Phase fMRI reveals sparser function connectivity than magnitude fMRI

2019 ◽  
Author(s):  
Zikuan Chen ◽  
Vince D. Calhoun
Keyword(s):  
Function Connectivity

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

2020 ◽  
Author(s):  
Tong Chen
Keyword(s):  
Training Dataset ◽  
State Function ◽  
Selection Algorithm ◽  
Test Dataset ◽  
Less Is More ◽  
Functional Connections ◽  
Knowledge Based ◽  
Verbal Creativity ◽  
The Relationship ◽  
Function Connectivity

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.

scholarly journals Protein Function, Connectivity, and Duplicability in Yeast

2005 ◽  
Vol 23 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Anuphap Prachumwat ◽  
Wen-Hsiung Li
Keyword(s):  
Protein Function ◽  
Function Connectivity

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

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Chang Liu ◽  
Jie Xue ◽  
Xu Cheng ◽  
Weiwei Zhan ◽  
Xin Xiong ◽  
...  
Keyword(s):  
Conversion Rate ◽  
State Transition ◽  
Transition Process ◽  
Brain State ◽  
Healthy Children ◽  
Time Varying ◽  
The Brain ◽  
Function Connectivity ◽  
Time Point ◽  
Research Studies

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.

Rewarding Network Mechanism of Left Orbito-frontal Cortex Transcranial Magnetic Stimulation in Depression

2017 ◽  
Vol 41 (S1) ◽  
pp. S142-S143
Author(s):  
Q. Dai ◽  
Y. Xuntao ◽  
F. Zhengzhi
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Therapeutic Effect ◽  
Magnetic Stimulation ◽  
Antidepressant Treatment ◽  
Neural Mechanism ◽  
Clinical Depression ◽  
Dorsolateral Striatum ◽  
Lower Baseline ◽  
Before And After ◽  
Function Connectivity

ObjectiveThe difficulties in the clinical antidepressant treatment lead to the pursuing of more effective methods such as transcranial magnetic stimulation (TMS). Mixed findings from DLPFC targeted TMS result in the exploration of optimal stimulation location. Disturbed function of obitofrontal cortex (OFC) has been indicated in depression, which is involving in the remission of depression. However, whether it could be a more specific treating target is not tested. Simultaneously, disturbed reward network (RN) has been confirmed in depression, however, whether this could be improved by TMS treatment remains unclear.MethodsFourteen patients with major depressive disorder (MDD) were allocated in a four-week course of OFC targeted TMS. Motivated by the literature, before and after the treatment, the function connectivity of RN with the seed of ventral striatum was conducted. The results were also compared with the data from 33 healthy controls.ResultsThe OFC targeted TMS improved the clinical depression significantly and enhanced the function connectivity within the RN effectively. Specifically, lower baseline dorsolateral striatum connectivity predicted strong therapeutic effect of TMS on depression, while lower baseline insula connectivity predicted weak therapeutic effect on depression.ConclusionsThe findings offer the first experimental evidence of the therapeutic effect of OFC targeted TMS on clinical depression, enhanced function connectivity within RN might be the potential neural mechanism (Fig. 1). Lower dorsolateral striatum connection might be a reliable neural biomarker of strong responding for TMS treatment, which helps to identify the patients who will be cured by TMS most effectively.

scholarly journals Recognition Problems for Connectivity Functions

2021 ◽  
Author(s):  
◽  
Susan Jowett
Keyword(s):  
Connectivity Function ◽  
Submodular Set Function ◽  
Set Function ◽  
Function Connectivity

<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>

scholarly journals An Evolution Model of Trading Behavior Based on Peer Effect in Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Yue-Tang Bian ◽  
Lu Xu ◽  
Jin-Sheng Li ◽  
Jian-Min He ◽  
Ya-Ming Zhuang
Keyword(s):  
Stock Market ◽  
Mean Field ◽  
Field Analysis ◽  
Trading Behavior ◽  
Peer Effect ◽  
Behavior Dynamics ◽  
The Stability ◽  
Different Characteristics ◽  
The Relationship ◽  
Function Connectivity

This work concerns the modeling of evolvement of trading behavior in stock markets which can cause significant impact on the movements of prices and volatilities. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to peer effect in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the neighboring preferred degree, self-psychological preference, and the network topology of the relationship among them. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different characteristics of peer effect. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of neighboring preferred degree; the stability of equilibrium states of investors' behavior dynamics is directly related with the concavity and convexity of the peer effect function; connectivity and heterogeneity of the network play an important role in the evolution of the investment behavior in stock market.

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