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      Brain Glutamate Dynamics Predict Positive Agency in Healthy Women

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          Abstract

          Contributions of brain glutamate to conscious emotion are not well understood. Here we evaluate the relationship of experimentally-induced change in neocortical glutamate (ΔGlu) and subjective states in well individuals. Drug challenge with d-amphetamine (AMP; 20 mg oral), methamphetamine (MA; Desoxyn ®, 20 mg oral), and placebo (PBO) was conducted on three separate test days in a within-subjects double blind design. Proton magnetic resonance spectroscopy (MRS) quantified neurometabolites in the right dorsal anterior cingulate cortex (dACC) 140–150 m post-drug and PBO. Subjective states were assessed at half hour intervals over 5.5-hours on each session, yielding 3,792 responses per participant (91,008 responses overall, N=24 participants). Self-reports were reduced by principal components analysis to a single factor score of AMP- and MA-induced Positive Agency (ΔPA) in each participant. We found drug-induced ΔGlu related positively with ΔPA (ΔGluMA r=+.44, p<.05, N=21), with large effects in females (ΔGluMA r=+.52, p<.05; ΔGluAMP r=+.61, p<.05, N=11). States related to ΔGlu in females included rise in subjective stimulation, vigor, friendliness, elation, positive mood, positive affect ( r’s=+.51 to +.74, p<.05), and alleviation of anxiety ( r=−.61, p<.05, N=11). Self-reports correlated with DGlu to the extent they loaded on ΔPA ( r=.95 AMP, p=5×10 −10; r=.63 MA, p=.0015, N=11), indicating coherence of ΔGlu effects. Timing data indicated Glu shaped emotion both concurrently and prospectively, with no relationship to pre-MRS emotion (ΔGluAMP r=+.59 to +.65, p’s<.05; ΔGluMA r=+.53, p<.05, N=11). Together these findings indicate substantive, mechanistic contributions of neocortical Glu to positive agentic states in healthy individuals, most readily observed in women.

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          Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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            A power primer.

            One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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              Unified segmentation.

              A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
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                Author and article information

                Contributors
                Journal
                Res Sq
                ResearchSquare
                Research Square
                American Journal Experts
                15 June 2023
                : rs.3.rs-3021527
                Affiliations
                Brown University
                Brown University
                University of Calgary
                Author notes

                Author contributions

                TLW conceived the study idea and initiated, designed, and directed the study. TLW and MAG wrote the original and updated drafts of the manuscript. TLW and EGW collected the MRI data. TLW, ADH, and MAG conducted the data quality control procedures and performed the statistical and data analyses. TLW, MAG, and HEJ created the tables and figures. TLW, MAG, EGW, and ADH provided input on data analysis and interpretation of results. TLW, MAG, ADH, EGW, and HEJ revised the manuscript. All authors read and approved the final manuscript.

                Author information
                http://orcid.org/0000-0003-3395-0136
                http://orcid.org/0000-0003-0819-5124
                Article
                10.21203/rs.3.rs-3021527
                10.21203/rs.3.rs-3021527/v1
                10312947
                37398402
                6bdbbe20-49de-4cf9-b6f9-0f2fad2409a0

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                Funded by: National Institute of Health
                Award ID: DA029189
                Funded by: Hanlon Foundation
                Funded by: Zimmerman Fund for Scientific Innovation Awards in Brain Science
                Funded by: Carney Institute for Brain Science
                Funded by: COBRE Center for Central Nervous System Function NIH
                Award ID: P20 1P20GM130414-03
                Funded by: computational resources and services at the Center for Computation and Visualization, Brown University, NIH
                Award ID: S10 OD016366
                Funded by: scientific advisor and consultant to Strategic Aid Partners
                Award ID: 501c3
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