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      Resting-state functional connectivity in an auditory network differs between aspiring professional and amateur musicians and correlates with performance

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          Abstract

          Auditory experience-dependent plasticity is often studied in the domain of musical expertise. Available evidence suggests that years of musical practice are associated with structural and functional changes in auditory cortex and related brain regions. Resting-state functional magnetic resonance imaging (MRI) can be used to investigate neural correlates of musical training and expertise beyond specific task influences. Here, we compared two groups of musicians with varying expertise: 24 aspiring professional musicians preparing for their entrance exam at Universities of Arts versus 17 amateur musicians without any such aspirations but who also performed music on a regular basis. We used an interval recognition task to define task-relevant brain regions and computed functional connectivity and graph-theoretical measures in this network on separately acquired resting-state data. Aspiring professionals performed significantly better on all behavioral indicators including interval recognition and also showed significantly greater network strength and global efficiency than amateur musicians. Critically, both average network strength and global efficiency were correlated with interval recognition task performance assessed in the scanner, and with an additional measure of interval identification ability. These findings demonstrate that task-informed resting-state fMRI can capture connectivity differences that correspond to expertise-related differences in behavior.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00429-023-02711-1.

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          Most cited references101

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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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            FMRIPrep: a robust preprocessing pipeline for functional MRI

            Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.
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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.

                Author and article information

                Contributors
                papadaki@mpib-berlin.mpg.de
                Journal
                Brain Struct Funct
                Brain Struct Funct
                Brain Structure & Function
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1863-2653
                1863-2661
                4 October 2023
                4 October 2023
                2023
                : 228
                : 9
                : 2147-2163
                Affiliations
                [1 ]Center for Lifespan Psychology, Max Planck Institute for Human Development, ( https://ror.org/02pp7px91) Lentzeallee 94, 14195 Berlin, Germany
                [2 ]GRID grid.4372.2, ISNI 0000 0001 2105 1091, International Max Planck Research School on the Life Course (LIFE), ; Berlin, Germany
                [3 ]GRID grid.83440.3b, ISNI 0000000121901201, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, ; London, UK
                [4 ]Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, ( https://ror.org/02pp7px91) Berlin, Germany
                [5 ]Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, ( https://ror.org/01zgy1s35) Hamburg, Germany
                Article
                2711
                10.1007/s00429-023-02711-1
                10587189
                37792073
                57f0f734-9efe-4a07-bde1-bca691c3e6c1
                © 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 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
                : 17 January 2023
                : 10 September 2023
                Funding
                Funded by: Max Planck Institute for Human Development (2)
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Neurology
                resting-state fmri,auditory plasticity,musicians,graph theoretical measures,interval identification

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