The researchers looked at brains that were scanned using functional magnetic resonance imaging [fMRI] data collected from the Human Connectome Project to study the functional organization of individuals` brains, and found differences that they were able to use to identify an individual from a group of more than 100 people.
"Functional connectivity profiles act as a `fingerprint` that can accurately identify subjects from a large group," the scientists wrote in the paper, which was published on October 12 in the journal Nature Neuroscience.
"In most past studies, fMRI data have been used to draw contrasts between, say, patients and healthy controls," explained Emily Finn, co-author of the study, entitled `Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.`"
"We have learned a lot from these sorts of studies, but they tend to obscure individual differences which may be important," said Finn.
The team was able to identify the individuals by their brain function even when the subjects were carrying out different tasks, and during periods of rest. Specifically, the scientists examined coordinated activity between pairs of regions in the brain, which implies two regions are functionally connected.
As well as identifying the individuals, the researchers were also able to predict how they would perform on intelligence tests.
"Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior," they wrote.
However, Finn explained that the potential of the finding rests not in predicting IQ, but assessing things which are harder to predict from a test, such as an individual`s response to medication, or even propensity to develop neuropsychiatric diseases.
"Maybe we could find a way to predict it so we can intervene and prevent mental illness," by identifying individual profiles of brain connectivity, said Finn.
More about:
