The Cognitive Load Effect And Modulation Of Working Memory Brain Network

Working memory is a basic cognitive ability of human beings,which is essential in learning,reading,reasoning and other cognitive activities.In the fields of psychology,biomedicine,computational neuroscience and other fields,behavioral and brain imaging research based on working memory has become a hot topic for researchers.It has been demonstrated that working memory is closely related to the frontal parietal brain regions.In addition,whole brain network integration during cognitive states was modulated by cognitive loads and was significantly correlated with the behavioral performance.However,few studies have systematically explored the timescale and cortical hierarchy changes of brain network under different cognitive loads,the relationship between these changes and behavior,as well as the relationship between working memory,individual intelligence and task switching ability.On the basis of functional magnetic resonance imaging technology,this dissertation systematically clarified the changes of working memory brain network in different cognitive states and the relationship with its affecting factors(i.e.individual intelligence scores and task switching ability).This dissertation included four studies,the main contents are as follows:In the first study,this dissertation discussed the effect of different experimental designs on the working memory functional connectivity under different cognitive loads.As a classic paradigm of working memory,N-back paradigm is often used as block design.The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce signal-to-noise ratio of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.Therefore,this dissertation aimed to disentangle the effects of block/continuous design and cognitive load on task-related functional connectivity networks by using repeated-measures analysis of variance.Both behavioral and brain network results showed that block and continuous design consistently reflected the cognitive load effect,and the interaction between experimental design and cognitive load was significant.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and brain-behavioral relationships.Secondly,based on the results of the research in the first study,the follow-up research in this dissertation focused on the continuous design of working memory data.Using different methods,the effects of cognitive load on brain functional connectivity network,and the relationship between the cognitive load and behavioral performance were explored from temporal and spatial aspects.From static and dynamic functional connectivity network,this dissertation explored the different network reconfiguration properties and their relationship with specific cognitive processes.Specifically,this dissertation utilized static functional connectivity(s FC)and sliding-window-based dynamic FC(d FC)approached to investigate the similarity and alterations of edge weights and network topology at different cognitive loads,particularly their relationships with specific cognitive process.Both d FC/s FC networks showed subtle but significant reconfigurations that correlated with task performance.At higher cognitive load,brain network reconfiguration displayed increased functional integration in the s FC-based aggregate network,but faster and larger variability of modular reorganization in the d FCbased time-varying network,suggesting difficult task require more integrated and flexible network reconfigurations.Moreover,s FC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes,but d FC-based network reconfigurations mainly linked with the high-order cognitive process.Our findings suggest that reconfiguration profiles of s FC/d FC networks provide specific information about cognitive functioning,which could potentially be used to study brain function.Thirdly,from the two aspects of the functional gradient of brain spatial distribution and the timescale of local brain regions,this dissertation explored how the local activation and distributed system of the brain jointly adjust the functional integration of the brain network.Specifically,using the methods of functional gradient,local timescale and network topology,this dissertation investigated the effects of cognitive load on the functional gradient and local timescale of brain regions.The results demonstrated that,firstly,increased cognitive load was associated with lower principal gradient in transmodal cortices,higher principal gradient in primary cortices,decreased decay rate and reduced timescale variability.Secondly,global properties including gradient variability,timescale decay rate,timescale variability and network topology were all modulated by cognitive load,with timescale variability related to behavioral performance.Moreover,in 2-back state,the timescale variability was indirectly and negatively linked with global network integration,which is mediated by gradient variability.In conclusion,this dissertation provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states,which could contribute to better understanding about cognitive load effects of human brain network.Finally,based on the cognitive load effect reflected by the brain function gradient,this dissertation explores factors(individual intelligence and task switching ability)affecting the working memory performance,as well as their relationship from two aspects of behavioral and functional connection brain network.Specifically,using the methods of functional gradient and network topology,the relationship between working memory,individual intelligence score and task switching ability were explored from two aspects of behavior and brain imaging.The results showed that working memory behavior performance and the gradient scores of somatomotor network,salience network and frontoparietal network were significantly correlated with intelligence scores and task switching costs.However,the impact of intelligence and task switching ability on working memory was affected from different perspectives,that is,the characteristic of the two rather than the commonness.In addition,the gradient scores of frontoparietal network in task switching and working memory tasks were related to individual intelligence scores.Finally,the gradient score of frontoparietal network in task switching was negatively correlated with working memory performance under the regulation of individual intelligence.This study provided new evidence for the relationship between working memory,individual intelligence and task switching ability,which helped to better understand the performance of working memory and cognitive function.In summary,using functional magnetic resonance imaging data,this dissertation first explored the experimental design effect of brain functional connectivity network for task state.Then,using appropriate design,this dissertation calculated the cognitive load effect in short and long timescale and spatial cerebral local and distributed changes of brain network,and the relationship between the network properties and behavior performance.Finally,as intelligence and task switching are infactors of working memory process,this dissertation calculated the relationship between these three factors.It provided new evidence for understanding of the relationship between brain regions and potential neural mechanisms under working memory process.