Changzhou Key Laboratory of Biomedical Information Technology

Changzhou, China

Changzhou Key Laboratory of Biomedical Information Technology

Changzhou, China
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Guo Q.,Changzhou University | Guo Q.,Changzhou Key Laboratory of Biomedical Information Technology | Zhou T.,Changzhou University | Zhou T.,Changzhou Key Laboratory of Biomedical Information Technology | And 6 more authors.
Brain and Behavior | Year: 2017

Background: Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Objective: Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. Methods: In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. Results: The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. Conclusions: The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems. © 2017 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.


Li W.,Changzhou University | Li W.,Changzhou Key Laboratory of Biomedical Information Technology | Zou L.,Changzhou University | Zou L.,Changzhou Key Laboratory of Biomedical Information Technology | And 6 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Full scalp electroencephalography (EEG) recording is generally used in brain computer interface (BCI) applications with multi-channel electrode cap. The data not only has comprehensive information about the application, but also has irrelevant information and noise which makes it difficult to reveal the patterns. This paper presents our preliminary research in selecting the optimal channels for the study of internet addiction with visual “Oddball” paradigm. A two-stage model was employed to select the most relevant channels about the task from the full set of 64 channels. First, channels were ranked according to power spectrum density (PSD) and Fisher ratio separately for each subject. Second, the occurrence rate of each channel among different subjects was computed. Channels whose occurrences was more than twice consisted the optimal combination. The optimal channels and other comparison combinations of channels (including the whole channels) were used to distinguish between the target and non-target stimuli with Fisher linear discriminant analysis method. Classification results showed that the channel selection method greatly reduced the abundant channels and guaranteed the classification accuracy, specificity and sensitivity. It can be concluded from the results that there is attention deficit on internet addicts. © Springer International Publishing Switzerland 2016.


Jiao Z.,Changzhou University | Jiao Z.,Changzhou Key Laboratory of Biomedical Information Technology | Zou L.,Changzhou University | Zou L.,Changzhou Key Laboratory of Biomedical Information Technology | And 3 more authors.
Journal of Information and Computational Science | Year: 2013

This paper develops a method to explore causality for time-series by using Vector Autoregressive (VAR) model and complex network measures. The Granger causality of multivariable time-series are analyzed based on VAR model, by which the weighed causality graph is built up to reveal a variety of causal relationship among components of time-series. Then the directed and weighted connectivity in Granger causality graph is described with complex network measures, and the statistical properties of multivariable time-series are characterized according to the network topological parameters. Simulation and experiment analysis demonstrate that the proposed method is feasible in testing the causality of general multivariable time-series as well as fMRI time-series. Copyright © 2013 Binary Information Press.


Zou L.,Changzhou University | Zou L.,Changzhou Key Laboratory of Biomedical Information Technology | Xu Y.,Changzhou University | Xu Y.,Changzhou Key Laboratory of Biomedical Information Technology | Zhou R.,Nanjing University
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | Year: 2015

In order to investigate the relationship and difference in brain functional connectivity when subjects process different valence of emotional images, a new method is employed to extract the relatively weak connected regions more accurately. First, a K-means algorithm based on density is proposed to analyze the brain functional connectivity and extract the brain structure model which has the functional connectivity. Then, aggregation index is introduced to evaluate the positioning accuracy of activated brain regions. The above results are also compared with the results using independent component analysis (ICA) algorithm; Finally, the advantage of K-means algorithm based on density in the field of brain functional connectivity analysis is demonstrated in terms of the intensity of voxel activation and the position precision of activated brain regions. The experimental results show that relatively obvious activity areas mainly distributed in the frontal lobe, cingulum and hypothalamus in the process of emotional stimulation processing, which provides a more reliable method for subsequent clinical observation and diagnosis. ©, 2015, Institute of Computing Technology. All right reserved.


Zou L.,Changzhou University | Zou L.,Changzhou Key Laboratory of Biomedical Information Technology | Guo Q.,Changzhou University | Guo Q.,Changzhou Key Laboratory of Biomedical Information Technology | And 7 more authors.
Technology and Health Care | Year: 2016

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity. © 2016 - IOS Press and the authors. All rights reserved.

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