Literature DB >> 34417967

Altered resting-state functional networks in patients with premenstrual syndrome: a graph-theoretical based study.

Chengxiang Liu1,2, Chunmei Xuan3, Jiayu Wu1,2, Shasha Li3, Guang Yang1,2, Ruiqing Piao1,2, Gaoxiong Duan3, Demao Deng4, Peng Liu5,6.   

Abstract

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Previous studies have indicated alterations of brain functional connectivity in PMS patients. However, little is known about the overall organization of brain network in PMS patients. Functional magnetic resonance imaging data deriving from 20 PMS patients and 21 healthy controls (HCs). Pearson correlation between mean time-series was used to estimate connectivity matrix between each paired regions of interest, and the connectivity matrix for each participant was then binarized. Graph theory analysis was applied to assess each participant's global and local topological properties of brain functional network. Correlation analysis was performed to evaluate relationships between the daily rating of severity of problems (DRSP) and abnormal network properties. PMS patients had lower small-worldness values than HCs. PMS-related alterations of nodal properties were mainly found in the posterior cingulate cortex, precuneus and angular gyrus. The PMS-related abnormal connectivity components were mainly associated with the thalamus, putamen and middle cingulate cortex. In the PMS group, the DRSP score were negatively correlated with the area under the curves of nodal local efficiency in the posterior cingulate cortex. Our study suggests that the graph-theory method may be one potential tool to detect disruptions of brain connections and may provide important evidence for understanding the PMS from the disrupted network organization perspective.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Brain functional network; Functional magnetic resonance imaging; Premenstrual syndrome; Topological analysis

Mesh:

Year:  2021        PMID: 34417967     DOI: 10.1007/s11682-021-00518-4

Source DB:  PubMed          Journal:  Brain Imaging Behav        ISSN: 1931-7557            Impact factor:   3.978


  38 in total

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Journal:  Brain       Date:  2006-01-06       Impact factor: 13.501

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Journal:  Hum Brain Mapp       Date:  2019-07-22       Impact factor: 5.038

3.  Larger volume and different functional connectivity of the amygdala in women with premenstrual syndrome.

Authors:  Demao Deng; Yong Pang; Gaoxiong Duan; Huimei Liu; Hai Liao; Peng Liu; Yanfei Liu; Shasha Li; Wenfu Chen; Danhong Wen; Chunmei Xuan; Min Li
Journal:  Eur Radiol       Date:  2017-12-19       Impact factor: 5.315

4.  Hippocampal fractional amplitude of low-frequency fluctuation and functional connectivity changes in premenstrual syndrome.

Authors:  Gaoxiong Duan; Huimei Liu; Yong Pang; Peng Liu; Yanfei Liu; Geliang Wang; Hai Liao; Lijun Tang; Wenfu Chen; Xiaping Mo; Danhong Wen; Hua Lin; Demao Deng
Journal:  J Magn Reson Imaging       Date:  2017-06-03       Impact factor: 4.813

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Authors:  Timo De Bondt; Dirk Smeets; Pim Pullens; Wim Van Hecke; Yves Jacquemyn; Paul M Parizel
Journal:  Brain Res       Date:  2015-08-04       Impact factor: 3.252

6.  Diurnal rhythms of free estradiol and cortisol during the normal menstrual cycle in women with major depression.

Authors:  Ai-Min Bao; Yi-Fu Ji; Eus J W Van Someren; Michel A Hofman; Rong-Yu Liu; Jiang-Ning Zhou
Journal:  Horm Behav       Date:  2004-02       Impact factor: 3.587

Review 7.  Complex brain networks: graph theoretical analysis of structural and functional systems.

Authors:  Ed Bullmore; Olaf Sporns
Journal:  Nat Rev Neurosci       Date:  2009-02-04       Impact factor: 34.870

8.  The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.

Authors:  Vince D Calhoun; Tor D Wager; Anjali Krishnan; Keri S Rosch; Karen E Seymour; Mary Beth Nebel; Stewart H Mostofsky; Prashanth Nyalakanai; Kent Kiehl
Journal:  Hum Brain Mapp       Date:  2017-07-26       Impact factor: 5.038

9.  Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease.

Authors:  Michelle Case; Sina Shirinpour; Vishal Vijayakumar; Huishi Zhang; Yvonne Datta; Stephen Nelson; Paola Pergami; Deepika S Darbari; Kalpna Gupta; Bin He
Journal:  Neuroimage Clin       Date:  2018-11-14       Impact factor: 4.881

10.  The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline.

Authors:  Haifeng Chen; Xiaoning Sheng; Caimei Luo; Ruomeng Qin; Qing Ye; Hui Zhao; Yun Xu; Feng Bai
Journal:  Transl Neurodegener       Date:  2020-05-27       Impact factor: 8.014

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