New Research Shows Individual Brain Activity Is As Unique As Fingerprints

These brain “connectivity profiles” alone allow researchers to identify individuals from the fMRI images of brain activity of more than 100 people, according to the study published 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,” said Emily Finn, a Ph.D. student in neuroscience and co-first author of the paper. “We have learned a lot from these sorts of studies, but they tend to obscure individual differences which may be important.”

Finn and co-first author Xilin Shen, under the direction of R. Todd Constable, professor of diagnostic radiology and neurosurgery at Yale, compiled fMRI data from 126 subjects who underwent six scan sessions over two days. Subjects performed different cognitive tasks during four of the sessions. In the other two, they simply rested. Researchers looked at activity in 268 brain regions: specifically, coordinated activity between pairs of regions. Highly coordinated activity implies two regions are functionally connected. Using the strength of these connections across the whole brain, the researchers were able to identify individuals from fMRI data alone, whether the subject was at rest or engaged in a task. They were also able to predict how subjects would perform on tasks.

Finn said she hopes that this ability might one day help clinicians predict or even treat neuropsychiatric diseases based on individual brain connectivity profiles.

Data for the study came from the Human Connectome Project led by the WU-Minn Consortium, which is funded by the 16 National Institutes of Health (NIH) Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Primary funding for the Yale researchers was provided by the NIH.

Publication: Emily S Finn, et al., “Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity,” Nature Neuroscience (2015); doi:10.1038/nn.4135

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