| Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis | |
| Ji, Shanling1; Zhang, Yinghui2; Chen, Nan1; Liu, Xia3; Li, Yongchao1; Shao, Xuexiao1; Yang, Zhengwu1; Yao, Zhijun1; Hu, Bin1,4,5,6,7 | |
| 2022-02-01 | |
| Online publication date | 2022-01 |
| Source Publication | Brain Imaging and Behavior Impact Factor & Quartile |
| ISSN | 1931-7557 |
| EISSN | 1931-7565 |
| Volume | 16Issue:1Pages:336-343 |
| page numbers | 8 |
| 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. |
| Keyword | Entropy fMRI Psychiatric disorder Right fusiform gyrus |
| Publisher | SPRINGER |
| DOI | 10.1007/s11682-021-00507-7 |
| Indexed By | SCIE ; SSCI |
| Language | 英语 |
| WOS Research Area | Neurosciences & Neurology |
| WOS Subject | Neuroimaging |
| WOS ID | WOS:000740144500002 |
| Original Document Type | Article |
| PMID | 34997426 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/476772 |
| Collection | 兰州大学 |
| Corresponding Author | Yao, Zhijun; Hu, Bin |
| Affiliation | 1.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou 730000, Gansu, Peoples R China; 2.Mental Hlth Ctr Hosp Guangyuan, Guangyuan, Peoples R China; 3.Qinghai Normal Univ, Sch Comp Sci, Xining, Peoples R China; 4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai Inst Biol Sci, Shanghai, Peoples R China; 5.Chinese Acad Sci, Joint Res Ctr Cognit Neurosensor Technol, Lanzhou, Peoples R China; 6.Chinese Acad Sci, Inst Semicond, Lanzhou, Peoples R China; 7.Lanzhou Univ, Minist Educ, Engn Res Ctr Open Source Software & Real Time Sys, Lanzhou 730000, Gansu, Peoples R China |
| Recommended Citation GB/T 7714 | Ji, Shanling,Zhang, Yinghui,Chen, Nan,et al. Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis[J]. Brain Imaging and Behavior,2022,16(1):336-343. |
| APA | Ji, Shanling.,Zhang, Yinghui.,Chen, Nan.,Liu, Xia.,Li, Yongchao.,...&Hu, Bin.(2022).Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis.Brain Imaging and Behavior,16(1),336-343. |
| MLA | Ji, Shanling,et al."Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis".Brain Imaging and Behavior 16.1(2022):336-343. |
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