Evidence map›Paper›PMID 34997426›Full record

SynthesisBrain imaging and behavior2022

Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis.

Shanling Ji, Yinghui Zhang, Nan Chen, Xia Liu, Yongchao Li, Xuexiao Shao, Zhengwu Yang, Zhijun Yao, Bin Hu

Abstract readMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis 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 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. Article
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

9 authors.

Shanling JiGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China.
Yinghui ZhangMental Health Center Hospital of Guangyuan, Guangyuan, China.
Nan ChenGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China.
Xia LiuSchool of Computer Science, Qinghai Normal University, Xining, China.
Yongchao LiGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China.
Xuexiao ShaoGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China.
Zhengwu YangGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China.
Zhijun YaoGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China. yaozj@lzu.edu.cn.
Bin HuGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Gansu Province, 730000, Lanzhou, China. bh@lzu.edu.cn.ORCID http://orcid.org/0000-0003-3514-5413

Funding

National Basic Research Program of China (973 Program) 2014CB744600National Key Research and Development Program of China 2019YFA0706200National Natural Science Foundation of China No.61632014, No.61627808, No.61210010Program of Beijing Municipal Science & Technology Commission Z171100000117005
6 · The paper itself

Abstract

Entropy is a measurement of brain signal complexity. Studies have found increased/decreased entropy of brain signals in psychiatric patients. There is no consistent conclusion regarding the relationship between the entropy of brain signals and mental illness. Therefore, this meta-analysis aimed to identify consistent abnormalities in the brain signal entropy in patients with different mental illnesses. We conducted a systematic search to collect resting-state functional magnetic resonance imaging (rs-fMRI) studies in patients with psychiatric disorders. This work identified 9 eligible rs-fMRI studies, which included a total of 14 experiments, 67 activation foci, and 1383 subjects. We tested the convergence across their findings by using the activation likelihood estimation method. P-value maps were corrected by using cluster-level family-wise error p < 0.05 and permuting 2000 times. Results showed that patients with different psychiatric disorders shared commonly increased entropy of brain signals in the left inferior and middle frontal gyri, and the right fusiform gyrus, cuneus, precuneus. No shared alterations were found in the subcortical regions and cerebellum in the patient group. Our findings suggested that the increased entropy of brain signals in the cortex, not subcortical regions and cerebellum, might have associations with the pathophysiology across mental illnesses. This meta-analysis study provided the first comprehensive understanding of the abnormality in brain signal complexity across patients with different psychiatric disorders.

Indexed as

Brain MappingMental DisordersBrainEntropyHumansLikelihood FunctionsMagnetic Resonance ImagingEntropyfMRIPsychiatric disorderRight fusiform gyrus

Identifiers

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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.