Research On Effectiveness Analysis Methods Of Emotion Regulation Based On Real-time FMRI Neurofeedback

Insomnia could lead to the occurrence of major psychiatric disorders such as depression and anxiety disorders,which not only affect personal physical health and quality of life,but also may cause social problems such as traffic accidents and production safety hazards.The neural mechanism and regulation method of sleep disorders have been included as an important content of the National Brain Science Research Program.Real-time functional magnetic resonance imaging(rt-fMRI)neurofeedback technology is a novel form of cognitive therapy for non-drug adjuvant therapy,which realizes the self-regulation of neural activity by real-time feedback of the activation state of brain regions.Insomnia is closely related to depressive emotion.The study of rt-fMRI neurofeedback emotion regulation based on emotional brain regions in insomnia patients is just beginning,and there are some bottlenecks.One of the difficulties is how to analyze the effectiveness of rt-fMRI neurofeedback from imaging.Therefore,studying the imaging markers of rt-fMRI neurofeedback emotion regulation in insomnia patients to assess the training effect and personalized prediction of the training effect has important clinical application value for the application of rt-fMRI neurofeedback in the treatment of insomnia.The study aimed to explore the effectiveness of rt-fMRI neurofeedback technology in improving the emotion regulation ability of insomnia patients,so we carried out a rt-fMRI neurofeedback emotion regulation experiment based on amygdala for insomnia patients,and then analyzed the changes of neural activity from the brain image characteristics with "seed point functional connection","effective connection of multiple brain regions",and "brain functional network connection" to study the emotion regulation effectiveness evaluation and prediction method of rt-fMRI neurofeedback technology.The main work is as follows:1.A rt-fMRI neurofeedback effectiveness evaluation method based on amygdala functional connectivity.The emotion regulation is a higher cognitive function of the brain,and the connectivity between the amygdala and multiple brain regions can reflect the effectiveness of emotion regulation.In this paper,we proposed a rt-fMRI neurofeedback effectiveness evaluation method based on amygdala functional connectivity,which extracted the functional connectivity network of the left and right amygdala of insomnia patients through voxel space and cortical space functional connectivity analysis based on the brain image characteristics of resting-state functional connectivity,and uses the difference between the functional connectivity networks before and after neurofeedback as an imaging marker to achieve neurofeedback effectiveness evaluation.The experimental results showed that the functional connectivity between amygdala and emotionrelated brain regions was significantly enhanced,while supplementary motor regions responsible for motor imagery was weakened in insomnia patients after neurofeedback training;the functional connectivity between left amygdala and left hemisphere para-insular regions and sensorimotor cortex were weakened.The result illustrated that rt-fMRI neurofeedback training using resting functional connectivity as an imaging marker could achieve effective assessment of improved emotion disorders.2.A rt-fMRI neurofeedback effectiveness evaluation method based on effective connectivity in multiple brain regions.The rt-fMRI neurofeedback emotion regulation processes rely on interactions between emotional brain regions,in which there are top-down emotional control and bottom-up emotional influences between the amygdala and prefrontal cortex.In this paper,we proposed a rt-fMRI neurofeedback effectiveness evaluation method based on effective connectivity in multiple brain regions,which explored the bidirectional information flow between the amygdala and prefrontal cortex in insomnia patients during rt-fMRI neurofeedback selfregulation using a dynamic causal model(DCM),and used the effective connectivity difference between the amygdala and prefrontal cortex before and after neurofeedback training as an imaging marker to achieve neurofeedback effectiveness evaluation.The experimental results showed that there was a bidirectional effective connection between the amygdala and the prefrontal cortex in the optimal model for DCM,and the neurofeedback stimulation effect returned to the prefrontal cortex and the connection between amygdala and prefrontal cortex.Further results suggested that rt-fMRI neurofeedback training enhanced bidirectional effective connectivity between the amygdala and prefrontal cortex.This result illustrated that rt-fMRI neurofeedback training using effective connectivity based on multiple brain regions as an imaging marker could achieve effective assessment of improved emotion disorders.3.A rt-fMRI neurofeedback effectiveness prediction method based on brain network connectivity.Rt-fMRI neurofeedback techniques as a potential means of treating insomnia have distinct differences in individual treatment effects.In this paper,a prediction method of rt-fMRI neurofeedback effectiveness in insomnia patients based on brain network connection was proposed.This method used independent component analysis to obtain the spatiotemporal distribution of brain activity in insomnia patients.By extracting the functional connection characteristics of brain network before neurofeedback,a support vector machine(SVM)prediction model based on brain networks connection was constructed to realize the individualized prediction of neurofeedback training effect.The experimental results showed that the functional connectivity matrix of brain networks such as default network,salience network,executive control network,basal ganglia and sensorimotor network before rt-fMRI neurofeedback in insomnia patients could be used as predictive features to construct a predictive model,and the accuracy of predicting the effect of rtfMRI neurofeedback training reached 87.5%,which could preliminarily provide a scientific basis for doctors and patients to select treatment methods for insomnia.