Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort

[1]  Tim C Kietzmann,et al.  The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan , 2021, eLife.

[2]  Dan J Stein,et al.  Brain charts for the human lifespan , 2021, Nature.

[3]  Yaojing Chen,et al.  Brain mechanisms underlying neuropsychiatric symptoms in Alzheimer’s disease: a systematic review of symptom-general and –specific lesion patterns , 2021, Molecular neurodegeneration.

[4]  Chaozhe Zhu,et al.  Transcranial brain atlas for school-aged children and adolescents , 2021, Brain Stimulation.

[5]  A. Conrad,et al.  Brain Developmental Trajectories in Children and Young Adults with Isolated Cleft Lip and/or Cleft Palate , 2021, Developmental neuropsychology.

[6]  A. Alexander,et al.  A 16-year study of longitudinal volumetric brain development in males with autism , 2021, NeuroImage.

[7]  S. Tapert,et al.  Neuroimaging markers of adolescent depression in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study. , 2021, Journal of affective disorders.

[8]  N. Shu,et al.  Early prevention of cognitive impairment in the community population: The Beijing Aging Brain Rejuvenation Initiative , 2021, Alzheimer's & dementia : the journal of the Alzheimer's Association.

[9]  G. Hancock,et al.  Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood , 2021, Developmental Cognitive Neuroscience.

[10]  J. Qiu,et al.  Mapping Domain- and Age-Specific Functional Brain Activity for Children’s Cognitive and Affective Development , 2021, Neuroscience Bulletin.

[11]  S. Tapert,et al.  Developing functional network connectivity of the dorsal anterior cingulate cortex mediates externalizing psychopathology in adolescents with child neglect , 2021, Developmental Cognitive Neuroscience.

[12]  Lei Ai,et al.  U-Net Model for Brain Extraction : Trained on Humans for Transfer to Non-1 human Primates 2 3 , 2021 .

[13]  Yong He,et al.  Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study , 2020, NeuroImage.

[14]  D. Margulies,et al.  Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence , 2020, Proceedings of the National Academy of Sciences of the United States of America.

[15]  A. L. Arenas,et al.  Fractionating autism based on neuroanatomical normative modeling , 2020, Translational Psychiatry.

[16]  A. Qiu.  Child brain growth standard: age and ethnicity dependent. , 2020, Science bulletin.

[17]  X. Zuo,et al.  Dream , 2020, Phillis.

[18]  J. Buizer-Voskamp,et al.  The YOUth study: Rationale, design, and study procedures , 2020, Developmental Cognitive Neuroscience.

[19]  R. Spencer,et al.  Habitual sleep is associated with both source memory and hippocampal subfield volume during early childhood , 2020, Scientific Reports.

[20]  G. Schumann,et al.  Population normative models of human brain growth across development. , 2020, Science bulletin.

[21]  R. Cox,et al.  A series of five population‐specific Indian brain templates and atlases spanning ages 6–60 years , 2020, Human brain mapping.

[22]  C. Weems,et al.  Developmental Variation in Amygdala Volumes: Modeling Differences Across Time, Age, and Puberty. , 2020, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[23]  Timothy O. Laumann,et al.  Towards Reproducible Brain-Wide Association Studies , 2020, bioRxiv.

[24]  Boris C. Bernhardt,et al.  Dispersion of functional gradients across the adult lifespan , 2020, NeuroImage.

[25]  M. Silvestrini,et al.  Reader response: An investigation of antihypertensive class, dementia, and cognitive decline: A meta-analysis , 2020 .

[26]  R. Cox,et al.  A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years , 2020 .

[27]  Michael P. Milham,et al.  Charting brain growth in tandem with brain templates at school age. , 2020, Science bulletin.

[28]  Joanne C. Beer,et al.  Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data☆ , 2020, NeuroImage.

[29]  M. Kaess,et al.  Neuropsychological development in adolescents: Longitudinal associations with white matter microstructure , 2020, Developmental Cognitive Neuroscience.

[30]  J. Qiu,et al.  OFC and its connectivity with amygdala as predictors for future social anxiety in adolescents , 2020, Developmental Cognitive Neuroscience.

[31]  Morgan A. Botdorf,et al.  Longitudinal development of hippocampal subregions from early‐ to mid‐childhood , 2020, Hippocampus.

[32]  T. Little,et al.  Underused Methods in Developmental Science to Inform Policy and Practice , 2020 .

[33]  Finnegan J. Calabro,et al.  Dopamine-related striatal neurophysiology is associated with specialization of frontostriatal reward circuitry through adolescence , 2020, bioRxiv.

[34]  Gareth J. Barker,et al.  The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India , 2020, Molecular Psychiatry.

[35]  M. Mallar Chakravarty,et al.  Creation of an Open Science Dataset from PREVENT-AD, a Longitudinal Cohort Study of Pre-symptomatic Alzheimer’s Disease , 2020, bioRxiv.

[36]  D. Minhas,et al.  Maturation of the human striatal dopamine system revealed by PET and quantitative MRI , 2020, Nature Communications.

[37]  Joanne C. Beer,et al.  An investigation of antihypertensive class, dementia, and cognitive decline: A meta-analysis. , 2019, Neurology.

[38]  Christos Davatzikos,et al.  Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data , 2019, NeuroImage.

[39]  R. Dixon,et al.  Age, cohort, and period effects on metamemory beliefs. , 2019, Psychology and aging.

[40]  K. Schaie,et al.  Cohort differences in cognitive aging: The role of perceived work environment. , 2019, Psychology and aging.

[41]  Eva H. Telzer,et al.  Longitudinal changes in amygdala, hippocampus and cortisol development following early caregiving adversity , 2019, Developmental Cognitive Neuroscience.

[42]  Finnegan J. Calabro,et al.  Development of Hippocampal-Prefrontal Cortex Interactions through Adolescence. , 2019, Cerebral cortex.

[43]  Kewei Chen,et al.  White Matter Microstructural Change Contributes to Worse Cognitive Function in Patients With Type 2 Diabetes , 2019, Diabetes.

[44]  K. Mills,et al.  Modeling Individual Differences in Brain Development , 2020, Biological Psychiatry.

[45]  M. Milham,et al.  Harnessing reliability for neuroscience research , 2019, Nature Human Behaviour.

[46]  C. Beckmann,et al.  Conceptualizing mental disorders as deviations from normative functioning , 2019, Molecular Psychiatry.

[47]  Lara M. Wierenga,et al.  A three‐wave longitudinal study of subcortical–cortical resting‐state connectivity in adolescence: Testing age‐ and puberty‐related changes , 2019, Human brain mapping.

[48]  V. Calhoun,et al.  Precision medicine and global mental health. , 2019, The Lancet. Global health.

[49]  Thomas E. Nichols,et al.  Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects , 2018, NeuroImage.

[50]  S. Bölte,et al.  Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models , 2018, bioRxiv.

[51]  Jun Zhang,et al.  Growth patterns from birth to 24 months in Chinese children: a birth cohorts study across China , 2018, BMC Pediatrics.

[52]  R. Ophoff,et al.  Structural brain alterations in youth with psychosis and bipolar spectrum symptoms , 2018, bioRxiv.

[53]  Anders M. Dale,et al.  The genetic architecture of the human cerebral cortex , 2020, Science.

[54]  P. Bjerregaard,et al.  Growth of children in Greenland exceeds the World Health Organization growth charts , 2018, Acta paediatrica.

[55]  C. Bergeman,et al.  Affective Experience Across the Adult Lifespan: An Accelerated Longitudinal Design , 2018, Psychology and aging.

[56]  H. Garavan,et al.  Recruiting the ABCD sample: Design considerations and procedures , 2018, Developmental Cognitive Neuroscience.

[57]  Xi-Nian Zuo,et al.  Developmental population neuroscience: emerging from ICHBD , 2018 .

[58]  Steven J. Schiff,et al.  Normative human brain volume growth. , 2018, Journal of neurosurgery. Pediatrics.

[59]  Klaus P. Ebmeier,et al.  Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”) , 2018, European Psychiatry.

[60]  B. J. Casey,et al.  Prediction complements explanation in understanding the developing brain , 2018, Nature Communications.

[61]  Venkateswararao Cherukuri,et al.  Endoscopic Treatment versus Shunting for Infant Hydrocephalus in Uganda , 2017, The New England journal of medicine.

[62]  B. Luna,et al.  The expression of established cognitive brain states stabilizes with working memory development , 2017, eLife.

[63]  Beatriz Luna,et al.  Protracted development of executive and mnemonic brain systems underlying working memory in adolescence: A longitudinal fMRI study , 2017, NeuroImage.

[64]  Xi-Nian Zuo,et al.  Chinese Color Nest Project: Growing up in China , 2017 .

[65]  Vincent Frouin,et al.  The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders , 2017, Molecular Autism.

[66]  Nathan T. Carter,et al.  Age, Time Period, and Birth Cohort Differences in Self-Esteem: Reexamining a Cohort-Sequential Longitudinal Study , 2017, Journal of personality and social psychology.

[67]  Richard F. Betzel,et al.  Human Connectomics across the Life Span , 2017, Trends in Cognitive Sciences.

[68]  Alan C. Evans,et al.  Early brain development in infants at high risk for autism spectrum disorder , 2017, Nature.

[69]  Nancy Y. Ip,et al.  China Brain Project: Basic Neuroscience, Brain Diseases, and Brain-Inspired Computing , 2016, Neuron.

[70]  H. Hart,et al.  Structural and Functional Brain Abnormalities in Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder: A Comparative Meta-analysis. , 2016, JAMA psychiatry.

[71]  I. Melle,et al.  Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders. , 2016, Journal of psychiatry & neuroscience : JPN.

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

[73]  Jack Bowden,et al.  Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data , 2016, Statistical methods in medical research.

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

[75]  Philip Shaw,et al.  Maps of the Development of the Brain's Functional Architecture: Could They Provide Growth Charts for Psychiatry? , 2016, JAMA psychiatry.

[76]  N. Krause,et al.  Forms of Attrition in a Longitudinal Study of Religion and Health in Older Adults and Implications for Sample Bias , 2016, Journal of religion and health.

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

[78]  Christine Ecker,et al.  Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan , 2015, The Lancet Neurology.

[79]  Chaogan Yan,et al.  Dorsal anterior cingulate cortex in typically developing children: Laterality analysis , 2015, Developmental Cognitive Neuroscience.

[80]  Anders M. Dale,et al.  Modeling the 3D Geometry of the Cortical Surface with Genetic Ancestry , 2015, Current Biology.

[81]  Christos Davatzikos,et al.  Imaging patterns of brain development and their relationship to cognition. , 2015, Cerebral cortex.

[82]  Torkel Klingberg,et al.  The role of fronto-parietal and fronto-striatal networks in the development of working memory: a longitudinal study. , 2015, Cerebral cortex.

[83]  C. Tesch-Römer,et al.  Changing predictors of self-rated health: Disentangling age and cohort effects. , 2015, Psychology and aging.

[84]  J. Lainhart.  Brain imaging research in autism spectrum disorders: in search of neuropathology and health across the lifespan , 2015, Current opinion in psychiatry.

[85]  Zhenyu Yang,et al.  Comparison of the China growth charts with the WHO growth standards in assessing malnutrition of children , 2015, BMJ Open.

[86]  Nicholas Lange,et al.  Longitudinal Volumetric Brain Changes in Autism Spectrum Disorder Ages 6–35 Years , 2015, Autism research : official journal of the International Society for Autism Research.

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

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

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

[90]  William D. Marslen-Wilson,et al.  The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing , 2014, BMC Neurology.

[91]  Gerard R. Ridgway,et al.  Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects , 2014, NeuroImage.

[92]  K. Mills,et al.  Methods and considerations for longitudinal structural brain imaging analysis across development , 2014, Developmental Cognitive Neuroscience.

[93]  John O. Willis,et al.  Wechsler Intelligence Scale for Children–Fourth Edition , 2014 .

[94]  Hua Shu,et al.  The Stability of Literacy-Related Cognitive Contributions to Chinese Character Naming and Reading Fluency , 2013, Journal of psycholinguistic research.

[95]  L. Ferrucci,et al.  The Effect of Birth Cohort on Well-Being , 2013, Psychological science.

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

[97]  T. Cole,et al.  The development of growth references and growth charts , 2012, Annals of human biology.

[98]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[99]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[100]  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.

[101]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[102]  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.

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

[104]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[105]  P. Cuijpers,et al.  The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods , 2008, International journal of methods in psychiatric research.

[106]  Sheng He,et al.  fMRI revealed neural substrate for reversible working memory dysfunction in subclinical hypothyroidism. , 2006, Brain : a journal of neurology.

[107]  Alan C. Evans,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[108]  I. Baron.  Test Review: Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) , 2005, Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence.

[109]  K Warner Schaie,et al.  The Seattle Longitudinal Study: Relationship Between Personality and Cognition , 2004, Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition.

[110]  I. Koch,et al.  The role of response selection for inhibition of task sets in task shifting. , 2003, Journal of experimental psychology. Human perception and performance.

[111]  Bruce D. McCandliss,et al.  Testing the Efficiency and Independence of Attentional Networks , 2002, Journal of Cognitive Neuroscience.

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

[113]  Golda S. Ginsburg,et al.  Factor structure of the childhood anxiety sensitivity index. , 1999, Behaviour research and therapy.

[114]  Marley W. Watkins,et al.  Long-term stability of the Wechsler Intelligence Scale for Children--Fourth Edition. , 1998, Psychological assessment.

[115]  J. Parker,et al.  The Multidimensional Anxiety Scale for Children (MASC): factor structure, reliability, and validity. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.

[116]  P. Lovibond,et al.  The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. , 1995, Behaviour research and therapy.

[117]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[118]  W. Stone,et al.  Development of the Social Anxiety Scale for Children: Reliability and Concurrent Validity , 1988 .

[119]  E. Torrance.  The Role of Creativity in Identification of the Gifted and Talented , 1984 .

[120]  Peter Renshaw,et al.  Loneliness in children. , 1984 .

[121]  T. Kamarck,et al.  A global measure of perceived stress. , 1983, Journal of health and social behavior.

[122]  C. Edelbrock,et al.  Manual for the Child: Behavior Checklist and Revised Child Behavior Profile , 1983 .

[123]  X. Zuo,et al.  Tracing Human Amygdala across School Age , 2021 .

[124]  N. Dosenbach,et al.  Developmental Cognitive Neuroscience in the Era of Networks and Big Data: Strengths, Weaknesses, Opportunities, and Threats , 2021, Annual Review of Developmental Psychology.

[125]  K. Kapp-Simon,et al.  The Americleft Psychosocial Outcomes Project: A Multicenter Approach to Advancing Psychosocial Outcomes for Youth With Cleft Lip and Palate. , 2017, Clinical practice in pediatric psychology.

[126]  J. Rapoport,et al.  Child Psychiatry Branch of the National Institute of Mental Health Longitudinal Structural Magnetic Resonance Imaging Study of Human Brain Development , 2015, Neuropsychopharmacology.

[127]  D. Marcus,et al.  Obscuring Surface Anatomy in Volumetric Imaging Data , 2012, Neuroinformatics.

[128]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[129]  Nian-Shing Chen,et al.  Multiple Representation Skills and Creativity Effects on Mathematical Problem Solving using a Multimedia Whiteboard System , 2007, J. Educ. Technol. Soc..

[130]  H I Nahoum,et al.  Growth patterns. , 1991, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[131]  C. Spielberger.  Manual for the State-Trait Anxiety Inventory (STAI) (Form Y , 1983 .

[132]  P. Birleson.  The validity of depressive disorder in childhood and the development of a self-rating scale: a research report. , 1981, Journal of child psychology and psychiatry, and allied disciplines.