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      Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder

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          Abstract

          Psilocybin has shown promise for the treatment of mood disorders, which are often accompanied by cognitive dysfunction including cognitive rigidity. Recent studies have proposed neuropsychoplastogenic effects as mechanisms underlying the enduring therapeutic effects of psilocybin. In an open-label study of 24 patients with major depressive disorder, we tested the enduring effects of psilocybin therapy on cognitive flexibility (perseverative errors on a set-shifting task), neural flexibility (dynamics of functional connectivity or dFC via functional magnetic resonance imaging), and neurometabolite concentrations (via magnetic resonance spectroscopy) in brain regions supporting cognitive flexibility and implicated in acute psilocybin effects (e.g., the anterior cingulate cortex, or ACC). Psilocybin therapy increased cognitive flexibility for at least 4 weeks post-treatment, though these improvements were not correlated with the previously reported antidepressant effects. One week after psilocybin therapy, glutamate and N-acetylaspartate concentrations were decreased in the ACC, and dFC was increased between the ACC and the posterior cingulate cortex (PCC). Surprisingly, greater increases in dFC between the ACC and PCC were associated with less improvement in cognitive flexibility after psilocybin therapy. Connectome-based predictive modeling demonstrated that baseline dFC emanating from the ACC predicted improvements in cognitive flexibility. In these models, greater baseline dFC was associated with better baseline cognitive flexibility but less improvement in cognitive flexibility. These findings suggest a nuanced relationship between cognitive and neural flexibility. Whereas some enduring increases in neural dynamics may allow for shifting out of a maladaptively rigid state, larger persisting increases in neural dynamics may be of less benefit to psilocybin therapy.

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          Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

          Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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            A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

            A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
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              Functional network organization of the human brain.

              Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                mdoss3@jhmi.edu
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                8 November 2021
                8 November 2021
                2021
                : 11
                : 574
                Affiliations
                [1 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Psychiatry and Behavioral Sciences, , Johns Hopkins University School of Medicine, ; Baltimore, USA
                [2 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Center for Psychedelic & Consciousness Research, , Johns Hopkins University School of Medicine, ; Baltimore, USA
                [3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Radiology and Radiological Science, , Johns Hopkins University School of Medicine, ; Baltimore, USA
                [4 ]GRID grid.170205.1, ISNI 0000 0004 1936 7822, Department of Psychology, , University of Chicago, ; Chicago, USA
                [5 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, College of Social Work, , The Ohio State University, ; Columbus, USA
                [6 ]GRID grid.240023.7, ISNI 0000 0004 0427 667X, F.M. Kirby Research Center for Functional Brain Imaging, , Kennedy Krieger Institute, ; Baltimore, USA
                [7 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Neuroscience, , Johns Hopkins University School of Medicine, ; Baltimore, USA
                Author information
                http://orcid.org/0000-0003-2939-2522
                http://orcid.org/0000-0001-6179-4025
                http://orcid.org/0000-0003-0706-5440
                http://orcid.org/0000-0002-6410-7793
                http://orcid.org/0000-0001-5185-7854
                Article
                1706
                10.1038/s41398-021-01706-y
                8575795
                34750350
                abd6b2b0-8890-45a9-ae3f-aa213a04cfa9
                © 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 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
                : 7 June 2021
                : 14 October 2021
                : 26 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000026, U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA);
                Award ID: T32DA007209
                Award ID: R01DA03889
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA)
                Funded by: Heffter Research Institute, Steven and Alexandra Cohen Foundation, Tim Ferriss, Blake Mycoskie, Matt Mullenweg, Craig Nerenberg, the Riverstyx Foundation, and Dave Morin.
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Clinical Psychology & Psychiatry
                neuroscience,predictive markers
                Clinical Psychology & Psychiatry
                neuroscience, predictive markers

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