activity within these networks can reveal people’s empathic disposition,” Iacoboni said. “The beauty of the study is that the MRIs helped us predict the results of each participant’s questionnaire,” he added.
Leonardo Christov-Moore, study co-author from the University of Southern California, said that the new technique may be expanded to improve treatment as well as diagnosis. “The predictive power of machine learning algorithms like this one, when applied to brain data, can also help us predict how well a given patient will respond to a given intervention, helping us tailor optimal therapeutic strategies from the get-go,” Christov-Moore said.