PMID- 32470024
OWN - NLM
STAT- MEDLINE
DCOM- 20200817
LR  - 20200817
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 15
IP  - 5
DP  - 2020
TI  - Alterations of functional and structural connectivity in patients with brain 
      metastases.
PG  - e0233833
LID - 10.1371/journal.pone.0233833 [doi]
LID - e0233833
AB  - Metastases are the most prevalent tumors in the brain and are commonly associated 
      with high morbidity and mortality. Previous studies have suggested that brain 
      tumors can induce a loss of functional connectivity and alter the brain network 
      architecture. Little is known about the effect of brain metastases on whole-brain 
      functional and structural connectivity networks. In this study, 14 patients with 
      brain metastases and 16 healthy controls underwent resting state functional 
      magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). We 
      constructed functional connectivity network using rs-fMRI signal correlations and 
      structural connectivity network using DTI tractography. Graph theoretical 
      analysis was employed to calculate network properties. We further evaluated the 
      performance of brain networks after metastases resection by a simulated method. 
      Compared to healthy controls, patients with brain metastases showed an altered 
      "small-world" architecture in both functional and structural connectivity 
      networks, shifting to a more randomness organization. Besides, the coupling 
      strength of functional-structural connectivity was decreased in patients. After 
      removing nodes infiltrated by metastases, aggravated disruptions were found in 
      both functional and structural connectivity networks, and the alterations of 
      network properties correlated with the removed hubs number. Our findings suggest 
      that brain metastases interfere with the optimal network organization and 
      relationship of functional and structural connectivity networks, and tumor 
      resection involving hubs could cause a worse performance of brain networks. This 
      study provides neuroimaging guidance for neurosurgical planning and postoperative 
      assessment of brain metastases from the aspect of brain networks.
FAU - Hua, Bo
AU  - Hua B
AD  - Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of 
      Science and Engineering, Zigong, China.
AD  - School of Automation and Information Engineering, Sichuan University of Science 
      and Engineering, Zigong, China.
FAU - Ding, Xin
AU  - Ding X
AD  - Department of Neurology, The First Affiliated Hospital of Chengdu Medical 
      College, Chengdu, China.
FAU - Xiong, Minghua
AU  - Xiong M
AD  - Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of 
      Science and Engineering, Zigong, China.
AD  - School of Automation and Information Engineering, Sichuan University of Science 
      and Engineering, Zigong, China.
FAU - Zhang, Fanyu
AU  - Zhang F
AD  - Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of 
      Science and Engineering, Zigong, China.
AD  - School of Automation and Information Engineering, Sichuan University of Science 
      and Engineering, Zigong, China.
FAU - Luo, Yi
AU  - Luo Y
AD  - Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of 
      Science and Engineering, Zigong, China.
AD  - School of Automation and Information Engineering, Sichuan University of Science 
      and Engineering, Zigong, China.
FAU - Ding, Jurong
AU  - Ding J
AUID- ORCID: 0000-0001-6708-5131
AD  - Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of 
      Science and Engineering, Zigong, China.
AD  - School of Automation and Information Engineering, Sichuan University of Science 
      and Engineering, Zigong, China.
FAU - Ding, Zhongxiang
AU  - Ding Z
AD  - Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang 
      University School of Medicine, Hangzhou, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200529
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Algorithms
MH  - Area Under Curve
MH  - Brain Neoplasms/*physiopathology/*secondary
MH  - Case-Control Studies
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Nerve Net/*physiopathology
PMC - PMC7259727
COIS- The authors have declared that no competing interests exist.
EDAT- 2020/05/30 06:00
MHDA- 2020/08/18 06:00
PMCR- 2020/05/29
CRDT- 2020/05/30 06:00
PHST- 2020/02/04 00:00 [received]
PHST- 2020/05/13 00:00 [accepted]
PHST- 2020/05/30 06:00 [entrez]
PHST- 2020/05/30 06:00 [pubmed]
PHST- 2020/08/18 06:00 [medline]
PHST- 2020/05/29 00:00 [pmc-release]
AID - PONE-D-20-02755 [pii]
AID - 10.1371/journal.pone.0233833 [doi]
PST - epublish
SO  - PLoS One. 2020 May 29;15(5):e0233833. doi: 10.1371/journal.pone.0233833. 
      eCollection 2020.