In a new study, researchers from Caltech, Cedars-Sinai Medical Center, and the University of Salerno show that their new computing tool can predict a person’s intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. Functional MRI develops a map of brain activity by detecting changes in blood flow to specific brain regions. In other words, an individual’s intelligence can be gleaned from patterns of activity in their brain when they’re not doing or thinking anything in particular—no math problems, no vocabulary quizzes, no puzzles.
“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,” says Ralph Adolphs (PhD ’92), Bren Professor of Psychology, Neuroscience, and Biology, and 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. Adolphs and his colleagues downloaded the brain scans and intelligence scores from almost 900 individuals who had participated in the HCP, fed these 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 Julien Dubois (PhD ’13), a postdoctoral fellow at Cedars-Sinai Medical Center. But there is a lot of room for improvement, he adds. The scans are coarse and noisy measures of what is actually happening in the brain, and a lot of potentially useful information is still being discarded.
“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 says. “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?” 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. …
Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
But what makes this all the more remarkable is that the study controlled for brain size.
As I’ve blogged about before, brain size itself is known to explain 16% of the variation in IQ (perhaps 20% when you control for gender as many studies don’t), and because brain size was controlled, any IQ variation explained by brain size is independent of the 20% variation explained by brain activity.
So does this mean that by scanning both brain size and brain activity, they can explain perhaps 40% of IQ variation (20% + 20%)? If so a composite neurological score consisting of both brain size and brain activity would correlate 0.63 with IQ (the square root of 40% of the variance).
Of course none of this proves IQ is genetic, but what it may prove is that IQ is largely biological.
Or does it?
Here we get into the philosophically tricky distinction between culture and biology. Arthur Jensen has stated that g (general intelligence) appears to be a wholly biological variable, not amenable to psychological manipulation.
But how do we interpret Jensen’s claim when all psychology is ultimately biological. Even if one asserts that IQ tests measure only middle class knowledge, that in itself must leave a neurobiological imprint, as all learning does. If so, machine learning should eventually be able to scan your brain to determine whether you took French class in high school or read Hamlet, should it not?
So if culture itself affects brain physiology, what does it even mean for g to not be amenable to psychological manipulation? I think it means variation in g must be caused by biological variation (genes, nutrition etc) and not by cultural variation. Overall brain size is probably not much influenced by culture (except for in extreme pathological cases) but I don’t know about brain activity.