Human Connectomics across the Life Span

[1]  Richard C. Reynolds,et al.  An improved model of motion-related signal changes in fMRI , 2017, NeuroImage.

[2]  R. Cameron Craddock,et al.  Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels , 2016, Biological Psychiatry.

[3]  Yong He,et al.  Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood , 2016, Cerebral cortex.

[4]  John H. Gilmore,et al.  Functional Connectivity of the Infant Human Brain , 2016, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[5]  Moriah E. Thomason,et al.  Functional Connectivity of the Human Brain in Utero , 2016, Trends in Cognitive Sciences.

[6]  Timothy O. Laumann,et al.  Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project , 2016, Brain Connect..

[7]  O. Sporns,et al.  From regions to connections and networks: new bridges between brain and behavior , 2016, Current Opinion in Neurobiology.

[8]  Steen Moeller,et al.  The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.

[9]  Stine K. Krogsrud,et al.  Neurodevelopmental origins of lifespan changes in brain and cognition , 2016, Proceedings of the National Academy of Sciences.

[10]  Garth J Thompson,et al.  The Whole-Brain "Global" Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism , 2016, Brain Connect..

[11]  R. Adolphs,et al.  Building a Science of Individual Differences from fMRI , 2016, Trends in Cognitive Sciences.

[12]  Chandra Sripada,et al.  Growth Charting of Brain Connectivity Networks and the Identification of Attention Impairment in Youth. , 2016, JAMA psychiatry.

[13]  Keith A. Johnson,et al.  Multiple Brain Markers are Linked to Age-Related Variation in Cognition. , 2016, Cerebral cortex.

[14]  Yong He,et al.  Toward Developmental Connectomics of the Human Brain , 2016, Front. Neuroanat..

[15]  Mark Wronkiewicz,et al.  A simple generative model of the mouse mesoscale connectome , 2016, eLife.

[16]  Wei Zhang,et al.  Lifespan anxiety is reflected in human amygdala cortical connectivity , 2015, Human brain mapping.

[17]  Xi-Nian Zuo,et al.  Regional Homogeneity , 2015, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[18]  Olaf Sporns,et al.  Generative models of the human connectome , 2015, NeuroImage.

[19]  Evan M. Gordon,et al.  Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.

[20]  Kay Jann,et al.  Characterizing Resting-State Brain Function Using Arterial Spin Labeling , 2015, Brain Connect..

[21]  Stine K. Krogsrud,et al.  Development and aging of cortical thickness correspond to genetic organization patterns , 2015, Proceedings of the National Academy of Sciences.

[22]  Marcus Kaiser,et al.  A geometric network model of intrinsic grey-matter connectivity of the human brain , 2015, Scientific Reports.

[23]  S. Petersen,et al.  Brain Networks and Cognitive Architectures , 2015, Neuron.

[24]  X. Zuo,et al.  Neuroscience and Biobehavioral Reviews Putting Age-related Task Activation into Large-scale Brain Networks: a Meta-analysis of 114 Fmri Studies on Healthy Aging , 2022 .

[25]  A. Lansner,et al.  Neurocognitive Architecture of Working Memory , 2015, Neuron.

[26]  Maki S. Koyama,et al.  Intrinsic brain indices of verbal working memory capacity in children and adolescents , 2015, Developmental Cognitive Neuroscience.

[27]  Bruce R. Rosen,et al.  Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures , 2015, Scientific Data.

[28]  Alan C. Evans,et al.  Changes in thickness and surface area of the human cortex and their relationship with intelligence. , 2015, Cerebral cortex.

[29]  David Bartrés-Faz,et al.  Reorganization of brain networks in aging: a review of functional connectivity studies , 2015, Front. Psychol..

[30]  Alberto Llera,et al.  ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.

[31]  Maarten Mennes,et al.  Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI , 2015, NeuroImage.

[32]  Xi-Nian Zuo,et al.  Quantile rank maps: A new tool for understanding individual brain development , 2015, NeuroImage.

[33]  Gregor Thut,et al.  Neuroscience and Biobehavioral Reviews the Contribution of Tms–eeg Coregistration in the Exploration of the Human Cortical Connectome , 2022 .

[34]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[35]  Xi-Nian Zuo,et al.  A Connectome Computation System for discovery science of brain , 2015 .

[36]  E. Bullmore,et al.  Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[37]  P. Uhlhaas,et al.  Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data , 2013, Cerebral cortex.

[38]  Bing Chen,et al.  An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.

[39]  Mary E. Meyerand,et al.  Age-Related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks , 2014, Brain Connect..

[40]  Joaquín Goñi,et al.  Changes in structural and functional connectivity among resting-state networks across the human lifespan , 2014, NeuroImage.

[41]  Denise C. Park,et al.  Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.

[42]  U. Lindenberger.  Human cognitive aging: Corriger la fortune? , 2014, Science.

[43]  B. Wandell,et al.  Lifespan maturation and degeneration of human brain white matter , 2014, Nature Communications.

[44]  B. Leventhal,et al.  Unraveling the Miswired Connectome: A Developmental Perspective , 2014, Neuron.

[45]  X. Zuo,et al.  Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective , 2014, Neuroscience & Biobehavioral Reviews.

[46]  H. Duffau.  The huge plastic potential of adult brain and the role of connectomics: New insights provided by serial mappings in glioma surgery , 2014, Cortex.

[47]  H. Francis Song,et al.  A simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Linda B. Smith,et al.  Developmental process emerges from extended brain–body–behavior networks , 2014, Trends in Cognitive Sciences.

[49]  Thomas T. Liu,et al.  Developmental changes in resting and functional cerebral blood flow and their relationship to the BOLD response , 2014, Human brain mapping.

[50]  Christian Langkammer,et al.  Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan , 2014, Human brain mapping.

[51]  O. Sporns.  Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[52]  Xi-Nian Zuo,et al.  Tai Chi Chuan optimizes the functional organization of the intrinsic human brain architecture in older adults , 2014, Front. Aging Neurosci..

[53]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[54]  D. Hu,et al.  Neurobiological basis of head motion in brain imaging , 2014, Proceedings of the National Academy of Sciences.

[55]  Catie Chang,et al.  Connectivity trajectory across lifespan differentiates the precuneus from the default network , 2014, NeuroImage.

[56]  A. Mackey,et al.  Resting-State fMRI , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[57]  George W Rebok,et al.  Ten‐Year Effects of the Advanced Cognitive Training for Independent and Vital Elderly Cognitive Training Trial on Cognition and Everyday Functioning in Older Adults , 2014, Journal of the American Geriatrics Society.

[58]  Valerie A. I. Natale,et al.  Worldwide variation in human growth and the World Health Organization growth standards: a systematic review , 2014, BMJ Open.

[59]  Yong He,et al.  Topological organization of the human brain functional connectome across the lifespan , 2013, Developmental Cognitive Neuroscience.

[60]  Yong He,et al.  Assessment of system dysfunction in the brain through MRI-based connectomics , 2013, The Lancet Neurology.

[61]  D. Barch,et al.  The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. , 2013, JAMA pediatrics.

[62]  T. Vos,et al.  Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010 , 2013, The Lancet.

[63]  Moriah E. Thomason,et al.  Functional plasticity before the cradle: A review of neural functional imaging in the human fetus , 2013, Neuroscience & Biobehavioral Reviews.

[64]  Israel Liberzon,et al.  Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood , 2013, Proceedings of the National Academy of Sciences.

[65]  Kevin Murphy,et al.  Resting-state fMRI confounds and cleanup , 2013, NeuroImage.

[66]  Yufeng Zang,et al.  Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes , 2013, NeuroImage.

[67]  R. Cameron Craddock,et al.  Clinical applications of the functional connectome , 2013, NeuroImage.

[68]  Henry Kennedy,et al.  A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule , 2013, Neuron.

[69]  M. P. van den Heuvel,et al.  The Ontogeny of the Human Connectome , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[70]  L. Bherer,et al.  A Review of the Effects of Physical Activity and Exercise on Cognitive and Brain Functions in Older Adults , 2013, Journal of aging research.

[71]  Peter A. Robinson,et al.  Using Geometry to Uncover Relationships Between Isotropy, Homogeneity, and Modularity in Cortical Connectivity , 2013, Brain Connect..

[72]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[73]  Khader M. Hasan,et al.  Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach , 2013, NeuroImage.

[74]  Hang Joon Jo,et al.  The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..

[75]  N. Fox,et al.  Cognition assessment using the NIH Toolbox , 2013, Neurology.

[76]  Alvaro Pascual-Leone,et al.  Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity , 2013, NeuroImage.

[77]  J. Grutzendler,et al.  In Vivo Imaging of Cerebral Microvascular Plasticity from Birth to Death , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[78]  Swathi P. Iyer,et al.  Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..

[79]  Á. Pascual-Leone,et al.  Changes in plasticity across the lifespan: cause of disease and target for intervention. , 2013, Progress in brain research.

[80]  G. Bartzokis,et al.  Multimodal Magnetic Resonance Imaging Assessment of White Matter Aging Trajectories Over the Lifespan of Healthy Individuals , 2012, Biological Psychiatry.

[81]  Lars T. Westlye,et al.  Network-specific effects of age and in-scanner subject motion: A resting-state fMRI study of 238 healthy adults , 2012, NeuroImage.

[82]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[83]  Vijay K. Venkatraman,et al.  Neuroanatomical Assessment of Biological Maturity , 2012, Current Biology.

[84]  D. Hu,et al.  Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI , 2012, PloS one.

[85]  P. Hagmann,et al.  MR connectomics: a conceptual framework for studying the developing brain , 2012, Front. Syst. Neurosci..

[86]  Hang Joon Jo,et al.  Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression , 2012, Brain Connect..

[87]  J. Rapoport,et al.  Simple models of human brain functional networks , 2012, Proceedings of the National Academy of Sciences.

[88]  Efstathios D. Gennatas,et al.  Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome , 2012, Neuron.

[89]  B. Biswal,et al.  Characterizing variation in the functional connectome: promise and pitfalls , 2012, Trends in Cognitive Sciences.

[90]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[91]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[92]  N. Volkow,et al.  Aging and Functional Brain Networks , 2011, Molecular Psychiatry.

[93]  W. Thompson,et al.  Design considerations for characterizing psychiatric trajectories across the lifespan: application to effects of APOE-ε4 on cerebral cortical thickness in Alzheimer's disease. , 2011, The American journal of psychiatry.

[94]  Paul M. Thompson,et al.  Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan , 2011, NeuroImage.

[95]  C. Kelly,et al.  Differential Development of Human Brain White Matter Tracts , 2011, PloS one.

[96]  Xi-Nian Zuo,et al.  Resting-State Functional Connectivity Indexes Reading Competence in Children and Adults , 2011, The Journal of Neuroscience.

[97]  Yong He,et al.  Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI , 2011, NeuroImage.

[98]  Cleofé Peña-Gómez,et al.  Changes in Cortical Plasticity Across the Lifespan , 2011, Front. Ag. Neurosci..

[99]  Alan C. Evans,et al.  Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy , 2010, The Journal of Neuroscience.

[100]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[101]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[102]  A. Dale,et al.  Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. , 2010, Cerebral cortex.

[103]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[104]  E. McAuley,et al.  Frontiers in Aging Neuroscience Aging Neuroscience , 2022 .

[105]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[106]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[107]  B. Peterson,et al.  Normal Development of Brain Circuits , 2010, Neuropsychopharmacology.

[108]  Alan C. Evans,et al.  Age- and Gender-Related Differences in the Cortical Anatomical Network , 2009, The Journal of Neuroscience.

[109]  Liang Wang,et al.  Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study , 2009, Human brain mapping.

[110]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[111]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[112]  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.

[113]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[114]  S. Petersen,et al.  The maturing architecture of the brain's default network , 2008, Proceedings of the National Academy of Sciences.

[115]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[116]  S. Petersen,et al.  Development of distinct control networks through segregation and integration , 2007, Proceedings of the National Academy of Sciences.

[117]  Luciano da Fontoura Costa,et al.  Predicting the connectivity of primate cortical networks from topological and spatial node properties , 2007, BMC Systems Biology.

[118]  Lars Nyberg,et al.  Cognitive and neural plasticity in aging: General and task-specific limitations , 2006, Neuroscience & Biobehavioral Reviews.

[119]  Paige E. Scalf,et al.  Aerobic exercise training increases brain volume in aging humans. , 2006, The journals of gerontology. Series A, Biological sciences and medical sciences.

[120]  Alan C. Evans,et al.  Intellectual ability and cortical development in children and adolescents , 2006, Nature.

[121]  V. Menon,et al.  White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. , 2005, Cerebral cortex.

[122]  Paige E. Scalf,et al.  The implications of cortical recruitment and brain morphology for individual differences in inhibitory function in aging humans. , 2005, Psychology and aging.

[123]  Olga V. Demler,et al.  Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. , 2005, Archives of general psychiatry.

[124]  T. Hare,et al.  Changes in cerebral functional organization during cognitive development , 2005, Current Opinion in Neurobiology.

[125]  R. Buckner,et al.  Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD , 2005, Neurology.

[126]  J. Rothwell,et al.  Theta Burst Stimulation of the Human Motor Cortex , 2005, Neuron.

[127]  A. Toga,et al.  Mapping Changes in the Human Cortex throughout the Span of Life , 2004, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[128]  Paige E. Scalf,et al.  Cardiovascular fitness, cortical plasticity, and aging. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[129]  Suzanne E. Welcome,et al.  Mapping cortical change across the human life span , 2003, Nature Neuroscience.

[130]  Arthur F Kramer,et al.  Exercise, experience and the aging brain , 2002, Neurobiology of Aging.

[131]  R. Cabeza.  Hemispheric asymmetry reduction in older adults: the HAROLD model. , 2002, Psychology and aging.

[132]  Lars Kai Hansen,et al.  The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analysis Framework , 2000, NeuroImage.

[133]  J. Townsend,et al.  Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. , 2000, Radiology.

[134]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[135]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[136]  M. Bear,et al.  Synaptic plasticity: LTP and LTD , 1994, Current Opinion in Neurobiology.