In a new study just published in the Proceedings of the National Academy of Sciences today, an international team of scientists reported that they can now make people less afraid of everyday objects of phobia such as snakes and spiders, by directly manipulating the brain activity in human participants, while completely bypassing their conscious awareness – such that the procedure itself is free from the typical subjective unpleasantness one may need to go through in traditional psychotherapeutic treatments.
The study is based on recent experiments conducted at the Advanced Telecommunications Research Institute International, Japan, which have already demonstrated the effectiveness of rationale applied here. By using cutting edge methods borrowed from artificial intelligence similar to computer algorithms used to recognize faces from images, the team was able to read out unconscious spontaneous occurrences of mental images in the brain. That is, these researchers can tell if a participant's brain is 'unconsciously' thinking of a snake (which happens every now and then without our awareness), based on images acquired using conventional fMRI (functional magnetic resonance imaging, a measurement available in many hospitals). By giving the participant a small amount of monetary reward whenever this happens, the snake is thus associated with a positive feeling, thereby eventually becomes less frightening and unpleasant.
"We knew it could work in principle. The challenge was to figure out how to read out the snake-related thoughts from the brain images in the clinic, with actual patients rather than normal participants in the laboratory," says lead author Dr. Vincent Taschereau-Dumouchel, who is a clinical psychologist by training. "The big difference is, in normal participants we can show them many images of snakes, and let the computer algorithm learn what is the relevant pattern of brain activity from a large amount of data. But if we are to apply this procedure to patients, who are uncomfortable with seeing snakes in the first place, this becomes a problem."
The team devised an innovative solution to the problem, by inferring the patterns of brain activity from other participants.
"We can think of it this way: Let's say you are afraid of snakes. To decode the patterns of your brain activity, you do not necessarily have to see snakes. I, as a surrogate of yours, can see snakes for you, as I'm not afraid of them. From there, we could computationally infer what should be your brain signature for snakes, based on mine, with an ingenious method devised by the Haxby lab at Dartmouth, called hyperalignment," says last author Professor Hakwan Lau who is based at UCLA as well as the University of Hong Kong.
Although different individual's brain activity patterns have different spatial organizations, the hyperalignment method can correct for this discrepancy. Importantly, the team realized that a patient could also have not just one, but as many as dozens of surrogate participants to help. They have shown that with a large amount of data from many surrogates – all collected without having the potential patient see any of the feared images – they can crack the problem with reliable results.
"Not only did we replicate our previous findings, that after the intervention participants showed reduced physiological and brain responses to the feared images, the effects were somewhat more robust still. This is true even though we are now dealing with the added complexity of everyday images relevant to actual phobia," says Mitsuo Kawato, the corresponding author from the team based at ATR, Japan.
The team feel that now they are in a position to take this new method to test it in actual phobic patients. If successful, they are hoping that this can inspire novel type of treatments, not just for phobia for a variety of related psychiatric illnesses too, including post-traumatic stress disorders.
Taschereau-Dumouchel V, Cortese A, Chiba T, Knotts JD, Kawato M, Lau H: Towards an unconscious neural-reinforcement intervention for common fears, Proceedings of the National Academy of Sciences of the United States of America (PNAS), doi/10.1073/pnas.1721572115
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