Evidence map›Paper›PMID 26635544›Full record

ArticleFrontiers in neuroanatomy2015

The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety.

Yan Tao, Bing Liu, Xiaolong Zhang, Jin Li, Wen Qin, Chunshui Yu, Tianzi Jiang

Abstract read
In one paragraph

Article in Frontiers in neuroanatomy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yan TaoBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Bing LiuBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Xiaolong ZhangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Jin LiBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
Wen QinDepartment of Radiology, Tianjin Medical University General Hospital Tianjin, China.
Chunshui YuDepartment of Radiology, Tianjin Medical University General Hospital Tianjin, China.
Tianzi JiangBrainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China ; Queensland Brain Institute, The University of Queensland Brisbane, QLD, Australia ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The default mode network (DMN) is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI). Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects with age ranging from 18 to 29 years old. The Group ICA method was used to extract the DMN component from functional magnetic resonance imaging (fMRI) data and a probabilistic fiber tractography technique based on DTI data was applied to construct the global structural connectivity pattern of the DMN. Then we used the graph theory method to analyze the DMN structural network and found that memory quotient (MQ) score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, the results we found that the network efficiency of the DMN were related to memory and anxiety measures, indicated that the DMN may play a role in the memory and anxiety.

Indexed as

anxietydefault mode networkDTImemorystructural network

Identifiers

PMID26635544
PMCPMC4659898

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.