Resting state brain default network in patients with motor aphasia resulting from cerebral infarction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-11

AUTHORS

Xin Wang, Meihao Wang, Weizhuo Wang, Huiru Liu, Jiejie Tao, Chuang Yang, Jiance Li

ABSTRACT

To explore the brain default mode network (DMN) in patients with motor aphasia resulting from cerebral infarction, we used resting state functional magnetic resonance imaging (fMRI) to investigate the possible neural mechanism. Thirteen patients with motor aphasia resulting from cerebral infarction and ten matched controls were selected in this study. All subjects were examined using resting state fMRI. We chose the posterior cingulate cortex as the region of interest and then used functional connectivity analysis to calculate the DMN functional connectivity and analyze differences in the functional connectivity between the two groups. Compared with normal controls, aphasia patient group showed a significantly decreased functional connectivity in bilateral medial frontal gyrus, superior frontal gyrus, middle frontal gyrus, middle temporal gyrus, precuneus and cuneus. The aphasia patient group showed increased functional connectivity mainly in bilateral medial frontal gyrus, middle frontal gyrus, inferior frontal gyrus, precentral gyrus, insula. The DMN in cerebral infarction motor aphasia patients showed significantly decreased functional connectivity in the resting state. The DMN most likely plays an important role in motor aphasia resulting from cerebral infarction. Furthermore, functional connectivity in the brain regions surrounding the left and right Broca’s areas was significantly enhanced due to compensatory mechanisms. This may be helpful for the recovery of language function in cerebral infarction patients with motor aphasia. More... »

PAGES

4069-4076

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11434-014-0491-3

DOI

http://dx.doi.org/10.1007/s11434-014-0491-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028181119


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