Recognition Problems for Connectivity Functions

<p>A connectivity function is a symmetric, submodular set function. Connectivity functions arise naturally from graphs, matroids and other structures. This thesis focuses mainly on recognition problems for connectivity functions, that is when a connectivity function comes from a particular type of structure. In particular we give a method for identifying when a connectivity function comes from a graph, which uses no more than a polynomial number of evaluations of the connectivity function. We also give a proof that no such method can exist for matroids.</p>

Recognition Problems for Connectivity Functions

<p>A connectivity function is a symmetric, submodular set function. Connectivity functions arise naturally from graphs, matroids and other structures. This thesis focuses mainly on recognition problems for connectivity functions, that is when a connectivity function comes from a particular type of structure. In particular we give a method for identifying when a connectivity function comes from a graph, which uses no more than a polynomial number of evaluations of the connectivity function. We also give a proof that no such method can exist for matroids.</p>

Recurrent Implication of Striatal Cholinergic Interneurons in a Range of Neurodevelopmental, Neurodegenerative, and Neuropsychiatric Disorders

Cholinergic interneurons are “gatekeepers” for striatal circuitry and play pivotal roles in attention, goal-directed actions, habit formation, and behavioral flexibility. Accordingly, perturbations to striatal cholinergic interneurons have been associated with many neurodevelopmental, neurodegenerative, and neuropsychiatric disorders. The role of acetylcholine in many of these disorders is well known, but the use of drugs targeting cholinergic systems fell out of favor due to adverse side effects and the introduction of other broadly acting compounds. However, in response to recent findings, re-examining the mechanisms of cholinergic interneuron dysfunction may reveal key insights into underlying pathogeneses. Here, we provide an update on striatal cholinergic interneuron function, connectivity, and their putative involvement in several disorders. In doing so, we aim to spotlight recurring physiological themes, circuits, and mechanisms that can be investigated in future studies using new tools and approaches.

Less is More: Predicting Creativity with Function Connectivity of Rest-fMRI using Feature Selection Methods

Creativity is the source of national scientific and technological progress andeconomic development. However, there is little research using machine learning method to studythe relationship between the functional connectivity and verbal creativity. In this paper, weproposed a prior-knowledge-based and data-driven based method (PDM) to explore the mostrelevant functional connections (FC) for the prediction of creativity. Specifically, we classify 289participants into high and low creation group by using their resting state function nuclear magneticresonance (fMRI) data. A total of 34,716 functional connections (FC) were analyzed in the wholebrain. The PDM selected 13 FCs out of the 34,716FCs, which consists of rank sum test, randomforest, and backward selection algorithm. The selected13 FCs can effectively distinguish high andlow creation groups with an accuracy of 85.6% in training dataset and 67.2% in test dataset, muchhigher than the accuracy achieved by using the 134FCs selected by traditional statistics method.The contribution of each FC to the prediction has been also studied. The results suggest that lessnumber of FCs can produce better prediction results, and that less important FCs may contributemore in the prediction. The finding of this paper may help us to better understand the neuralmechanisms of creative brain networks. And the proposed method could be useful in any otherresearches that intensively explore the relationship between neuroimaging metrics and behaviorscores.

Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI

BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.

Ensemble-based Ultrahigh-dimensional Variable Screening

Since the sure independence screening (SIS) method by Fan and Lv, many different variable screening methods have been proposed based on different measures under different models. However, most of these methods are designed for specific models. In practice, we often have very little information about the data generating process and different methods can result in very different sets of features. The heterogeneity presented here motivates us to combine various screening methods simultaneously. In this paper, we introduce a general ensemble-based framework to efficiently combine results from multiple variable screening methods. The consistency and sure screening property of proposed framework has been established. Extensive simulation studies confirm our intuition that the proposed ensemble-based method is more robust against model specification than using single variable screening method. The proposed ensemble-based method is used to predict attention deficit hyperactivity disorder (ADHD) status using brain function connectivity (FC).