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      Characterization of the Brain Functional Architecture of Psychostimulant Withdrawal Using Single-Cell Whole-Brain Imaging

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          Abstract

          Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.

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          Complex network measures of brain connectivity: uses and interpretations.

          Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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            A mesoscale connectome of the mouse brain.

            Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
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              Neurobiology of addiction: a neurocircuitry analysis.

              Drug addiction represents a dramatic dysregulation of motivational circuits that is caused by a combination of exaggerated incentive salience and habit formation, reward deficits and stress surfeits, and compromised executive function in three stages. The rewarding effects of drugs of abuse, development of incentive salience, and development of drug-seeking habits in the binge/intoxication stage involve changes in dopamine and opioid peptides in the basal ganglia. The increases in negative emotional states and dysphoric and stress-like responses in the withdrawal/negative affect stage involve decreases in the function of the dopamine component of the reward system and recruitment of brain stress neurotransmitters, such as corticotropin-releasing factor and dynorphin, in the neurocircuitry of the extended amygdala. The craving and deficits in executive function in the so-called preoccupation/anticipation stage involve the dysregulation of key afferent projections from the prefrontal cortex and insula, including glutamate, to the basal ganglia and extended amygdala. Molecular genetic studies have identified transduction and transcription factors that act in neurocircuitry associated with the development and maintenance of addiction that might mediate initial vulnerability, maintenance, and relapse associated with addiction.
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                Author and article information

                Journal
                eNeuro
                eNeuro
                eneuro
                eNeuro
                eNeuro
                Society for Neuroscience
                2373-2822
                17 September 2021
                2 November 2021
                Nov-Dec 2021
                : 8
                : 6
                : ENEURO.0208-19.2021
                Affiliations
                [1 ]School of Medicine, Department of Psychiatry, University of California San Diego , La Jolla, CA 92093
                [2 ]College of Veterinary Medicine, Department of Basic Medical Sciences, Purdue University , West Lafayette, IN 47907
                [3 ]Department of Neuroscience, The Scripps Research Institute , La Jolla, CA 92037
                [4 ]Beckman Institute, Cal-Tech , Pasadena, CA 91125
                Author notes

                The authors declare no competing financial interests.

                Author contributions: A.K. and O.G. designed research; A.K., M.K., L.C.S., and A.C. performed research; A.K., L.C.S., and S.S. analyzed data; A.K. and O.G. wrote the paper.

                This work was supported by National Institutes of Health Grants DA044451, DA043799, DA047113, AA006420, AA020608, AA022977, AA027301, and AA007456; the Tobacco-Related Disease Research Program Grant 27IR-0047; Tobacco-Related Disease Research Program (grant no. T31KT1859); the 2021 Psychiatry Department Chair’s Excellence Fund to M.K.; the Pearson Center for Alcoholism and Addiction Research and the Preclinical Addiction Research Consortium at UCSD.

                Correspondence should be addressed to Olivier George at olgeorge@ 123456ucsd.edu .
                Author information
                https://orcid.org/0000-0001-9434-4987
                https://orcid.org/0000-0002-3700-5003
                Article
                eN-NWR-0208-19
                10.1523/ENEURO.0208-19.2021
                8570684
                34580158
                2c042dc0-6b73-478f-aa99-5b53780a5120
                Copyright © 2021 Kimbrough et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 31 May 2019
                : 8 August 2021
                : 9 August 2021
                Page count
                Figures: 8, Tables: 6, Equations: 13, References: 88, Pages: 34, Words: 00
                Funding
                Funded by: HHS | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA), doi 10.13039/100000027;
                Award ID: AA007456
                Award ID: AA027301
                Award ID: AA022977
                Award ID: AA020608
                Award ID: AA006420
                Funded by: HHS | NIH | National Institute on Drug Abuse (NIDA), doi 10.13039/100000026;
                Award ID: DA047113
                Award ID: DA043799
                Award ID: DA044451
                Funded by: Tobacco-Related Disease Research Program (TRDRP), doi 10.13039/100005188;
                Award ID: 27IR-0047
                Categories
                6
                Research Article: New Research
                Neuronal Excitability
                Custom metadata
                November/December 2021

                addiction,functional connectivity,graph theory,idisco,neural activity,withdrawal

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