ArticleHealthcare technology letters
Differential analysis of brain functional network parameters in MHE patients.
Article in Healthcare technology letters. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Differential analysis of brain functional network parameters in MHE patients.Healthcare technology lettersArticle
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4 authors.
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Abstract
Resting-state functional magnetic resonance imaging, using blood-oxygen-level-dependence signal data and graph theory, was employed to explore brain functional network parameter changes in 32 MHE patients and 21 healthy controls. The Gretna software package and spm8 are used to preprocess and process the data in matlab2012b to calculate the global efficiency (Eg), local efficiency (El), nodal degree (nodal De), nodal clustering coefficient (nodal Cp), nodal shortest path length (nodal Lp), and nodal betweenness (nodal Be) as brain functional network characteristic parameters. The BrainNet View soft is used to draw network maps and present surface-based data. Within the sparsity range of the selected network, A double-sample t-test revealed significant differences about the characteristic parameters in the following brain regions: the Nodal Cp in AAL62, AAL26, AAL43, and AAL47; the De in AAL66, AAL68, AAL47, and AAL74; the nodal Lp in AAL28, the El in AAL62, AAL31, and AAL47; the Eg in AAL28, AAL32, and AAL51, and the nodal Be in AAL28, AAL32, AAL76, and AAL82. These changes in brain network nodes may signal early brain damage in MHE, helping to characterize MHE and predict mental decline in cirrhosis patients.
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