Just by looking at a scan of your brain, scientists can now tell how smart you are.
Caltech researchers, assisted by Cedars-Sinai Medical Center and the University of Salerno, have developed a new computing tool that can predict a person’s intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. Their study shows that an individual’s intelligence can be gleaned from patterns of activity in their brain when they’re not doing or thinking anything in particular.
“We found if we just have people lie in the scanner and do nothing while we measure the pattern of activity in their brain, we can use the data to predict their intelligence,” Dr. Ralph Adolphs, Bren Professor of Psychology, Neuroscience, and Biology, said in a Caltech news release last week.
Adolphs is also the Director and Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Caltech Brain Imaging Center.
To train their algorithm on the complex patterns of activity in the human brain, Adolphs and his team used data collected by the Human Connectome Project (HCP), a scientific endeavor funded by the National Institutes of Health (NIH) that seeks to improve understanding of the many connections in the human brain. They downloaded the brain scans and intelligence scores from almost 900 individuals who had participated in the HCP, fed them into their algorithm, and set it to work.
After processing the data, the team’s algorithm was able to predict intelligence at statistically significant levels across these 900 subjects, says Dr. Julien Dubois, a postdoctoral fellow at Cedars-Sinai Medical Center.
“The information that we derive from the brain measurements can be used to account for about 20 percent of the variance in intelligence we observed in our subjects,” Dubois said. “We are doing very well, but we are still quite far from being able to match the results of hour-long intelligence tests, like the Wechsler Adult Intelligence Scale.”
Dubois also points out a sort of philosophical conundrum inherent in the work. “Since the algorithm is trained on intelligence scores to begin with, how do we know that the intelligence scores are correct?” he asked.
The researchers addressed this issue by extracting a more precise estimate of intelligence across 10 different cognitive tasks that the subjects had taken, not only from an IQ test.
In predicting intelligence from brain scans, the algorithm is doing something that humans cannot, as even an experienced neuroscientist cannot look at a brain scan and tell how intelligent a person is.
“If trained properly, these algorithms can answer questions as complex as the one we are trying to answer here,” says co-author Paola Galdi, a former PhD student at the University of Salerno and now a postdoctoral fellow at the University of Edinburgh. “They are very powerful, but if you actually ask, ‘How do they learn? How do they do these things?’ These are difficult questions to answer.”
The team conducted the study as part of an ongoing quest to build a diagnostic tool that can tell a great deal about a person’s mind from their brain scans. They said they would like to one day see MRIs work as well for diagnosing conditions like autism, schizophrenia, and anxiety as they currently do for finding tumors, aneurysms, or liver disease.
“Functional MRIs have not yet delivered on its promise as a diagnostic tool. We, and many others, are actively working to change this,” says Dubois. “The availability of large datasets that can be mined by scientists around the world is making this possible.”
Intelligence was chosen as one of the first test beds for the technology because research has shown that it’s very stable over time. A person’s IQ score will not vary much over a period of weeks, months, or years.
The researchers also conducted a parallel study, using the same test population and approach, that attempted to predict personality traits from fMRI brain scans. An individual’s personality, Adolphs says, is at least as stable as intelligence over a long period of time.
As it turned out, predicting personality using the method for predicting intelligence was much more difficult. “The personality scores in the database are just from short, self-report questionnaires,” Dubois said. “That’s not going to be a very accurate measure of personality to begin with, so it is no wonder we cannot predict it well from the MRI data.”
Adolphs and Dubois say they are now teaming up with colleagues from different fields, including Caltech philosophy professor Frederick Eberhardt, to follow up on their findings.
Papers that describe the two studies, titled “Resting-state functional brain connectivity best predicts the personality dimension of openness to experience,” and “A distributed brain network predicts general intelligence from resting-state human neuroimaging data,” are available online through bioR?iv (pronounced “bio-archive”), a free online archive and distribution service for articles in the life sciences operated by Cold Spring Harbor Laboratory. Their publication in Personality Neuroscience, and in Philosophical Transactions of the Royal Society, is pending.
Other Caltech co-authors of the study are Lynn K. Paul, a senior research scientist, and graduate student Yanting Han.
Funding for the studies was provided by the National Institute of Mental Health, the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech, the Carver Mead New Ventures Fund, and a grant from the Brain and Behavior Research Foundation.