Evidence map›Paper›PMID 38627958›Full record

ArticleAddiction biology2024

Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence.

Xian Mo, Ping Jiang, Jiayu Sun, Lu Lu, Lei Li, Xiaoqi Huang, Jiajun Xu, Jing Li, Junran Zhang, Qiyong Gong

Open access · goldAbstract read
In one paragraph

Article in Addiction biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Xian MoWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Ping JiangDepartment of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
Jiayu SunDepartment of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Lu LuDepartment of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
Lei LiDepartment of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.
Xiaoqi HuangDepartment of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0001-8686-5010
Jiajun XuMental Health Center, West China Hospital of Sichuan University, Chengdu, China.
Jing LiMental Health Center, West China Hospital of Sichuan University, Chengdu, China.
Junran ZhangCollege of Electrical Engineering, Sichuan University, Chengdu, China.ORCID 0000-0002-8035-8824
Qiyong GongDepartment of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-5912-4871
Chinese Academy of Medical Sciences & Peking Union Medical College · CNSichuan University · CN

Funding

1•3•5 project for disciplines of excellence, West China Hospital, Sichuan University ZYJC21041Deyang Science and Technology Foundation 82171513Med-X Center for Informatics funding project YGJC012Natural Science Foundation of Sichuan Province (Youth Science Foundation Project) 2022NSFSC1454Sichuan Science and Technology Program 23ZDYF2913
6 · The paper itself

Abstract

Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.

Indexed as

EmotionsSubstance Withdrawal SyndromeAnxietyBrainBrain MappingHumansMagnetic Resonance ImagingMaleanxiety and depressive symptomscortical thicknessmagnetic resonance imagingmethamphetaminestructural connectivity

Identifiers

PMID38627958
PMCPMC11021798
OpenAlexW4394873008

What OpenQuestion holds

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Registered trials

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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.