In recent years, researchers have found ways to remove specific fear from the brain, increase a person’s self-confidence, or even change people’s preferences, using a combination of artificial intelligence and brain-scraping technology. Their approach could provide new treatments for patients with conditions such as post-traumatic stress disorder (PTSD), phobias or anxiety disorder.
But while this approach is very promising, in some people it is still unsuccessful. Why are there such differences in output? A better understanding of how the brain can self-regulate its own patterns of activity would go a long way toward establishing the method for clinical use. The researchers in charge of this method have therefore disseminated specific data (comprising five different studies) to the community, in an effort to translate them from basic science to application. acceleration.
This method is called ‘Decoded Neurofeedback’, and is based on a method of reading and recognizing certain information in the brain – for example, fear memory. Dr. Mitsuo Kawato, Director of Computational Neuroscience Laboratories at the ATR International Institute in Japan, and senior author of the paper and who started this method ten years ago, explained: “In Decoded Neurofeedback experiments, brain scanning to monitor brain activity, and to identify complex activity patterns that resemble a particular memory or state of mind.When the pattern is found, we give a small reward to our experimental participants. Mental state. Importantly, participants do not need to be aware of the content of the patterns for this to work. ”
Dr Aurelio Cortese, senior researcher at ATR Institute International and lead author of the paper, outlined the vision for releasing the data: “The Decoded Neurofeedback approach could bring significant benefits to clinical populations across Traditional Remedies Patients may avoid the stress associated with exposure to medications, or side effects caused by established drugs, so it is vital that we accelerate the development of the Decoded Neurofeedback method – and this will only be possible if more scientists can work on their own data. ”
The research group has built a valuable neuroimaging database of more than 60 Decoded Neurofeedback training recipients. This database includes structural images of the brain, functional images of the brain, machine learning decoders, and additional processing data. Anyone wishing to use the database must apply through an ATR institution store  no Synapse , an online source of neuroscientific data. Details of how to access the database are specified in the original release as well as the ATR and Synapse websites.
 ATR institute store
Cortese A, Tanaka SC, Amano K, Koizumi A, Lau H, Sasaki Y, Shibata K, Taschereau-Dumouchel V, Watanabe T, Kawato M. “The DecNef collection, fMRI data from closed disconnect neurofeedback tests” Scientific data.
The five original studies included are listed below:
Study 1: Shibata K, Watanabe T, Kawato M, Sasaki Y (2016) Different patterns of activity in the same brain region followed opposite emotional states: PLoS Biol. 14 (9): e1002546. doi: 10.1371 / journal.pbio.1002546.
Study 2: Amano K, Shibata K, Kawato M, Sasaki Y, Watanabe T (2016) Learning to link color guidance in early visual fields with concomitant neurofeedback fMRI. Curr Biol. 26 (14): 1861-1866. doi: 10.1016 / j.cub.2016.05.014.
Study 3: Koizumi A, Amano K, Cortese A, Shibata K, Yoshida W, Seymour B, Kawato M, Lau H (2016) Reducing fearless fear by reinforcing neural activity that avoids sensory expression. Behavior Nat Hum. 1: 0006. doi: 10.1038 / s41562-016-0006.
Study 4: Cortese A, Amano K, Koizumi A, Kawato M, Lau H (2016) Multivoxel neurofeedback selectively alters confidence without altering visual performance. Nat Commun. 7: 13669. doi: 10.1038 / ncomms13669.
Study 5: Taschereau-Dumouchel V, Cortese A, Chiba T, Knotts JD, Kawato M, Lau H (2018) Towards unconscious neural reinforcement intervention for common fear. Proc Natl Acad Sci USA. 115 (13): 3470-3475. doi: 10.1073 / pnas.1721572115.
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