The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks
暂无分享,去创建一个
[1] V. Calhoun,et al. Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder. , 2021, Alcoholism, clinical and experimental research.
[2] M. Phillips,et al. PFC neuromodulation with theta burst stimulation to impact behavior and neural network activity in schizophrenia and bipolar disorder , 2021, Neuropsychopharmacology.
[3] Jeffrey D. Voigt,et al. Theta burst stimulation for the acute treatment of major depressive disorder: A systematic review and meta-analysis , 2021, Translational Psychiatry.
[4] Bradley E. Belsher,et al. Advances in repetitive transcranial magnetic stimulation for posttraumatic stress disorder: A systematic review. , 2021, Journal of psychiatric research.
[5] D. Tranel,et al. Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs , 2021, Proceedings of the National Academy of Sciences.
[6] P. Fitzgerald. Targeting repetitive transcranial magnetic stimulation in depression: do we really know what we are stimulating and how best to do it? , 2021, Brain Stimulation.
[7] J. Sweeney,et al. Network-level functional topological changes after mindfulness-based cognitive therapy in mood dysregulated adolescents at familial risk for bipolar disorder: a pilot study , 2021, BMC Psychiatry.
[8] Ruiwang Huang,et al. Efficacy and acceptability of transcranial direct current stimulation for treating depression: A meta-analysis of randomized controlled trials , 2021, Neuroscience & Biobehavioral Reviews.
[9] Evan M. Gordon,et al. Cingulo-opercular control network and disused motor circuits joined in standby mode , 2021, Proceedings of the National Academy of Sciences.
[10] Lingjiang Li,et al. Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder. , 2021, Journal of affective disorders.
[11] E. Rawls,et al. An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis , 2020, bioRxiv.
[12] Klaas E. Stephan,et al. Regression dynamic causal modeling for resting‐state fMRI , 2020, bioRxiv.
[13] Danielle S. Bassett,et al. Multimodal network dynamics underpinning working memory , 2020, Nature Communications.
[14] G. Venkatasubramanian,et al. Effect of prefrontal tDCS on resting brain fMRI graph measures in Alcohol Use Disorders: A randomized, double-blind, sham-controlled study. , 2020, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[15] Yanpei Wang,et al. Altered resting functional network topology assessed using graph theory in youth with attention-deficit/hyperactivity disorder , 2020, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[16] J. Goñi,et al. Functional network connectivity in early-stage schizophrenia , 2020, Schizophrenia Research.
[17] Kimberly L. Ray,et al. Dynamic reorganization of the frontal parietal network during cognitive control and episodic memory , 2019, Cognitive, Affective, & Behavioral Neuroscience.
[18] Danielle S. Bassett,et al. Flexible Coordinator and Switcher Hubs for Adaptive Task Control , 2019, The Journal of Neuroscience.
[19] Hong Li,et al. Rich-Club Analysis in Adults With ADHD Connectomes Reveals an Abnormal Structural Core Network , 2019, Journal of attention disorders.
[20] Daniele Marinazzo,et al. Advancing functional connectivity research from association to causation , 2019, Nature Neuroscience.
[21] Cameron S Carter,et al. Dynamic reorganization of the frontal parietal network during cognitive control and episodic memory , 2019, Cognitive, Affective, & Behavioral Neuroscience.
[22] A. Zilverstand,et al. Effects of single-session versus multi-session non-invasive brain stimulation on craving and consumption in individuals with drug addiction, eating disorders or obesity: A meta-analysis , 2019, Brain Stimulation.
[23] Michael W. Cole,et al. Mapping the human brain's cortical-subcortical functional network organization , 2018, NeuroImage.
[24] Matthew F. Glasser,et al. A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex , 2019, bioRxiv.
[25] Clark Glymour,et al. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods , 2019, Network Neuroscience.
[26] Ying Lin,et al. Intrinsic overlapping modular organization of human brain functional networks revealed by a multiobjective evolutionary algorithm , 2018, NeuroImage.
[27] Evan M. Gordon,et al. Reward-related regions form a preferentially coupled system at rest , 2018, bioRxiv.
[28] Vince D. Calhoun,et al. Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder , 2018, Front. Psychiatry.
[29] A. Fornito,et al. The development of brain network hubs , 2018, Developmental Cognitive Neuroscience.
[30] Ben D. Fulcher,et al. Consistency and differences between centrality measures across distinct classes of networks , 2018, PloS one.
[31] Mapping the human , 2018, Nature Methods.
[32] Danielle S. Bassett,et al. A mechanistic model of connector hubs, modularity and cognition , 2018, Nature Human Behaviour.
[33] S. Petersen,et al. Control networks and hubs. , 2018, Psychophysiology.
[34] Jasmine Hect,et al. Hubs in the human fetal brain network , 2018, Developmental Cognitive Neuroscience.
[35] Nicole M. Long,et al. Bottom-Up and Top-Down Factors Differentially Influence Stimulus Representations Across Large-Scale Attentional Networks , 2018, The Journal of Neuroscience.
[36] David Badre,et al. Frontal Cortex and the Hierarchical Control of Behavior , 2018, Trends in Cognitive Sciences.
[37] G Deco,et al. Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data , 2017, bioRxiv.
[38] Frederick Eberhardt,et al. Causal mapping of emotion networks in the human brain: Framework and initial findings , 2017, Neuropsychologia.
[39] Joachim M. Buhmann,et al. Regression DCM for fMRI , 2017, NeuroImage.
[40] Y Wang,et al. Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder , 2017, Translational Psychiatry.
[41] Rita Z. Goldstein,et al. Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review , 2017, NeuroImage.
[42] Clark Glymour,et al. A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images , 2016, International Journal of Data Science and Analytics.
[43] Kaustubh R. Kulkarni,et al. Cognitive task information is transferred between brain regions via resting-state network topology , 2017, bioRxiv.
[44] C. Glymour,et al. A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images , 2016, International Journal of Data Science and Analytics.
[45] Steen Moeller,et al. The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.
[46] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[47] Daniel J Mitchell,et al. Task Encoding across the Multiple Demand Cortex Is Consistent with a Frontoparietal and Cingulo-Opercular Dual Networks Distinction , 2016, The Journal of Neuroscience.
[48] K. Hwang,et al. The Contribution of Network Organization and Integration to the Development of Cognitive Control , 2015, PLoS biology.
[49] Seyed Shahriar Arab,et al. CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis , 2015, PloS one.
[50] Dustin Scheinost,et al. The (in)stability of functional brain network measures across thresholds , 2015, NeuroImage.
[51] E. Bullmore,et al. Wiring cost and topological participation of the mouse brain connectome , 2015, Proceedings of the National Academy of Sciences.
[52] E. Bullmore,et al. Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia , 2015, Proceedings of the National Academy of Sciences.
[53] H. Lei,et al. Altered brain functional networks in heavy smokers , 2015, Addiction biology.
[54] Luke J. Hearne,et al. Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity , 2015, Human brain mapping.
[55] Richard F. Betzel,et al. Cooperative and Competitive Spreading Dynamics on the Human Connectome , 2015, Neuron.
[56] Mark Jenkinson,et al. MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.
[57] Steen Moeller,et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.
[58] Adeel Razi,et al. A DCM for resting state fMRI , 2014, NeuroImage.
[59] Jean M. Vettel,et al. Controllability of structural brain networks , 2014, Nature Communications.
[60] E. Bullmore,et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders , 2014, Brain : a journal of neurology.
[61] Ludovica Griffanti,et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.
[62] Gereon R. Fink,et al. Dorsal and Ventral Attention Systems , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[63] Anil K. Seth,et al. The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference , 2014, Journal of Neuroscience Methods.
[64] Samuel D. Carpenter,et al. Structural and Functional Rich Club Organization of the Brain in Children and Adults , 2014, PloS one.
[65] Junming Shao,et al. Aberrant topology of striatum's connectivity is associated with the number of episodes in depression. , 2014, Brain : a journal of neurology.
[66] Joseph Ramsey,et al. Bayesian networks for fMRI: A primer , 2014, NeuroImage.
[67] Peter J Hellyer,et al. The Control of Global Brain Dynamics: Opposing Actions of Frontoparietal Control and Default Mode Networks on Attention , 2014, The Journal of Neuroscience.
[68] Clark Glymour,et al. Non-Gaussian methods and high-pass filters in the estimation of effective connections , 2014, NeuroImage.
[69] Olaf Sporns,et al. Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks , 2014, PLoS Comput. Biol..
[70] O. Sporns,et al. Network hubs in the human brain , 2013, Trends in Cognitive Sciences.
[71] Mark W. Woolrich,et al. Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.
[72] Olaf Sporns,et al. The human connectome: Origins and challenges , 2013, NeuroImage.
[73] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[74] Abraham Z. Snyder,et al. Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.
[75] Steen Moeller,et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.
[76] Abraham Z. Snyder,et al. Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.
[77] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[78] Jonathan D. Power,et al. Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.
[79] Jonathan D. Power,et al. Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.
[80] Cedric E. Ginestet,et al. Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.
[81] Bryon A Mueller,et al. Global functional connectivity abnormalities in children with fetal alcohol spectrum disorders. , 2013, Alcoholism, clinical and experimental research.
[82] Aapo Hyvärinen,et al. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models , 2013, J. Mach. Learn. Res..
[83] Kathleen M. Gates,et al. Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples , 2012, NeuroImage.
[84] O. Sporns. Discovering the Human Connectome , 2012 .
[85] Stephen M. Smith,et al. The future of FMRI connectivity , 2012, NeuroImage.
[86] O. Sporns,et al. Network centrality in the human functional connectome. , 2012, Cerebral cortex.
[87] O. Sporns,et al. High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.
[88] O. Sporns,et al. Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.
[89] Clark Glymour,et al. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study , 2011, NeuroImage.
[90] Karl J. Friston,et al. Generalised filtering and stochastic DCM for fMRI , 2011, NeuroImage.
[91] Nora D. Volkow,et al. Functional connectivity hubs in the human brain , 2011, NeuroImage.
[92] Danielle S Bassett,et al. Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.
[93] Mark W. Woolrich,et al. Network modelling methods for FMRI , 2011, NeuroImage.
[94] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[95] J. Duncan. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour , 2010, Trends in Cognitive Sciences.
[96] Walter Schneider,et al. Identifying the brain's most globally connected regions , 2010, NeuroImage.
[97] Russell A. Poldrack,et al. Six problems for causal inference from fMRI , 2010, NeuroImage.
[98] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[99] Keith A. Johnson,et al. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.
[100] S. Petersen,et al. A dual-networks architecture of top-down control , 2008, Trends in Cognitive Sciences.
[101] Bharat B. Biswal,et al. Competition between functional brain networks mediates behavioral variability , 2008, NeuroImage.
[102] O. Sporns,et al. Identification and Classification of Hubs in Brain Networks , 2007, PloS one.
[103] S. Petersen,et al. Development of distinct control networks through segregation and integration , 2007, Proceedings of the National Academy of Sciences.
[104] Edward T. Bullmore,et al. Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..
[105] Danielle Smith Bassett,et al. Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[106] Kristina M. Visscher,et al. A Core System for the Implementation of Task Sets , 2006, Neuron.
[107] E. Bullmore,et al. A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.
[108] R. Guimerà,et al. Functional cartography of complex metabolic networks , 2005, Nature.
[109] G. Cecchi,et al. Scale-free brain functional networks. , 2003, Physical review letters.
[110] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[111] David Maxwell Chickering,et al. Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..
[112] M. Corbetta,et al. Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.
[113] V. Latora,et al. Efficient behavior of small-world networks. , 2001, Physical review letters.
[114] Anthony Randal McIntosh,et al. Towards a network theory of cognition , 2000, Neural Networks.
[115] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[116] S. Bressler. Large-scale cortical networks and cognition , 1995, Brain Research Reviews.
[117] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[118] Leonard M. Freeman,et al. A set of measures of centrality based upon betweenness , 1977 .
[119] C. Granger. Investigating causal relations by econometric models and cross-spectral methods , 1969 .
[120] Scott Marek,et al. Control networks of the frontal lobes. , 2019, Handbook of clinical neurology.
[121] Karl J. Friston,et al. Year : 2011 Generalised filtering and stochastic DCM for fMRI , 2017 .
[122] Rita Z. Goldstein,et al. Cognitive interventions for addiction medicine: Understanding the underlying neurobiological mechanisms. , 2016, Progress in brain research.
[123] G. Fink,et al. Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles , 2013 .
[124] Karl J. Friston,et al. PHRENOLOGY : What Can Neuroimaging Tell Us About Distributed Circuitry ? , 2005 .
[125] P. Spirtes,et al. Causation, Prediction, and Search, 2nd Edition , 2001 .
[126] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[127] P. Erdos,et al. On the evolution of random graphs , 1984 .