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      The gut microbiome is associated with brain structure and function in schizophrenia

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          Abstract

          The effect of the gut microbiome on the central nervous system and its possible role in mental disorders have received increasing attention. However, knowledge about the relationship between the gut microbiome and brain structure and function is still very limited. Here, we used 16S rRNA sequencing with structural magnetic resonance imaging (sMRI) and resting-state functional (rs-fMRI) to investigate differences in fecal microbiota between 38 patients with schizophrenia (SZ) and 38 demographically matched normal controls (NCs) and explored whether such differences were associated with brain structure and function. At the genus level, we found that the relative abundance of Ruminococcus and Roseburia was significantly lower, whereas the abundance of Veillonella was significantly higher in SZ patients than in NCs. Additionally, the analysis of MRI data revealed that several brain regions showed significantly lower gray matter volume (GMV) and regional homogeneity (ReHo) but significantly higher amplitude of low-frequency fluctuation in SZ patients than in NCs. Moreover, the alpha diversity of the gut microbiota showed a strong linear relationship with the values of both GMV and ReHo. In SZ patients, the ReHo indexes in the right STC ( r = − 0.35, p = 0.031, FDR corrected p = 0.039), the left cuneus ( r = − 0.33, p = 0.044, FDR corrected p = 0.053) and the right MTC ( r = − 0.34, p = 0.03, FDR corrected p = 0.052) were negatively correlated with the abundance of the genus Roseburia. Our results suggest that the potential role of the gut microbiome in SZ is related to alterations in brain structure and function. This study provides insights into the underlying neuropathology of SZ.

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          Most cited references72

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          Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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            The Microbiota-Gut-Brain Axis

            The importance of the gut-brain axis in maintaining homeostasis has long been appreciated. However, the past 15 yr have seen the emergence of the microbiota (the trillions of microorganisms within and on our bodies) as one of the key regulators of gut-brain function and has led to the appreciation of the importance of a distinct microbiota-gut-brain axis. This axis is gaining ever more traction in fields investigating the biological and physiological basis of psychiatric, neurodevelopmental, age-related, and neurodegenerative disorders. The microbiota and the brain communicate with each other via various routes including the immune system, tryptophan metabolism, the vagus nerve and the enteric nervous system, involving microbial metabolites such as short-chain fatty acids, branched chain amino acids, and peptidoglycans. Many factors can influence microbiota composition in early life, including infection, mode of birth delivery, use of antibiotic medications, the nature of nutritional provision, environmental stressors, and host genetics. At the other extreme of life, microbial diversity diminishes with aging. Stress, in particular, can significantly impact the microbiota-gut-brain axis at all stages of life. Much recent work has implicated the gut microbiota in many conditions including autism, anxiety, obesity, schizophrenia, Parkinson’s disease, and Alzheimer’s disease. Animal models have been paramount in linking the regulation of fundamental neural processes, such as neurogenesis and myelination, to microbiome activation of microglia. Moreover, translational human studies are ongoing and will greatly enhance the field. Future studies will focus on understanding the mechanisms underlying the microbiota-gut-brain axis and attempt to elucidate microbial-based intervention and therapeutic strategies for neuropsychiatric disorders.
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              A fast diffeomorphic image registration algorithm.

              This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.

                Author and article information

                Contributors
                13580380071@163.com
                kaiwu@scut.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 May 2021
                7 May 2021
                2021
                : 11
                : 9743
                Affiliations
                [1 ]Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006 China
                [2 ]The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, 510370 China
                [3 ]Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, 510370 China
                [4 ]Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500 China
                [5 ]National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, 510006 China
                [6 ]Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, 510006 China
                [7 ]National Engineering Research Center for Healthcare Devices, Guangzhou, 510500 China
                [8 ]Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ USA
                [9 ]Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, 511400 China
                [10 ]Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, 980-8575 Japan
                Article
                89166
                10.1038/s41598-021-89166-8
                8105323
                33963227
                32f1059d-ca56-485d-be83-9ef6cb6e3460
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 January 2021
                : 20 April 2021
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award ID: 31771074, 81802230
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100012166, National Key Research and Development Program of China;
                Award ID: 2020YFC2004300, 2020YFC2004301, 2019YFC0118800, 2019YFC0118802, 2019YFC0118804, 2019YFC0118805
                Award ID: 2020YFC2004300, 2020YFC2004301, 2019YFC0118800, 2019YFC0118802, 2019YFC0118804, 2019YFC0118805
                Award Recipient :
                Funded by: The scientific research project of traditional Chinese medicine of Guangdong
                Award ID: 20211306
                Award ID: 20211306
                Award ID: 20211306
                Award Recipient :
                Funded by: Science and Technology Program of Guangzhou
                Award ID: 201704020168, 201704020113, 201807010064, 201803010100, 201903010032
                Award ID: 201704020168, 201704020113, 201807010064, 201803010100, 201903010032
                Award Recipient :
                Funded by: Guangdong Key Project in “Development of new tools for diagnosis and treatment of Autism”
                Award ID: 2018B030335001
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                clinical microbiology,neuroscience
                Uncategorized
                clinical microbiology, neuroscience

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