{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:49:28Z","timestamp":1781153368938,"version":"3.54.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> Idiopathic blepharospasm (BSP) is a common adult-onset focal dystonia. Neuroimaging technology can be used to visualize functional and microstructural changes of the whole brain.<\/jats:p><jats:p><jats:bold>Method:<\/jats:bold> We used resting-state functional MRI (rs-fMRI) and graph theoretical analysis to explore the functional connectome in patients with BSP. Altogether 20 patients with BSP and 20 age- and gender-matched healthy controls (HCs) were included in the study. Measures of network topology were calculated, such as small-world parameters (clustering coefficient [<jats:italic>C<\/jats:italic><jats:sub>p<\/jats:sub>], the shortest path length [<jats:italic>L<\/jats:italic><jats:sub>p<\/jats:sub>]), network efficiency parameters (global efficiency [<jats:italic>E<\/jats:italic><jats:sub>glob<\/jats:sub>], local efficiency [<jats:italic>E<\/jats:italic><jats:sub>loc<\/jats:sub>]), and the nodal parameter (nodal efficiency [<jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub>]). In addition, the least absolute shrinkage and selection operator (LASSO) regression was adopted to determine the most critical imaging features, and the classification model using critical imaging features was constructed.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> Compared with HCs, the BSP group showed significantly decreased <jats:italic>E<\/jats:italic><jats:sub>loc<\/jats:sub>. Imaging features of nodal centrality (<jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub>) were entered into the LASSO method, and the classification model was constructed with nine imaging nodes. The area under the curve (AUC) was 0.995 (95% CI: 0.973\u20131.000), and the sensitivity and specificity were 95% and 100%, respectively. Specifically, four imaging nodes within the sensorimotor network (SMN), cerebellum, and default mode network (DMN) held the prominent information. Compared with HCs, the BSP group showed significantly increased <jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub> in the postcentral region within the SMN, decreased <jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub> in the precentral region within the SMN, increased <jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub> in the medial cerebellum, and increased <jats:italic>E<\/jats:italic><jats:sub>nod<\/jats:sub> in the precuneus within the DMN.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> The network model in BSP showed reduced local connectivity. Baseline connectomic measures derived from rs-fMRI data may be capable of identifying patients with BSP, and regions from the SMN, cerebellum, and DMN may provide key insights into the underlying pathophysiology of BSP.<\/jats:p>","DOI":"10.3389\/fneur.2021.708634","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T04:16:44Z","timestamp":1641961004000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Impaired Topographic Organization in Patients With Idiopathic 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