Brain Network Analysis Of Audiovisual Integration In Older Adults

As the global population ageing problem becomes more and more serious,the research on cognitive function related to aging has received more attention.As we all know,multisensory integration participates in all aspects of human life,work and learning,and a large number of studies have proved that multisensory integration can enhance people’s cognitive processing.The impact of normal aging on multisensory integration has become one of the more important brain cognitive studies in recent years.As the cognitive function of human beings gradually degenerates with age,it is inevitable,but this cognitive impairment can be compensated by the compensation of sensory ability.Therefore,by studying multisensory integration,the cognitive process of old adults is explored.Today’s aging problem is significant.Audiovisual integration is a typical multisensory integration.In the past,most of the audiovisual integration studies related to aging were based on ERP technology and behavioral data to explore the cognitive differences between the old adults and young adults.Therefore,the brain function network caused by aging is not yet known.Topological changes in completing cognitive tasks.In this study,by designing time-asynchronized audiovisual integration experiments,brain network analysis methods and machine learning SVM classification techniques are used to explore age-related functional connections and network topology properties to explore cognitive awareness with aging.The brain mechanism of decline and memory loss.The main work of this research is as follows:(1)Data collectionThe experiment records the EEG and behavioral data of old adults and young adults in the time-asynchronized audiovisual integration task(the task has seven conditions),and analyzes the behavioral data and finds that old adults are in each both of the peak time points of young adults are higher under stimuli conditions.(2)Dynamic functional connection analysisDifferent from the traditional method of constructing brain networks based on the entire time series and extracting functional connections,the study calculates the average functional connections of the weighted brain networks at various time points to measure the global weighted network connection strength at each time point.It was found that there were significant inter-group differences between 0-400 ms in the theta band and 50-200 ms in the alpha band,and old adults and young adults induced stronger functional connections in the AV and V50 A conditions.(3)Static brain network analysisIn this study,a static brain network was constructed during the period of possible audiovisual integration,and its network topology properties were calculated and analyzed.It was found that for theta and alpha bands,old adults showed higher global and local efficiency under the AV and V50 A conditions.At the same time,there is a significant correlation between the network properties and the peak time points of behavior in the young adults’ audiovisual tasks.(4)Dynamic brain network analysisThis study is based on the evidence that the brain activity has time-varying properties over a period of time.During the period in which audiovisual integration may occur,a dynamic brain network sliding with time windows is constructed,and its network topology properties are calculated and analyzed.The results are found for theta and alpha bands,old adults showed higher network efficiencies under various stimuli conditions,and network efficiencies of two groups had obvious dynamic changes with time.(5)SVM classification researchThe functional connections and network properties of the static brain network and the dynamic brain network were extracted as the classification features.The principal component analysis technique was used to select the features.The SVM classifier was used to classify the brains of old adults and young adults.It is found that the accuracy of the dynamic brain network with functional connections and network properties as classification features is high.In this study,we studied the changes of the brain network topologies in the aging process,and found that during the time-asynchronized audiovisual integration task,old adults stimulated higher functional connectivity and network efficiency in theta and alpha bands.It may be that old adults are more difficult than young adults to concentrate and suppress distracting stimuli,so higher cognitive needs may be needed to complete the integration.At the same time,according to the SVM classification results,it can be found that the functional connectivity and network properties of the dynamic brain network can better detect the cognitive decline associated with normal aging,which reveals the potential physiological and pathological brain mechanisms during aging,and the prevention of brain diseases and the improvement of the health in old adults are of great significance.