| The brain is one of the most powerful and complex organs in nature,and it is the basis for human beings to process advanced cognitive activities such as selfconsciousness,creative thinking and imagination.With the popularization of f MRI,probing the cognitive mechanism of the brain by task-state f MRI has become the top topic in neuroimaging.Among large quantities of cognitive experimental stimuli,insight in creative problem solving possesses great research value and useful purpose.Chunk decomposition with Chinese characters is an important representative of insight problem solving,which requires the mental representation transformation in accordance with behavioral goals.Previous studies on chunk decomposition have identified that the frontal,parietal,and occipital cortex in the cognitive control network selectively activated in response to chunk tightness.However,these studies are limited to the neural mechanism of isolated brain regions,and lose sight of the fact that the brain is a highly interactive and extremely complex information processing system.As a consequence,how to reveal the cognitive pattern of chunk decomposition with Chinese characters and predict its behavior performance from the whole brain network has become an important problem to be solved urgently.To sum up,the thesis comprehensively deciphers chunk decomposition with Chinese characters from grouplevel inference and individual-level prediction based on the interaction of brain network.The main contributions and innovations are as follows:First,the thesis proposed a novel method to model the cognitive pattern of chunk decomposition based on the global neuronal workspace theory(GNW).Given that functional localization strategy in previous studies may overlook the interaction brain regions,the thesis proposed that multiple specialized regions have to be interconnected to maintain goal representation during the course of chunk decomposition according to GNW.Therefore,the thesis applied a beta-series correlation method to investigate interregional functional connectivity in the event-related design of chunk decomposition tasks using Chinese characters,which would highlight critical nodes irrespective to chunk tightness.The results reveal a network of functional hubs with highly within or between module connections,including the orbitofrontal cortex,superior/inferior parietal lobule,hippocampus,and thalamus.The thesis speculates that the thalamus integrates information across modular as an integrative hub while the orbitofrontal cortex tracks the mental states of chunk decomposition on a moment-tomoment basis.The superior and inferior parietal lobule collaborate to manipulate the mental representation of chunk decomposition and the hippocampus associates the relationship between elements in the question and solution phase.Furthermore,the tightness of chunks is not only associated with different processors in visual systems but also leads to increased intermodular connections in right superior frontal gyrus and left precentral gyrus.In conclusion,the thesis first reveals the whole brain cognitive pattern of chunk decomposition in addition to the tightness-related nodes in the frontal and occipital cortex.Second,in this thesis,a reliable and efficient method is proposed to predict the behavior performance of chunk decomposition with Chinese characters based on functional motif coherence fingerprint.In view of the fact that previous researches concentrated on understanding the neural mechanism of chunk decomposition from the group-level inference,and were short of exploration on the diversity of individual-level behavior performance,the thesis extracts the high-order local descriptors of the complex brain network from the whole brain directed network and then uses the elastic net to reduce the high-dimensional network descriptors in order to solve the problem that it is difficult to be used for ubiquitous prediction analysis.Finally,the mapping from neuroimaging features to behavior performance is established based on gradient boosting decision tree.In general,the method has achieved a nice performance in predicting individual reaction time in terms of the mean absolute error is 0.365.The results at the individual level show that the more difficult the cognitive experimental stimuli are,the greater the difference of cognitive ability between individuals is.All in all,this thesis further elucidates the differences of chunk decomposition with Chinese characters from the perspective of individual-level prediction,which provides new insights and research ideas for further understanding chunk decomposition with Chinese characters. |
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