Our Neural Fingerprints
With advances in neuroimaging techniques and the computational ability needed to sort through these data-rich scans, that day may arrive sooner than you expect.
Cognitive scientists like Rajeev Raizada, who will be starting as assistant professor in the Department of Brain and CognitiveSciences in July, are laying the foundation for such diagnostic abilities by turning to functional magnetic resonance imaging (fMRI). Unlike X-rays, CATscans, and other types of brain imaging, fMRI involves no surgery, dyes, or exposure to radiation and can be safely deployed over time, providing a risk-free way for scientists to watch our brains in action.
"The brain has about 100 billion neurons, and they send electrical impulses to each other in a few thousandths of a second," saysRaizada. Scientists use fMRI to indirectly capture that electrical activity by picking up the increases in blood oxygen that occur when thousands of neurons become active. "If you pump your muscles, the body sends more blood to the area," Raizada explains. "When neurons are active, the circulatory system increases their blood supply to provide more oxygen and glucose."
"By a lucky quirk of nature, oxygenated blood has a slightly different magnetic signal," he explains. The scans virtually segment the brain into a three-dimensional grid of about 40,000 pixels known as voxels and, using a magnetic field, measure the changes in oxygen levels in each tiny segment.
The result is a huge amount of information about what's going on inside the skull. But it is precisely that wealth of data that is part of the challenge. An fMRI scan creates about one image every two seconds.Multiply that over time—some studies record data for more than 20 minutes at a stretch—and by the dozen or more participants in the typical study, and the data mushrooms.
That's where super computing comes into play. Researchers are hard at work creating statistical algorithms and other computations to sift through the millions and millions of data points the scans create, saysRaizada. First they have to separate out the background "noise," things like random fluctuations in blood flow or the magnetic field of the instrumentation that don't relate to the action being studied.
The ultimate challenge is to home in on the signal of interest, Raizada explains. For example, if researchers ask participants to look at a series of objects, they want to isolate just the brain patterns related to that activity. To find those patterns, Raizada compares the scans of different people all performing the same task and looks for similarities.
Here's the rub. Although brains are broadly similar,thinking patterns are individual. Says Raizada: "Each person has his or her own idiosyncratic neural fingerprint."
To tease out the similarities amidst the difference, Raizada looks at the relationship between one person's neural patterns and compares it to the relationships between others' neural patterns. Using such correlations,he says, is one possible solution to decoding the brain's thought processes.It's a puzzle that neuroscientists are approaching from many different angles.
Ultimately, Raizada believes neuroimaging may prove most useful in diagnosing the source of cognitive problems missing from behavioral tests alone. For example, two children may have outwardly similar difficulties with reading, but a brain scan may show that the impairment arises from different sources. One child may be struggling with attention issues, while the other child may have problems with phonological awareness.
Such distinctions are critical, notes Raizada. "Different types of impairment call for completely different types of treatments."
Imagine a day when neuroscientists will use a brain scan to diagnose the underlying causes of learning disabilities like dyslexia and to detect such impairments long before children experience difficulty or, potentially, failure in school.