Evidence map›Paper›PMID 32314196›Full record

ArticleBrain imaging and behavior2021

The changes of brain functional networks in young adult smokers based on independent component analysis.

XianFu Wang, Ting Xue, Fang Dong, Yangding Li, Dongdong Xie, Chang Liu, Ming Zhang, Yanzhi Bi, Kai Yuan, Dahua Yu

Abstract read
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In one paragraph

Article in Brain imaging and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Regulation of craving for real-time fMRI neurofeedback based on individual classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
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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.

XianFu Wang *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.
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.
Yangding LiCollege of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China.
Dongdong XieInner 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.
Chang LiuInner 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.
Ming ZhangInner 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.
Yanzhi BiCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, 100101, 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.

Funding

National Natural Science Foundation of China 31800926National Natural Science Foundation of China 61771266National Natural Science Foundation of China 81401478National Natural Science Foundation of China 81401488National Natural Science Foundation of China 81470816National Natural Science Foundation of China 81471737National Natural Science Foundation of China 81571751National Natural Science Foundation of China 81571753National Natural Science Foundation of China 81701780National Natural Science Foundation of China 81871426National Natural Science Foundation of China 81871430the Fundamental Research Funds for the Central Universities JB151204The Natural Science Basic Research Plan in Shaan-xi Province of China 2018JM7075the Natural Science Foundation of Inner Mongolia 2017MS(LH)0814, 2018LH08079the program for Young adult Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region NJYT-17-B11the program of Science and Technology in Universities of Inner Mongolia Autonomous Region NJZY17262US National Institutes of Health, Intramural Research program Y1AA3009
6 · The paper itself

Abstract

Intrinsic functional connectivity (FC) networks, including the default mode network (DMN), central executive network (CEN), and salience network (SN), have been implicated in nicotine addiction. However, litter evidence exists about the abnormalities in the three networks in young adult smokers. Forty-eight young adult smokers and 49 age- and gender-matched non-smokers were recruited in the present study. Resting-state functional magnetic resonance imaging (fMRI) data were analyzed by a combination of independent component analysis (ICA) and dual regression to identify potential differences of FC patterns in the DMN, CEN, and SN. Compared to non-smokers, young adult smokers showed enhanced FC of the left posterior cingulate cortex (LPCC), right medial prefrontal cortex (RMPFC) and right precuneus within the DMN network, of the right dorsolateral prefrontal cortex (DLPFC) within the right CEN, and of the left anterior insula (LAI) within the SN. We also found increased FC between the DMN, CEN and key node of the SN (anterior insula, AI). Correlation analysis showed that the increased FC within the networks was significantly correlated with smoking behaviors (pack-years, smoking duration, FTND, first smoking age, and number of cigarettes per day). Our findings may provide additional evidence for conceptualizing the framework of nicotine addiction as a disease of intercommunicating brain networks.

Indexed as

Brain MappingSmokersBrainCerebral CortexHumansMagnetic Resonance ImagingYoung AdultFunctional networksIndependent component analysisResting stateYoung adult smokers

Identifiers

PMID32314196

What OpenQuestion holds

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Registered trials

None linked

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.