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

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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.

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Abbreviations

PMS:

Premenstrual syndrome

HCs:

Healthy controls

HAMA:

Hamilton anxiety scale

HAMD:

Hamilton depression scale

DRSP:

Daily record of severity of problems

BMI:

Body mass index

Cp :

Clustering coefficients

Lp :

Characteristic path lengths

γ:

Normalized clustering coefficients

λ:

Normalized characteristic path lengths

σ:

Small-worldness

Eg :

Network global efficiency

Eloc :

Network local efficiency

DC:

Degree centrality

Ei :

Nodal efficiency

NLe :

Nodal local efficiency

NLp :

Node characteristic path length

NCp :

Node clustering coefficient; The symbol “a” stands for the areas under the curves (AUC) of the topological metrics (Cp, Lp, γ, λ, σ, Eg, Eloc, DC, Ei, NLe, NLp, NCp)

R:

Right

L:

Left

preCC:

Precentral cortex

dlSFC:

Dorsolateral superior frontal cortex

OFC:

Orbitofrontal cortex

SMA:

Supplementary motor area

SFC:

Superior frontal cortex

MCC:

Middle cingulate cortex

PCC:

Posterior cingulate cortex

ANG:

Angular gyrus

preCUN:

Precuneus

PUT:

Putamen

PAL:

Pallidum

THA:

Thalamus

STC:

Superior temporal cortex

MTC:

Middle temporal cortex

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Funding

This study was financially supported by the National Natural Science Foundation of China under Grant Nos. 81771918, 82060315 and 81760886; Shaanxi Natural Science Foundation under Grant No. 2020JM-197; Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380086; and Fundamental Research Funds for the Central Universities under Grant No. XJS201202.

This study was authorized by the Medicine Ethics Committee of First Affiliated Hospital, Guangxi University of Chinese Medicine. All the research processes for this study were performed in consistent with the Declaration of Helsinki.

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  1. Chengxiang Liu and Chunmei Xuan these authors equally contributed to this manuscript

Authors and Affiliations

  1. Life Science Research Center, School of Life Science and Technology, Xidian University, Xi`an, 710071, Shaanxi, China

    Chengxiang Liu, Jiayu Wu, Guang Yang, Ruiqing Piao & Peng Liu

  2. Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, School of Life Science and Technology, Xidian University, Xi`an, 710071, Shaanxi, China

    Chengxiang Liu, Jiayu Wu, Guang Yang, Ruiqing Piao & Peng Liu

  3. Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, Guangxi, China

    Chunmei Xuan, Shasha Li & Gaoxiong Duan

  4. Department of Radiology, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, Guangxi, China

    Demao Deng

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  1. Chengxiang Liu
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Liu, C., Xuan, C., Wu, J. et al. Altered resting-state functional networks in patients with premenstrual syndrome: a graph-theoretical based study. Brain Imaging and Behavior 16, 435–444 (2022). https://doi.org/10.1007/s11682-021-00518-4

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