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      Network connectivity and structural correlates of survival in progressive supranuclear palsy and corticobasal syndrome

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          Abstract

          There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson‐plus and the UK National PSP Research Network (PROSPECT‐MR). Resting‐state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large‐scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between‐network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients' demographics, structural imaging, and clinical scores using five‐fold cross‐validation. In PSP and CBS, between‐network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between‐network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics.

          Abstract

          We tested whether MRI‐based biomarkers improve the prediction of survival time in progressive supranuclear palsy and corticobasal syndrome in two complementary datasets. We found that between‐network connectivity predicts variability in survival and progression in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              Fitting Linear Mixed-Effects Models Using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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                Author and article information

                Contributors
                djw216@medschl.cam.ac.uk
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                03 June 2023
                1 August 2023
                : 44
                : 11 ( doiID: 10.1002/hbm.v44.11 )
                : 4239-4255
                Affiliations
                [ 1 ] Department of Clinical Neurosciences and Cambridge Centre for Parkinson‐plus University of Cambridge Cambridge UK
                [ 2 ] Cambridge University Hospitals NHS Foundation Trust Cambridge UK
                [ 3 ] Wessex Neurological Centre University Hospital Southampton Southampton UK
                [ 4 ] Norfolk and Norwich University Hospital Norwich UK
                [ 5 ] Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre University of Manchester Manchester UK
                [ 6 ] Departments of Geriatric Medicine and Nuclear Medicine University of Duisburg‐Essen Duisburg Germany
                [ 7 ] Oxford Parkinson's Disease Centre and Division of Neurology, Nuffield Department of Clinical Neurosciences University of Oxford Oxford UK
                [ 8 ] Department of Neuroscience Brighton and Sussex Medical School Brighton UK
                [ 9 ] Department of Neurology Royal Gwent Hospital Newport UK
                [ 10 ] Faculty of Medical Sciences Newcastle University Newcastle UK
                [ 11 ] Department of Clinical and Movement Neurosciences University College London, Queen Square Institute of Neurology London UK
                [ 12 ] MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK
                Author notes
                [*] [* ] Correspondence

                David J. Whiteside, Department of Clinical Neurosciences, Herchel Smith Building, Cambridge Biomedical Campus, Cambridge CB2 0SZ, United Kingdom,

                Email: djw216@ 123456medschl.cam.ac.uk

                Author information
                https://orcid.org/0000-0002-5890-9220
                https://orcid.org/0000-0002-8553-2801
                Article
                HBM26342
                10.1002/hbm.26342
                10318264
                37269181
                ca98d71d-a002-4332-878b-bc1627500033
                © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                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.

                History
                : 27 April 2023
                : 10 January 2023
                : 01 May 2023
                Page count
                Figures: 7, Tables: 1, Pages: 17, Words: 15139
                Funding
                Funded by: Alzheimer's Research Trust , doi 10.13039/501100000319;
                Award ID: ARUK‐RADF2021A‐010
                Funded by: Cambridge centre for Parkinson‐plus
                Award ID: RG95450
                Funded by: Evelyn Trust , doi 10.13039/501100004282;
                Award ID: 17/09
                Funded by: Medical Research Council , doi 10.13039/501100000265;
                Award ID: MC_UU_00030/14
                Award ID: MR/T033371/1
                Funded by: National Institute for Health and Care Research , doi 10.13039/501100000272;
                Award ID: ACF‐2018‐14‐016
                Award ID: NIHR203312
                Funded by: Progressive Supranuclear Palsy Association , doi 10.13039/100011707;
                Award ID: RG78738
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Award ID: 220258
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                August 1, 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:04.07.2023

                Neurology
                connectivity,corticobasal syndrome,fmri,prediction,progressive supranuclear palsy,survival,tauopathies

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