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      Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

      research-article
      1 , 2 , 3 , 4 , 1 , 3 , 5 , 1 , 5 , 6 , 7 , 8 ,
      iScience
      Elsevier
      Neuroscience, Sensory neuroscience

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Neuronal oscillations are ubiquitous in brain activity at all scales and their synchronization dynamics are essential for information processing in neuronal systems. The underlying synaptic mechanisms, while mainly based on GABA- and glutamatergic neurotransmission, are influenced by neuromodulatory systems that have highly variable densities of neurotransmitter receptors and transporters across the cortical mantle. How they constrain the network structures of interacting oscillations has remained a central unaddressed question. We asked here whether the receptor and transporter densities covary with the frequency-specific neuroanatomical patterns of inter-areal phase synchrony (PS) and amplitude correlation (AC) networks in resting-state magnetoencephalography (MEG) data. Network centrality in delta and gamma frequencies covaried positively with GABA-, NMDA-, dopaminergic-, and most serotonergic receptor and transporter densities while covariance was negative in alpha and beta bands. These results show that local receptor microarchitecture shapes macro-scale oscillation networks in spectrally specific patterns.

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          Highlights

          • Neurotransmitter receptor and transporter (NT-R/T) densities vary across the human cortex

          • Node centrality indexes hubness of brain regions in MEG phase and amplitude coupling networks

          • NT-R/T densities covary with node centrality in frequency- and region-specific patterns

          • Evidence that local receptor microarchitecture shapes macroscale networks

          Abstract

          Neuroscience; Sensory neuroscience

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          Most cited references93

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          Fast unfolding of communities in large networks

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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            Complex brain networks: graph theoretical analysis of structural and functional systems.

            Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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              Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.

              A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                09 October 2024
                15 November 2024
                09 October 2024
                : 27
                : 11
                : 111111
                Affiliations
                [1 ]Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
                [2 ]BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University, Helsinki, Finland
                [3 ]Department of Neuroscience and Bioengineering (NBE), Aalto University, Espoo, Finland
                [4 ]Department of Electrical Engineering and Information Technology, Technical University Darmstadt, Darmstadt, Germany
                [5 ]Centre for Cognitive Neuroimaging (CCNi), School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
                [6 ]Division of Psychology, VISE, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
                Author notes
                []Corresponding author satu.palva@ 123456helsinki.fi
                [7]

                Senior author

                [8]

                Lead contact

                Article
                S2589-0042(24)02336-8 111111
                10.1016/j.isci.2024.111111
                11544385
                1cb661c7-1c05-4ca9-9251-596aa6db5f40
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 February 2024
                : 6 July 2024
                : 2 October 2024
                Categories
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                neuroscience,sensory neuroscience
                neuroscience, sensory neuroscience

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