Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

[1]  Valerie J. Sydnor,et al.  Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology , 2021, Neuron.

[2]  D. Watson,et al.  The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence. , 2021, Annual review of clinical psychology.

[3]  D. Zald,et al.  Hierarchical models of psychopathology: empirical support, implications, and remaining issues , 2021, World psychiatry : official journal of the World Psychiatric Association.

[4]  R. N. Spreng,et al.  Heritability of individualized cortical network topography , 2020, Proceedings of the National Academy of Sciences.

[5]  Marisa N. Spann,et al.  Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders , 2020, Cerebral cortex.

[6]  Hans J. Johnson,et al.  Advanced Normalization Tools (ANTs) , 2020 .

[7]  D. Barch,et al.  Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood. , 2020, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[8]  E. Feczko,et al.  Parsing Psychiatric Heterogeneity Through Common and Unique Circuit-Level Deficits , 2020, Biological Psychiatry.

[9]  V. Calhoun,et al.  The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study , 2020, Translational Psychiatry.

[10]  Gregory T. Smith,et al.  The General Factor of Psychopathology. , 2020, Annual review of clinical psychology.

[11]  Evan M. Gordon,et al.  Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry , 2020, Proceedings of the National Academy of Sciences.

[12]  Conor Liston,et al.  Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes , 2020, Biological Psychiatry.

[13]  Russell T. Shinohara,et al.  Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth , 2019, Biological Psychiatry.

[14]  Evan M. Gordon,et al.  Integrative and Network-Specific Connectivity of the Basal Ganglia and Thalamus Defined in Individuals , 2019, Neuron.

[15]  Evan M. Gordon,et al.  Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry , 2019, Biological Psychiatry.

[16]  Annchen R. Knodt,et al.  A Pervasively Thinner Neocortex is a Transdiagnostic Feature of General Psychopathology , 2019, bioRxiv.

[17]  A. Mechelli,et al.  Transdiagnostic Dysfunctions in Brain Modules Across Patients with Schizophrenia, Bipolar Disorder, and Major Depressive Disorder: A Connectome-Based Study. , 2019, Schizophrenia bulletin.

[18]  Matthew G. Keefe,et al.  Development and Arealization of the Cerebral Cortex , 2019, Neuron.

[19]  Adon F. G. Rosen,et al.  Development of a computerized adaptive screening tool for overall psychopathology ("p"). , 2019, Journal of psychiatric research.

[20]  Xi-Nian Zuo,et al.  Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. , 2019, Cerebral cortex.

[21]  R. Poldrack,et al.  Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology , 2019, Biological Psychiatry.

[22]  Georg Langs,et al.  Performing group-level functional image analyses based on homologous functional regions mapped in individuals , 2019, PLoS biology.

[23]  A. Mechelli,et al.  Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders , 2019, Biological Psychiatry.

[24]  Joseph C. Griffis,et al.  Structural disconnections explain brain network dysfunction after stroke , 2019, bioRxiv.

[25]  Yuan Shen,et al.  Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning , 2019, Nature Human Behaviour.

[26]  Justin T. Baker,et al.  Functional connectomics of affective and psychotic pathology , 2018, Proceedings of the National Academy of Sciences.

[27]  B. Luna,et al.  Adolescence as a neurobiological critical period for the development of higher-order cognition , 2018, Neuroscience & Biobehavioral Reviews.

[28]  Meiling Li,et al.  Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness , 2018, Molecular Psychiatry.

[29]  Johannes Zimmermann,et al.  Progress in achieving quantitative classification of psychopathology , 2018, World psychiatry : official journal of the World Psychiatric Association.

[30]  G. Gong,et al.  The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features , 2018, NeuroImage.

[31]  Haochang Shou,et al.  On testing for spatial correspondence between maps of human brain structure and function , 2018, NeuroImage.

[32]  Essa Yacoub,et al.  The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds , 2018, NeuroImage.

[33]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[34]  Avshalom Caspi,et al.  All for One and One for All: Mental Disorders in One Dimension. , 2018, The American journal of psychiatry.

[35]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[36]  Dustin Scheinost,et al.  Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets , 2018, NeuroImage.

[37]  Stephen M Smith,et al.  The relationship between spatial configuration and functional connectivity of brain regions , 2017, bioRxiv.

[38]  Richard F. Betzel,et al.  Linked dimensions of psychopathology and connectivity in functional brain networks , 2017, bioRxiv.

[39]  Evan M. Gordon,et al.  Precision Functional Mapping of Individual Human Brains , 2017, Neuron.

[40]  Yong Fan,et al.  Large-scale sparse functional networks from resting state fMRI , 2017, NeuroImage.

[41]  Rodrigo M. Braga,et al.  Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity , 2017, Neuron.

[42]  Russell T. Shinohara,et al.  Common and Dissociable Regional Cerebral Blood Flow Differences Associate with Dimensions of Psychopathology Across Categorical Diagnoses , 2017, Molecular Psychiatry.

[43]  Dov B. Lerman-Sinkoff,et al.  Transdiagnostic Associations Between Functional Brain Network Integrity and Cognition , 2017, JAMA psychiatry.

[44]  David Watson,et al.  The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies , 2017, Journal of abnormal psychology.

[45]  R. Gur,et al.  Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion , 2017, Proceedings of the National Academy of Sciences.

[46]  O. Andreassen,et al.  Delayed stabilization and individualization in connectome development are related to psychiatric disorders , 2017, Nature Neuroscience.

[47]  Evan M. Gordon,et al.  Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.

[48]  R. Krueger,et al.  A Hierarchical Causal Taxonomy of Psychopathology Across the Life Span , 2017, Psychological bulletin.

[49]  B. Turetsky,et al.  Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two‐year follow‐up , 2017, World psychiatry : official journal of the World Psychiatric Association.

[50]  Evan M. Gordon,et al.  Individual Variability of the System‐Level Organization of the Human Brain , 2015, Cerebral cortex.

[51]  Elizabeth Jefferies,et al.  Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.

[52]  Efstathios D. Gennatas,et al.  Common and Dissociable Mechanisms of Executive System Dysfunction Across Psychiatric Disorders in Youth. , 2016, The American journal of psychiatry.

[53]  Timothy O. Laumann,et al.  Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.

[54]  Kosha Ruparel,et al.  The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. , 2015, Journal of child psychology and psychiatry, and allied disciplines.

[55]  R. Buckner,et al.  Parcellating Cortical Functional Networks in Individuals , 2015, Nature Neuroscience.

[56]  Evan M. Gordon,et al.  Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.

[57]  J. Haynes.  A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

[58]  Kosha Ruparel,et al.  Functional neuroimaging abnormalities in youth with psychosis spectrum symptoms. , 2015, JAMA psychiatry.

[59]  R. Gur,et al.  Topologically Dissociable Patterns of Development of the Human Cerebral Cortex , 2015, The Journal of Neuroscience.

[60]  Timothy O. Laumann,et al.  An approach for parcellating human cortical areas using resting-state correlations , 2014, NeuroImage.

[61]  T. Insel.  The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. , 2014, The American journal of psychiatry.

[62]  Christos Davatzikos,et al.  Neuroimaging of the Philadelphia Neurodevelopmental Cohort , 2014, NeuroImage.

[63]  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.

[64]  Abraham Z. Snyder,et al.  Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.

[65]  Christos Davatzikos,et al.  Functional Maturation of the Executive System during Adolescence , 2013, The Journal of Neuroscience.

[66]  M. Fox,et al.  Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.

[67]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[68]  Bengt Muthén,et al.  Bayesian structural equation modeling: a more flexible representation of substantive theory. , 2012, Psychological methods.

[69]  A. Hariri,et al.  Is there a general factor of prevalent psychopathology during adulthood? , 2012, Journal of abnormal psychology.

[70]  Xiaojun Wu,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[71]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[72]  V. Menon.  Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

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

[74]  Anthony Randal McIntosh,et al.  Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.

[75]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[76]  T. Insel,et al.  Wesleyan University From the SelectedWorks of Charles A . Sanislow , Ph . D . 2010 Research Domain Criteria ( RDoC ) : Toward a New Classification Framework for Research on Mental Disorders , 2018 .

[77]  Shen-Ju Chou,et al.  Area Patterning of the Mammalian Cortex , 2007, Neuron.

[78]  J. Rubenstein,et al.  Patterning of frontal cortex subdivisions by Fgf17 , 2007, Proceedings of the National Academy of Sciences.

[79]  H. Abdi.  Partial Least Square Regression PLS-Regression , 2007 .

[80]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[81]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[82]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[83]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[84]  M. Raichle,et al.  Anatomic Localization and Quantitative Analysis of Gradient Refocused Echo-Planar fMRI Susceptibility Artifacts , 1997, NeuroImage.

[85]  N. Ryan,et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.