Systems and methods for modulating cognitive control with real-time functional magnetic resonance neurofeedback

Abstract

The invention discloses a system and method for real-time functional magnetic resonance neurofeedback to regulate cognitive control. The brain signal generated by the magnetic resonance imager is transmitted to the TBV and matlab interface through the communication protocol, and the voxel decoding module is based on the selected control level. All the voxels in the brain network are preliminarily screened and weighted to complete the training of the classifier; the real-time functional magnetic resonance neurofeedback module calculates and sends brain signals based on the previously constructed classifier to judge the current cognitive control level of the brain The high and low probability values ​​of the neurofeedback training are carried out by the scores converted from the probability values; when the neurofeedback training spans multiple scans, the spatial coordinate conversion module projects the voxels selected by the classifier from the standard space to the current scan Local space, so that feedback training can be accurately mapped to the same voxel. The present invention can improve self-cognitive control by performing real-time magnetic resonance neurofeedback training on voxel state recognition of individual cognitive control brain network.

Description

technical field [0001] The invention belongs to the technical field of cognitive neuroscience, is based on cognitive science theory, and relates to a platform for adjusting self-cognitive control level by real-time functional magnetic resonance neurofeedback technology, and then regulating addiction craving. Background technique [0002] Addiction-related studies have found that different regions of the prefrontal cortex have abnormal activities under different tasks (eg, motor inhibition, attention orientation, and emotion regulation). The underlying cause of the abnormal activity in the prefrontal brain area is likely to be related to the decline of different levels of cognitive control, such as: seeing addictive substances and responding inappropriately to stimuli, seeing stimuli but not being able to respond to the situation. Making conditioning, stimulus and action linkages by memory inappropriate retrieval. Based on this, neurofeedback targeting different regions of t...

AI Technical Summary

Problems solved by technology

[0003] In the past 5 years, more researches have been applied to the treatment of addiction. The feedback brain areas roughly cover the brain areas related to addiction, but the selection of brain areas is rarely driven by very clear theories and used to explain the training. effectiveness

In addition, the current rtfMRI technology is mostly based on the training of a single brain region and the signal of the feedback signal is relatively single, but the effect is unstable and there are large individual differences in the long-term effect

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