Mind Reading with Functional MRI

By Emily Singer
MIT Technology Review, March 5, 2008

Edited by Andy Ross

Scientists can accurately predict which of a thousand pictures a person is looking at by analyzing brain activity using functional magnetic resonance imaging (fMRI). The approach should shed light on how the brain processes visual information, and it might one day be used to reconstruct dreams.

"[The research] suggests that fMRI-based measurements of brain activity contain much more information about underlying neural processes than has previously been appreciated," says Jack Gallant, a neuroscientist at the University of California, Berkeley, and senior author of the study.

FMRI detects blood flow in the brain, The new study uses the technology to analyze neural information processing. By employing computer models to analyze the kinds of information gathered from the neural activity, scientists can try to assess how neural signals are processed in different brain areas and ultimately fused to create a cohesive perception.

According to the study, published in the journal Nature, scientists first gathered information about how the brain processes images by recording activity in the visual cortex as subjects looked at several thousand randomly selected pictures. The researchers compiled this information to develop a computer model that would predict the pattern of brain activity triggered by any image.

When volunteers were later shown a new image not included in the first set, the computer model was able to correctly predict which picture out of 120 or a thousand possibilities the person looked at with 90 or 80 percent accuracy, respectively.

Gallant and his team plan to use this technology to better understand how the visual system works by building computational models of various theories and then testing their ability to interpret brain scans. Similar methods might also be useful in determining how those steps go awry in people with different kinds of cognitive deficits.

In the long term, this technology might be used to study more ephemeral phenomena, such as dreaming. "It is currently unknown whether processes like dreaming and imagination are realized in the brain in a way that is functionally similar to perception," says Gallant. "If they are, then the techniques developed in our study should be directly applicable."

Gallant and others caution that the technology is not yet able to actually reconstruct from scratch what a person sees. While researchers are working on this capability, it is largely limited by the resolution of fMRI itself. Current brain-scanning devices have a spatial resolution of about a millimeter, an area that contains hundreds of neurons, each responding to different bits of visual information.
 

Scientists Can Now Read Minds

By James Randerson
The Guardian, March 5, 2008

Edited by Andy Ross

Scientists can predict images being viewed by people by using scanners to study their brain activity.

The breakthrough by American scientists took MRI scanning equipment to observe patterns of brain activity when a subject examined a range of black and white photographs. Then a computer was able to correctly predict in nine out of ten cases which image people were focused on. Random guesswork would have been accurate less than once in every hundred tries.

The scientists led by Dr Jack Gallant, University of California at Berkeley, said in Nature: "Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone. Imagine a general brain-reading device that could reconstruct a picture of a person's visual experience at any moment in time."

The researchers say the technique can only currently be applied to visual images and the experiments rely on cumbersome MRI scanning equipment and extremely powerful magnets. The software decoder itself has to be adapted to each individual during hours of training while in the scanner.

But the team have warned about potential privacy issues in the future when scanning techniques improve. Gallant: "It is possible that decoding brain activity could have serious ethical and privacy implications downstream in say, the 30-50 year time frame."

The technique relies on functional magnetic resonance imaging (fMRI), a standard technique that creates images of brain activity based on changes in blood flow to different brain regions.

The first step is to train the software decoder by scanning a subject's visual cortex while they view thousands of images over five hours. The next stage is to take a new set of images and use the decoder to predict the brain activity it would expect if the subject were viewing each of them. Finally, the subject views images from this second set while in the scanner. The software searches its predictions for the best match to its observations.

The software matched their observed brain activity with the predicted activity from the decoder. The closest match is the decoder's guess at which image the person is viewing. When using a set of 120 images, the software got it right nine out of ten times. With a thousand images, the accuracy dropped to eight out of ten. For random predictions, the success rate would be less than 1 percent.

The team estimate that if they chose from a billion images they would have a success rate of 20 percent. With that many images, Gallant said the software is close to working out what you are seeing from scratch. Said Gallant: "Probably the visual hardware is engaged and stuff from memory is sort of downloaded into your visual hardware and then replayed. To the extent that that is true we should be able to reconstruct imagery in dreams."
 

My Brain on Booze

By Emily Singer
MIT Technology Review, April 29, 2008

Edited by Andy Ross

Neuroscientist Alan Gevins has spent the past 40 years developing better ways to analyze the electrical signals emanating from our brains.

Electroencephalography (EEG) is a technology used to measure electrical activity produced by the brain via electrodes placed on the scalp. In recent years, enhanced computing power and increasingly sophisticated software have allowed scientists to more precisely record and analyze these signals.

Gevins, founder of SAM Technology and the San Francisco Brain Research Institute, has developed a system that combines EEG with cognitive testing to get a more direct measure of the brain's ability to remember and pay attention. He is now aiming to commercialize the technology.

Previous research by the group suggests that drinking may be more detrimental to our ability to function than previously thought. The brain effects of alcohol remain two to three hours after the behavioral effects have disappeared, even when blood alcohol level is as low as 0.02 percent.

The team is now finishing a large study looking at the effects of alcohol, marijuana, caffeine, and diphenhydramine, the active ingredient in Benadryl, on simulated driving, as well as on attention, working memory, and the ability to multitask.

After I gulp down my vodka, I head back to the testing room at SAM Technology. Earlier that morning, I was fitted with a cap dotted with sensors that detect electrical activity at different spots on the head. The headset sends its signals to a computer in the testing room. The device captures and processes my brain waves as I play a series of computer games.

An hour later, Gevins and Ilan show me the results of my testing. Their software analyzes a combination of rhythmic brain activity and evoked potentials, electrical signals linked to specific events in the world, like the appearance of a target in a video game.

After drinking, my performance on the games actually improved. But the EEG data revealed the true impact on my brain function: my brain had to work harder on the more complicated tasks after drinking. And it was slower to react to the targets on the computer screen.