Cognitive and default mode networks support developmental stability in functional connectome fingerprinting through adolescence
暂无分享,去创建一个
Finnegan J. Calabro | K. Roeder | L. Klei | B. Devlin | B. Luna | F. Calabro | William Foran | M. Jalbrzikowski | Fuchen Liu | Fuchen Lei | W. Foran
[1] Evan M. Gordon,et al. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry , 2019, Biological Psychiatry.
[2] Evan M. Gordon,et al. The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability , 2019, NeuroImage.
[3] Finnegan J. Calabro,et al. Development of Hippocampal-Prefrontal Cortex Interactions through Adolescence. , 2019, Cerebral cortex.
[4] Timothy O. Laumann,et al. Identifying reproducible individual differences in childhood functional brain networks: An ABCD study , 2019, Developmental Cognitive Neuroscience.
[5] Serdar Aslan,et al. Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter , 2019, NeuroImage.
[6] K. Roeder,et al. Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth. , 2019, Schizophrenia bulletin.
[7] D. Astle,et al. The cingulum as a marker of individual differences in neurocognitive development , 2019, Scientific Reports.
[8] B. Luna,et al. Age-Associated Deviations of Amygdala Functional Connectivity in Youths With Psychosis Spectrum Disorders: Relevance to Psychotic Symptoms. , 2019, The American journal of psychiatry.
[9] Dustin Scheinost,et al. The individual functional connectome is unique and stable over months to years , 2017, NeuroImage.
[10] M. Phillips,et al. Intrinsic functional connectivity correlates of person-level risk for bipolar disorder in offspring of affected parents , 2019, Neuropsychopharmacology.
[11] B. Luna,et al. Adolescence as a neurobiological critical period for the development of higher-order cognition , 2018, Neuroscience & Biobehavioral Reviews.
[12] Amanda V. Utevsky,et al. Developmental maturation of the precuneus as a functional core of the default-mode network , 2018, bioRxiv.
[13] Lars T. Westlye,et al. Stability of the Brain Functional Connectome Fingerprint in Individuals With Schizophrenia , 2018, JAMA psychiatry.
[14] Timothy O. Laumann,et al. Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.
[15] S. Galea,et al. Precision Medicine from a Public Health Perspective. , 2018, Annual review of public health.
[16] Dustin Scheinost,et al. Considering factors affecting the connectome-based identification process: Comment on Waller et al. , 2018, NeuroImage.
[17] Anders M. Dale,et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.
[18] Oscar Miranda-Dominguez,et al. Heritability of the human connectome: A connectotyping study , 2017, Network Neuroscience.
[19] Bart Larsen,et al. Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression , 2017, Biological Psychiatry.
[20] Johann Daniel Kruschwitz,et al. Evaluating the replicability, specificity, and generalizability of connectome fingerprints , 2017, NeuroImage.
[21] Evan M. Gordon,et al. Precision Functional Mapping of Individual Human Brains , 2017, Neuron.
[22] O. Andreassen,et al. Delayed stabilization and individualization in connectome development are related to psychiatric disorders , 2017, Nature Neuroscience.
[23] André Zugman,et al. Commentary: Functional connectome fingerprint: identifying individuals using patterns of brain connectivity , 2017, Front. Hum. Neurosci..
[24] Edward T. Bullmore,et al. Full Length Articles , 2022 .
[25] Beatriz Luna,et al. The role of experience in adolescent cognitive development: Integration of executive, memory, and mesolimbic systems , 2016, Neuroscience & Biobehavioral Reviews.
[26] Joseph Loscalzo,et al. Precision medicine in cardiology , 2016, Nature Reviews Cardiology.
[27] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[28] Xi-Nian Zuo,et al. Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability , 2016, bioRxiv.
[29] Timothy O. Laumann,et al. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.
[30] Evan M. Gordon,et al. Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.
[31] K. Hwang,et al. The Contribution of Network Organization and Integration to the Development of Cognitive Control , 2015, PLoS biology.
[32] M. Chun,et al. Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.
[33] Evan M. Gordon,et al. Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.
[34] Bart Larsen,et al. An integrative model of the maturation of cognitive control. , 2015, Annual review of neuroscience.
[35] Alberto Llera,et al. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.
[36] Maarten Mennes,et al. Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI , 2015, NeuroImage.
[37] R. Kümmerli,et al. Quorum sensing triggers the stochastic escape of individual cells from Pseudomonas putida biofilms , 2015, Nature Communications.
[38] J. Dukart,et al. When Structure Affects Function – The Need for Partial Volume Effect Correction in Functional and Resting State Magnetic Resonance Imaging Studies , 2014, PloS one.
[39] Damien A. Fair,et al. Connectotyping: Model Based Fingerprinting of the Functional Connectome , 2014, PloS one.
[40] Eric A Youngstrom,et al. A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: we are ready to ROC. , 2014, Journal of pediatric psychology.
[41] Beatriz Luna,et al. The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity , 2013, NeuroImage.
[42] Xenophon Papademetris,et al. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.
[43] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[44] K. Hwang,et al. The development of hub architecture in the human functional brain network. , 2013, Cerebral cortex.
[45] Vivek Prabhakaran,et al. The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated , 2013, NeuroImage.
[46] M. Fox,et al. Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.
[47] 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.
[48] S. Plein,et al. Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial , 2012, The Lancet.
[49] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[50] Mert R. Sabuncu,et al. The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.
[51] Timothy O. Laumann,et al. Functional Network Organization of the Human Brain , 2011, Neuron.
[52] Xavier Robin,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.
[53] C. Almli,et al. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood , 2009, NeuroImage.
[54] T. Paus,et al. Why do many psychiatric disorders emerge during adolescence? , 2008, Nature Reviews Neuroscience.
[55] C. Florkowski. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. , 2008, The Clinical biochemist. Reviews.
[56] L. Steinberg. Cognitive and affective development in adolescence , 2005, Trends in Cognitive Sciences.
[57] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[58] A. Glaros,et al. Understanding the accuracy of tests with cutting scores: the sensitivity, specificity, and predictive value model. , 1988, Journal of clinical psychology.
[59] E. DeLong,et al. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.