Science. Brain activity is as unique – and identifying – as a fingerprint
INVESTIGATION. Each of us is unique, with our own strengths, weaknesses and idiosyncrasies. While this is a truism everyone grasps intuitively, it’s been difficult to determine if and how this individuality is reflected in brain activity.
To investigate, my colleagues and I looked at brain images from volunteers scanned using functional magnetic resonance imaging, or fMRI. This technique measures neural activity via blood flow in the brain while people are awake and mentally active. We calculated a “functional connectivity profile” for each person based on their individual patterns of synchronized activity between different parts of the brain.
In fact, it turns out that the ebb and flow of brain activity is like a fingerprint: each person has their own signature pattern, according to our study just published in the journal Nature Neuroscience. Using only their connectivity profiles, we could identify individuals from a group. Based purely on these profiles, we could also predict how people would perform on one type of intelligence test.
Trading the forest for the trees
fMRI is the best tool we have to study what goes on in a living, thinking human brain in a safe and noninvasive way. And yet fMRI data is notoriously noisy – lots of things influence the signal at any given time, and only some of them are related to the actual brain activity that we care about.
This is why, traditionally, fMRI studies average together data from many different people: the idea is that by finding common patterns of brain activity, we can get rid of much of the noise and end up with something closer to the “true” signal. Essentially, we blend all the individuals’ signals to get one version that’s representative of the whole population.
But you don’t need to be a brain scientist to recognize that everyone is different; this averaging probably obscures interesting activity patterns that are idiosyncratic to each person. And for fMRI to be practically useful – in medicine, for example – we’d need to get meaningful information based on a scan from a single person.
We set out to prove that analyzing fMRI data from individual people is indeed possible, by showing that these idiosyncratic activity patterns are reliable enough to identify individuals from a large group.
Analyzing individual scans
We used data from the Human Connectome Project (HCP), a major research effort to collect brain-imaging data along with behavioral, demographic and genetic information from a large number of healthy people. So far, data from 500 people have been released, and there are plans to collect 1,200 in total. All the data are made publicly available, so researchers anywhere can download it, analyze it in different ways, and mine it for interesting insights.
We looked at data from the first 126 participants in the HCP. Each person was scanned six different times. During two of the scans, people were simply resting, letting their minds wander. During the other four scans, they worked on some type of cognitive task: trying to hold items in mind in a test of working memory, listening to a story, solving math problems, looking at emotional faces or moving different parts of their body.
To analyze the fMRI data for each individual participant, we first divided the whole brain into 268 separate regions. While it’s an open question just how many different functional regions there are in the brain, previous work of ours has shown that using between 200 and 300 regions lets us detect subtle effects, while still keeping things manageable in terms of the time and computing power it takes to run the analyses.