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Zhong H.,Beijing University of Technology | Chen J.,Tsinghua University | Han J.,Beijing University of Technology | Zhong N.,Beijing University of Technology | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, traditional research of documents selection focuses on the concept, and cannot meet the requirement of the systematic Brain Informatics study. This paper analyzes the characteristics of source knowledge firstly with concepts, attributes and relations. Then, we calculate the weight of documents by using the improved method of VSM. Finally, the experiments using real documents associated with brain science are given and calculating the weight of each document achieves a better effect of ranking selection. © 2014 Springer International Publishing. Source


Han J.,Beijing University of Technology | Chen J.,Tsinghua University | Zhong H.,Beijing University of Technology | Zhong N.,Beijing University of Technology | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Finding and learning related research is a necessary work in Brain Informatics studies. However, the keyword-based search on brain and mental big data center often brings a large amount of unnecessary results. It is very difficult to find needed research from those results for researchers. This paper proposes a Brain Informatics research recommendation system based on the Data-Brain and BI provenances. By choosing interest aspects from the Data-Brain and applying the unification of search and reasoning based on Data-Brain interests, the more accurate search can be realized to find really related literatures for supporting systematic Brain Informatics studies. © 2014 Springer International Publishing. Source


Liu Y.,Capital Medical University | Liu Y.,Beijing Key Laboratory of MRI and Brain Informatics | Li J.,Capital Medical University | Butzkueven H.,University of Melbourne | And 6 more authors.
European Journal of Radiology | Year: 2013

Objective: To investigate microstructural tissue changes of trigeminal nerve (TGN) in patients with unilateral trigeminal neuralgia (TN) by multiple diffusion metrics, and correlate the diffusion indexes with the clinical variables. Methods: 16 patients with TN and 6 healthy controls (HC) were recruited into our study. All participants were imaged with a 3.0 T system with three-dimension time-of-flight (TOF) magnetic resonance angiography and fluid attenuated inversion recovery (FLAIR) DTI-sequence. We placed regions of interest over the root entry zone of the TGN and measured fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). The mean values of FA, MD, AD and RD were compared between the affected and unaffected sides in the same patient, and to HC values. The correlation between the side-to-side diffusion metric difference and clinical variables (disease duration and visual analogy scale, VAS) was further explored. Results: Compared with the unaffected side and HC, the affected side showed significantly decreased FA and increased RD; however, no significant changes of AD were found. A trend toward significantly increased MD was identified on the affected side comparing with the unaffected side. We also found the significant correlation between the FA reduction and VAS of pain (r = -0.55, p = 0.03). Conclusion: DTI can quantitatively assess the microstructural abnormalities of the affected TGN in patients with TN. Our results suggest demyelination without significant axonal injury is the essential pathological basis of the affected TGN by multiple diffusion metrics. The correlation between FA reduction and VAS suggests FA as a potential objective MRI biomarker to correlate with clinical severity. © 2012 Elsevier Ireland Ltd. Source


Liu Y.,Capital Medical University | Duan Y.,Capital Medical University | Liang P.,Capital Medical University | Jia X.,Capital Medical University | And 6 more authors.
Acta Radiologica | Year: 2012

Background: A clinically isolated syndrome (CIS) is the first manifestation of multiple sclerosis (MS). Previous task-related functional MRI studies demonstrate functional reorganization in patients with CIS. Purpose: To assess baseline brain activity changes in patients with CIS by using the technique of regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI. Material and Methods: Resting-state fMRIs data acquired from 37 patients with CIS and 37 age- and sex-matched normal controls were compared to investigate ALFF differences. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration, and T2 lesion volume (T2LV) were further explored. Results: Patients with CIS had significantly decreased ALFF in the right anterior cingulate cortex, right caudate, right lingual gyrus, and right cuneus (P, 0.05 corrected for multiple comparisons using Monte Carlo simulation) compared to normal controls, while no significantly increased ALFF were observed in CIS. No significant correlation was found between the EDSS, disease duration, T2LV, and ALFF in regions with significant group differences. Conclusion: In patients with CIS, resting-state fMRI demonstrates decreased activity in several brain regions. These results are in contrast to patients with established MS, in whom ALFF demonstrates several regions of increased activity. It is possible that this shift from decreased activity in CIS to increased activity in MS could reflect the dynamics of cortical reorganization. Source


Li C.,Beijing University of Technology | Zhou H.,Beijing Key Laboratory of MRI and Brain Informatics | Zhou J.,Beijing Key Laboratory of MRI and Brain Informatics | Xiang J.,Taiyuan University of Science and Technology | And 2 more authors.
2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 | Year: 2015

In recent years, the emergence of the fMRI provides a possibility of further exploration for the human brain. The complex workings of the brain are composed of a plurality of brain functional networks. To further explore the change affected by task in human brain functional network, the efficiency of a method of effective connectivity is examined by comparing the two resting state fMRI data before and after a task. Distinguished from the view of the brain network as a whole in previous studies, the method of Granger causality analysis (GCA) we used is focused on the internal organization within a brain network. Also, the method of functional connectivity is used to compare the efficiency of Granger causality analysis. The results show that taking the default mode network (DMN) as an example, the method of Granger causality analysis can be more sensitive to the functional re-organization in a human brain network after a task compared with functional connectivity. And the target of incoming causal influences area changed from rPCu to BG after task. © 2015 IEEE. Source

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