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      Functional network dynamics in a neurodevelopmental disorder of known genetic origin

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          Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9‐associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data‐driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting‐state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

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          Most cited references 52

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          A positive-negative mode of population covariation links brain connectivity, demographics and behavior

          We investigated the relationship between individual subjects’ functional connectomes and 280 behavioral and demographic measures, in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation; subjects were predominantly spread along a single “positive-negative” axis, linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
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            Temporally-independent functional modes of spontaneous brain activity.

            Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even "at rest," the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple "temporal functional modes," including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
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              Protein palmitoylation in neuronal development and synaptic plasticity.

              Protein palmitoylation, a classical and common lipid modification, regulates diverse aspects of neuronal protein trafficking and function. The reversible nature of palmitoylation provides a potential general mechanism for protein shuttling between intracellular compartments. The recent discovery of palmitoylating enzymes--a large DHHC (Asp-His-His-Cys) protein family--and the development of new proteomic and imaging methods have accelerated palmitoylation analysis. It is becoming clear that individual DHHC enzymes generate and maintain the specialized compartmentalization of substrates in polarized neurons. Here, we discuss the regulatory mechanisms for dynamic protein palmitoylation and the emerging roles of protein palmitoylation in various aspects of pathophysiology, including neuronal development and synaptic plasticity.

                Author and article information

                Hum Brain Mapp
                Hum Brain Mapp
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                22 October 2019
                1 February 2020
                : 41
                : 2 ( doiID: 10.1002/hbm.v41.2 )
                : 530-544
                [ 1 ] MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK
                [ 2 ] Oxford Centre for Human Brain Activity University of Oxford, University Department of Psychiatry, Warneford Hospital Oxford UK
                [ 3 ] Department of Medical Genetics University of Cambridge, Cambridge Institute for Medical Research Cambridge UK
                Author notes
                [* ] Correspondence

                Diandra Brkić, MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge, CB2 7EF, UK.

                Email: diandra.brkic@ 123456mrc-cbu.cam.ac.uk

                © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 4, Tables: 2, Pages: 15, Words: 12346
                Funded by: Wellcome Trust , open-funder-registry 10.13039/100010269;
                Funded by: MRC UK
                Award ID: MC‐A0606‐5PQ41
                Award ID: MC‐A0606‐5PQ40
                Funded by: National Institute of Health Research
                Research Article
                Research Articles
                Custom metadata
                February 1, 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.3 mode:remove_FC converted:03.06.2020


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