Evidence map›Paper›PMID 34686967›Full record

ArticleBrain imaging and behavior2022

Abnormal functional connectivity of the salience network in insomnia.

Yongxin Cheng, Ting Xue, Fang Dong, Yiting Hu, Mi Zhou, Xiaojian Li, Ruoyan Huang, Xiaoqi Lu, Kai Yuan, Dahua Yu

Abstract read
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In one paragraph

Article in Brain imaging and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  11. Difference of Resting State Functional Connectivity between Depressive Patients with and without Clinically Significant Insomnia.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025
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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

10 authors.

Yongxin Cheng *Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Ting Xue *Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China. xueting41@imust.edu.cn.
Fang DongInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Yiting HuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Mi ZhouInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Xiaojian LiInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Ruoyan HuangInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China.
Xiaoqi LuSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, Inner Mongolia, China.
Kai YuanInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China. kyuan@xidian.edu.cn.
Dahua YuInner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, Inner Mongolia, China. fmydh@imust.edu.cn.ORCID http://orcid.org/0000-0001-7850-7512

Funding

Chunhui Program of the Ministry of Education of the People's Republic of China 2018-45Fundamental Research Funds for the Central Universities JB151204National Natural Science Foundation of China 31800926National Natural Science Foundation of China 61771266National Natural Science Foundation of China 81401478National Natural Science Foundation of China 81470816National Natural Science Foundation of China 81471737National Natural Science Foundation of China 81701780National Natural Science Foundation of China 81871426National Natural Science Foundation of China 81871430Natural Science Basic Research Plan in Shaan-xi Province of China 2018JM7075Natural Science Foundation of Guangxi Province 2017GXNSFBA198221Natural Science Foundation of Inner Mongolia 2019JQ07Natural Science Foundation of Inner Mongolia 2020MS08059Natural Science Foundation of Inner Mongolia 2021MS08014Project of Guangxi Science and Technology GuiKeAD19110133science and technology planning project of Inner Mongolia Autonomous Region 2019GG109US National Institutes of Health, Intramural Research program Y1AA3009
6 · The paper itself

Abstract

The salience network plays an important role in detecting stimuli related to behavior and integrating neural processes. The aim of this study was to investigate changes in functional connectivity of the salience network in insomnia patients. Independent component analysis combined with a dual regression approach was used to examine functional connectivity differences in the salience network between patients with insomnia (n = 33) and healthy controls (n = 33). Pearson correlation analysis was used to analyze the relationship between differences in functional connectivity and the clinical characteristics of insomnia patients. Compared to healthy controls, insomnia patients showed increased functional connectivity in the dorsal anterior cingulate cortex within the salience network, as well as greater connectivity between the salience network and other brain regions including the dorsolateral prefrontal cortex, superior frontal gyrus, sensorimotor area and brain stem. The correlation analysis showed that increased functional connectivity between the salience network and left dorsolateral prefrontal cortex was positively correlated with Pittsburgh Sleep Quality Index score. Increased functional connectivity between salience network and several brain regions may be related to hyperarousal in insomnia patients. The connectivity between salience network and dorsolateral prefrontal cortex may potentially be used as a neuroimaging biomarker of sleep quality.

Indexed as

Sleep Initiation and Maintenance DisordersBrainBrain MappingHumansMagnetic Resonance ImagingPrefrontal CortexDual regressionIndependent component analysisInsomniaResting-state functional connectivitySalience network

Identifiers

PMID34686967

What OpenQuestion holds

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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.