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      Liraglutide restores impaired associative learning in individuals with obesity

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          Abstract

          Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1). In a single-blinded, randomized, controlled, crossover basic human functional magnetic resonance imaging study relying on a computational model of the adaptive learning process underlying behavioural responses, we show that adaptive learning is reduced when metabolic sensing is impaired in obesity, as indexed by reduced insulin sensitivity (participants: N = 30 with normal insulin sensitivity; N = 24 with impaired insulin sensitivity). Treatment with the GLP-1 receptor agonist liraglutide normalizes impaired learning of sensory associations in men and women with obesity. Collectively, our findings reveal that GLP-1 receptor activation modulates associative learning in people with obesity via its central effects within the mesoaccumbens pathway. These findings provide evidence for how metabolic signals can act as neuromodulators to adapt our behaviour to our body’s internal state and how GLP-1 receptor agonists work in clinics.

          Abstract

          Hanssen et al. demonstrate that associative learning is reduced when metabolic sensing is impaired in obesity and can be restored by liraglutide.

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          Most cited references98

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            Fast robust automated brain extraction.

            An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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              Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

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                Author and article information

                Contributors
                tittgemeyer@sf.mpg.de
                Journal
                Nat Metab
                Nat Metab
                Nature Metabolism
                Nature Publishing Group UK (London )
                2522-5812
                17 August 2023
                17 August 2023
                2023
                : 5
                : 8
                : 1352-1363
                Affiliations
                [1 ]GRID grid.418034.a, ISNI 0000 0004 4911 0702, Max Planck Institute for Metabolism Research, ; Cologne, Germany
                [2 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Faculty of Medicine and University Hospital Cologne, Policlinic for Endocrinology, Diabetology and Preventive Medicine (PEPD), , University of Cologne, ; Cologne, Germany
                [3 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Translational Neuromodeling Unit, Institute for Biomedical Engineering, , University of Zurich and Swiss Federal Institute of Technology, ; Zurich, Switzerland
                [4 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Faculty of Medicine and University Hospital Cologne, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD) and Excellence Center for Medical Mycology (ECMM), , University of Cologne, ; Cologne, Germany
                [5 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, , University of Cologne, ; Cologne, Germany
                [6 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), , University of Cologne, ; Cologne, Germany
                [7 ]GRID grid.411088.4, ISNI 0000 0004 0578 8220, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, , University Hospital Frankfurt, ; Frankfurt am Main, Germany
                [8 ]GRID grid.452463.2, German Centre for Infection Research (DZIF), , Partner Site Bonn-Cologne, ; Cologne, Germany
                [9 ]GRID grid.6190.e, ISNI 0000 0000 8580 3777, Faculty of Medicine and University Hospital Cologne, Clinical Trials Centre Cologne (ZKS Köln), , University of Cologne, ; Cologne, Germany
                Author information
                http://orcid.org/0000-0003-3761-8931
                http://orcid.org/0000-0002-1778-7239
                http://orcid.org/0000-0002-6323-1928
                http://orcid.org/0000-0003-1124-4310
                http://orcid.org/0000-0001-9599-3137
                http://orcid.org/0000-0002-6619-0092
                http://orcid.org/0000-0001-5072-2149
                Article
                859
                10.1038/s42255-023-00859-y
                10447249
                37592007
                e6b7642e-4179-469d-abeb-4c9ed6af1bdb
                © The Author(s) 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 August 2022
                : 7 July 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 390661388
                Award ID: 431549029
                Award ID: 431549029
                Award ID: 390661388
                Award ID: 390661388
                Award Recipient :
                Funded by: Deutsches Zentrum für Diabetesforschungs, 82DZD00502 Deutsches Zentrum für Diabetesforschungs, 82DZD03C2G
                Funded by: Deutsches Zentrum für Diabetesforschung, 82DZD00502
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                Custom metadata
                © Springer Nature Limited 2023

                metabolism,learning algorithms,endocrine system and metabolic diseases

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