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      Altered single‐subject gray matter structural networks in drug‐naïve attention deficit hyperactivity disorder children

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          Abstract

          Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug‐naïve children with ADHD and 83 age‐matched healthy controls. Single‐subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala ( p < .05, FDR corrected). Alterations in GM network topologies were observed in drug‐naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

          Abstract

          Alterations in gray matter network topologies were observed in drug‐naïve attention deficit hyperactivity disorder (ADHD) patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

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          Most cited references51

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          Network-based statistic: identifying differences in brain networks.

          Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. Copyright © 2010 Elsevier Inc. All rights reserved.
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            The economy of brain network organization.

            The brain is expensive, incurring high material and metabolic costs for its size--relative to the size of the body--and many aspects of brain network organization can be mostly explained by a parsimonious drive to minimize these costs. However, brain networks or connectomes also have high topological efficiency, robustness, modularity and a 'rich club' of connector hubs. Many of these and other advantageous topological properties will probably entail a wiring-cost premium. We propose that brain organization is shaped by an economic trade-off between minimizing costs and allowing the emergence of adaptively valuable topological patterns of anatomical or functional connectivity between multiple neuronal populations. This process of negotiating, and re-negotiating, trade-offs between wiring cost and topological value continues over long (decades) and short (millisecond) timescales as brain networks evolve, grow and adapt to changing cognitive demands. An economical analysis of neuropsychiatric disorders highlights the vulnerability of the more costly elements of brain networks to pathological attack or abnormal development.
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              GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

              Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website. 1
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                Author and article information

                Contributors
                qiyonggong@hmrrc.org.cn
                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
                19 November 2021
                March 2022
                : 43
                : 4 ( doiID: 10.1002/hbm.v43.4 )
                : 1256-1264
                Affiliations
                [ 1 ] Huaxi MR Research Center (HMRRC), Department of Radiology West China Hospital of Sichuan University Chengdu China
                [ 2 ] Department of Psychiatry West China Hospital of Sichuan University Chengdu China
                [ 3 ] Department of Psychiatry and Behavioral Neuroscience University of Cincinnati Cincinnati Ohio USA
                [ 4 ] Center for Psychiatric Neuroscience Feinstein Institute for Medical Research Manhasset New York USA
                [ 5 ] Division of Psychiatry Research Zucker Hillside Hospital Glen Oaks New York USA
                [ 6 ] Department of Psychiatry University of Cincinnati Cincinnati Ohio USA
                [ 7 ] Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China
                [ 8 ] Functional and Molecular Imaging Key Laboratory of Sichuan Province Huaxi Xiamen Hospital of Sichuan University Xiamen Fujian China
                Author notes
                [*] [* ] Correspondence

                Qiyong Gong, Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

                Email: qiyonggong@ 123456hmrrc.org.cn

                Author information
                https://orcid.org/0000-0001-8686-5010
                https://orcid.org/0000-0002-5912-4871
                Article
                HBM25718
                10.1002/hbm.25718
                8837581
                34797010
                a8bafedd-1190-49f0-a3a9-e826dcb05bbc
                © 2021 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-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 09 October 2021
                : 18 June 2021
                : 04 November 2021
                Page count
                Figures: 5, Tables: 1, Pages: 9, Words: 6788
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81801358
                Award ID: 81621003
                Award ID: 81801683
                Award ID: 81761128023
                Award ID: 81820108018
                Award ID: 82027808
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                March 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.1 mode:remove_FC converted:11.02.2022

                Neurology
                attention deficit hyperactivity disorder,cognitive deficits,gray matter networks,psychoradiology,symptom severity

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