Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies

[1]  G. Salmon,et al.  Attention deficit hyperactivity disorder. , 2018, British journal of hospital medicine.

[2]  B. Franke,et al.  From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics , 2015, Neuroscience & Biobehavioral Reviews.

[3]  Khader M. Hasan,et al.  Development and validation of a brain maturation index using longitudinal neuroanatomical scans , 2015, NeuroImage.

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

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

[6]  T. Insel,et al.  Brain disorders? Precisely , 2015, Science.

[7]  Damien A. Fair,et al.  Characterizing heterogeneity in children with and without ADHD based on reward system connectivity , 2015, Developmental Cognitive Neuroscience.

[8]  Maurizio Filippone,et al.  Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) , 2015, ICML.

[9]  Swathi P. Iyer,et al.  Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. , 2014, JAMA psychiatry.

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

[11]  S. Durston,et al.  Distinct neuropsychological profiles within ADHD: a latent class analysis of cognitive control, reward sensitivity and timing , 2014, Psychological Medicine.

[12]  Andre F. Marquand,et al.  Full Bayesian multi-task learning for multi-output brain decoding and accommodating missing data , 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

[13]  Michael J. Brammer,et al.  Bayesian multi-task learning for decoding multi-subject neuroimaging data , 2014, NeuroImage.

[14]  Kosha Ruparel,et al.  Neurocognitive growth charting in psychosis spectrum youths. , 2014, JAMA psychiatry.

[15]  Martin Styner,et al.  SGPP: spatial Gaussian predictive process models for neuroimaging data , 2014, NeuroImage.

[16]  Warren K. Bickel,et al.  Remember the Future II: Meta-analyses and Functional Overlap of Working Memory and Delay Discounting , 2014, Biological Psychiatry.

[17]  T. Goldberg,et al.  RDoCs redux , 2014, World psychiatry : official journal of the World Psychiatric Association.

[18]  Bruce N Cuthbert,et al.  The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology , 2014, World psychiatry : official journal of the World Psychiatric Association.

[19]  A. Scheres,et al.  Ventral–striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature , 2014, Neuroscience & Biobehavioral Reviews.

[20]  P. Rossini,et al.  Stratified medicine for mental disorders , 2014, European Neuropsychopharmacology.

[21]  J. Buhmann,et al.  Dissecting psychiatric spectrum disorders by generative embedding☆☆☆ , 2013, NeuroImage: Clinical.

[22]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[23]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[24]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[25]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[26]  T. Insel,et al.  Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? , 2012, Molecular Psychiatry.

[27]  I. Rezek,et al.  Models of Disease Spectra , 2012, 1207.4674.

[28]  Deepti R. Bathula,et al.  Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD , 2012, Proceedings of the National Academy of Sciences.

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

[30]  Janaina Mourão Miranda,et al.  Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine , 2011, NeuroImage.

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

[32]  A. Beekman,et al.  Identifying depressive subtypes in a large cohort study: results from the Netherlands Study of Depression and Anxiety (NESDA). , 2010, The Journal of clinical psychiatry.

[33]  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 .

[34]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[35]  Janaina Mourão Miranda,et al.  Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes , 2010, NeuroImage.

[36]  K. Kirby.  One-year temporal stability of delay-discount rates , 2009, Psychonomic bulletin & review.

[37]  S. Pliszka,et al.  Attention‐Deficit‐Hyperactivity Disorder: An Update , 2009, Pharmacotherapy.

[38]  J. Wickens,et al.  Research review: dopamine transfer deficit: a neurobiological theory of altered reinforcement mechanisms in ADHD. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[39]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[40]  B. Reynolds.  A review of delay-discounting research with humans: relations to drug use and gambling , 2006, Behavioural pharmacology.

[41]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[42]  Joseph V. Hajnal,et al.  A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T) , 2010, NeuroImage.

[43]  E. Willcutt,et al.  Instability of the DSM-IV Subtypes of ADHD from preschool through elementary school. , 2005, Archives of general psychiatry.

[44]  Nicole A. Lazar,et al.  Statistics of Extremes: Theory and Applications , 2005, Technometrics.

[45]  M. Tribus,et al.  Probability theory: the logic of science , 2003 .

[46]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[47]  L. Green,et al.  Area under the curve as a measure of discounting. , 2001, Journal of the experimental analysis of behavior.

[48]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[49]  L. Nystrom,et al.  Tracking the hemodynamic responses to reward and punishment in the striatum. , 2000, Journal of neurophysiology.

[50]  G. Ainslie.  Specious reward: a behavioral theory of impulsiveness and impulse control. , 1975, Psychological bulletin.

[51]  Tanja Hueber,et al.  Gaussian Processes For Machine Learning , 2016 .

[52]  J. Romeijn,et al.  Data-driven subtypes of major depressive disorder , 2013 .

[53]  B. Wexler,et al.  Social cognitive impairments and negative symptoms in schizophrenia: are there subtypes with distinct functional correlates? , 2013, Schizophrenia bulletin.